datos geoespaciales

Cities, infrastructures and the environment today generate a constant flow of data from sensors, transport networks, weather stations and Internet of Things (IoT) platforms, understood as networks of physical devices (digital traffic lights, air quality sensors, etc.) capable of measuring and transmitting information through digital systems. This growing volume of information makes it possible to improve the provision of public services, anticipate emergencies, plan the territory and respond to challenges associated with climate, mobility or resource management.

The increase in connected sources has transformed the nature of geospatial data. In contrast to traditional sets – updated periodically and oriented towards reference cartography or administrative inventories – dynamic data incorporate the temporal dimension as a structural component. An observation of air quality, a level of traffic occupancy or a hydrological measurement not only describes a phenomenon, but also places it at a specific time. The combination of space and time makes these observations fundamental elements for operating systems, predictive models and analyses based on time series.

In the field of open data, this type of information poses both opportunities and specific requirements. Opportunities include the possibility of building reusable digital services, facilitating near-real-time monitoring of urban and environmental phenomena, and fostering a reuse ecosystem based on continuous flows of interoperable data. The availability of up-to-date data also increases the capacity for evaluation and auditing of public policies, by allowing decisions to be contrasted with recent observations.

However, the opening of geospatial data in real time requires solving problems derived from technological heterogeneity. Sensor networks use different protocols, data models, and formats; the sources generate high volumes of observations with high frequency; and the absence of common semantic structures makes it difficult to cross-reference data between domains such as mobility, environment, energy or hydrology. In order for this data to be published and reused consistently, an interoperability framework is needed that standardizes the description of observed phenomena, the structure of time series, and access interfaces.

The open standards of the Open Geospatial Consortium (OGC) provide that framework. They define how to represent observations, dynamic entities, multitemporal coverages or sensor systems; establish APIs based on web principles that facilitate the consultation of open data; and allow different platforms to exchange information without the need for specific integrations. Its adoption reduces technological fragmentation, improves coherence between sources and favours the creation of public services based on up-to-date data.

Interoperability: The basic requirement for opening dynamic data

Public administrations today manage data generated by sensors of different types, heterogeneous platforms, different suppliers and systems that evolve independently. The publication of geospatial data in real time requires interoperability that allows information from multiple sources to be integrated, processed and reused. This diversity causes inconsistencies in formats, structures, vocabularies and protocols, which makes it difficult to open the data and reuse it by third parties. Let's see which aspects of interoperability are affected:

• Technical interoperability: refers to the ability of systems to exchange data using compatible interfaces, formats and models. In real-time data, this exchange requires mechanisms that allow for fast queries, frequent updates, and stable data structures. Without these elements, each flow would rely on ad hoc integrations, increasing complexity and reducing reusability.
• The Semantic interoperability: Dynamic data describe phenomena that change over short periods – traffic levels, weather parameters, flows, atmospheric emissions – and must be interpreted consistently. This implies having observation models, Vocabularies and common definitions that allow different applications to understand the meaning of each measurement and its units, capture conditions or constraints. Without this semantic layer, the opening of data in real time generates ambiguity and limits its integration with data from other domains.
• Structural interoperability: Real-time data streams tend to be continuous and voluminous, making it necessary to represent them as time series or sets of observations with consistent attributes. The absence of standardized structures complicates the publication of complete data, fragments information and prevents efficient queries. To provide open access to these data, it is necessary to adopt models that adequately represent the relationship between observed phenomenon, time of observation, associated geometry and measurement conditions.
• Interoperability in access via API: it is an essential condition for open data. APIs must be stable, documented, and based on public specifications that allow for reproducible queries. In the case of dynamic data, this layer guarantees that the flows can be consumed by external applications, analysis platforms, mapping tools or monitoring systems that operate in contexts other than the one that generates the data. Without interoperable APIs, real-time data is limited to internal uses.

Together, these levels of interoperability determine whether dynamic geospatial data can be published as open data without creating technical barriers.

OGC Standards for Publishing Real-Time Geospatial Data

The publication of georeferenced data in real time requires mechanisms that allow any user – administration, company, citizens or research community – to access them easily, with open formats and through stable interfaces. The Open Geospatial Consortium (OGC) develops a set of standards that enable exactly this: to describe, organize and expose spatial data in an interoperable and accessible way, which contributes to the openness of dynamic data.

What is OGC and why are its standards relevant?

The OGC is an international organization that defines common rules so that different systems can understand, exchange and use geospatial data without depending on specific technologies. These rules are published as open standards, which means that any person or institution can use them. In the realm of real-time data, these standards make it possible to:

• Represent what a sensor measures (e.g., temperature or traffic).
• Indicate where and when the observation was made.
• Structure time series.
• Expose data through open APIs.
• Connect IoT devices and networks with public platforms.

Together, this ecosystem of standards allows geospatial data – including data generated in real time – to be published and reused following a consistent framework. Each standard covers a specific part of the data cycle: from the definition of observations and sensors, to the way data is exposed using open APIs or web services. This modular organization makes it easier for administrations and organizations to select the components they need, avoiding technological dependencies and ensuring that data can be integrated between different platforms.

The OGC API family: Modern APIs for accessing open data

Within OGC, the newest line is family OGC API, a set of modern web interfaces designed to facilitate access to geospatial data using URLs and formats such as JSON or GeoJSON, common in the open data ecosystem.

Estas API permiten:

• Get only the part of the data that matters.
• Perform spatial searches ("give me only what's in this area").
• Access up-to-date data without the need for specialized software.
• Easily integrate them into web or mobile applications.

In this report: "How to use OGC APIs to boost geospatial data interoperability", we already told you about some of the most popular OGP APIs. While the report focuses on how to use OGC APIs for practical interoperability, this post expands the focus by explaining the underlying OGC data models—such as O&M, SensorML, or Moving Features—that underpin that interoperability.

On this basis, this post focuses on the standards that make this fluid exchange of information possible, especially in open data and real-time contexts. The most important standards in the context of real-time open data are:

Table 1. OGC Standards Relevant to Real-Time Geospatial Data

Models that structure observations and dynamic data

In addition to APIs, OGC defines several conceptual data models that allow you to consistently describe observations, sensors, and phenomena that change over time:

• O&M (Observations & Measurements): A model that defines the essential elements of an observation—measured phenomenon, instant, unity, and result—and serves as the semantic basis for sensor and time series data.
• SensorML: Language that describes the technical and operational characteristics of a sensor, including its location, calibration, and observation process.
• Moving Features: A model that allows mobile objects to be represented by means of space-time trajectories (such as vehicles, boats or fauna).

These models make it easy for different data sources to be interpreted uniformly and combined in analytics and applications.

The value of these standards for open data

Using OGC standards makes it easier to open dynamic data because:

• It provides common models that reduce heterogeneity between sources.
• It facilitates integration between domains (mobility, climate, hydrology).
• Avoid dependencies on proprietary technology.
• It allows the data to be reused in analytics, applications, or public services.
• Improves transparency by documenting sensors, methods, and frequencies.
• It ensures that data can be consumed directly by common tools.

Together, they form a conceptual and technical infrastructure that allows real-time geospatial data to be published as open data, without the need to develop system-specific solutions.

Real-time open geospatial data use cases

Real-time georeferenced data is already published as open data in different sectoral areas. These examples show how different administrations and bodies apply open standards and APIs to make dynamic data related to mobility, environment, hydrology and meteorology available to the public.

Below are several domains where Public Administrations already publish dynamic geospatial data using OGC standards.

Mobility and transport

Mobility systems generate data continuously: availability of shared vehicles, positions in near real-time, sensors for crossing in cycle lanes, traffic gauging or traffic light intersection status. These observations rely on distributed sensors and require data models capable of representing rapid variations in space and time.

OGC standards play a central role in this area. In particular, the OGC SensorThings API allows you to structure and publish observations from urban sensors using a uniform model – including devices, measurements, time series and relationships between them – accessible through an open API. This makes it easier for different operators and municipalities to publish mobility data in an interoperable way, reducing fragmentation between platforms.

The use of OGC standards in mobility not only guarantees technical compatibility, but also makes it possible for this data to be reused together with environmental, cartographic or climate information, generating multi-thematic analyses for urban planning, sustainability or operational transport management.

Example:

The open service of Toronto Bike Share, which publishes in SensorThings API format the status of its bike stations and vehicle availability.

Here each station is a sensor and each observation indicates the number of bicycles available at a specific time. This approach allows analysts, developers or researchers to integrate this data directly into urban mobility models, demand prediction systems or citizen dashboards without the need for specific adaptations.

Air quality, noise and urban sensors

Networks for monitoring air quality, noise or urban environmental conditions depend on automatic sensors that record measurements every few minutes. In order for this data to be integrated into analytics systems and published as open data, consistent models and APIs need to be available.

In this context, services based on OGC standards make it possible to publish data from fixed stations or distributed sensors in an interoperable way. Although many administrations use traditional interfaces such as OGC WMS to serve this data, the underlying structure is usually supported by observation models derived from the Observations & Measurements (O&M) family, which defines how to represent a measured phenomenon, its unit and the moment of observation.

Example:

The service Defra UK-AIR Sensor Observation Service provides access to near-real-time air quality measurement data from on-site stations in the UK.

The combination of O&M for data structure and open APIs for publication makes it easier for these urban sensors to be part of broader ecosystems that integrate mobility, meteorology or energy, enabling advanced urban analyses or environmental dashboards in near real-time.

Water cycle, hydrology and risk management

Hydrological systems generate crucial data for risk management: river levels and flows, rainfall, soil moisture or information from hydrometeorological stations. Interoperability is especially important in this domain, as this data is combined with hydraulic models, weather forecasting, and flood zone mapping.

To facilitate open access to time series and hydrological observations, several agencies use OGC API – Environmental Data Retrieval (EDR), an API designed to retrieve environmental data using simple queries at points, areas, or time intervals.

Example:

The USGS (United States Geological Survey), which documents the use of OGC API – EDR to access precipitation, temperature, or hydrological variable series.

This case shows how EDR allows you to request specific observations by location or date, returning only the values needed for analysis. While the USGS's specific hydrology data is served through its proprietary API, this case demonstrates how EDR fits into the hydrometeorological data structure and how it is applied in real operational flows.

The use of OGC standards in this area allows dynamic hydrological data to be integrated with flood zones, orthoimages or climate models, creating a solid basis for early warning systems, hydraulic planning and risk assessment.

Weather observation and forecasting

Meteorology is one of the domains with the highest production of dynamic data: automatic stations, radars, numerical prediction models, satellite observations and high-frequency atmospheric products. To publish this information as open data, the OGC API family  is becoming a key element, especially through OGC API – EDR, which allows observations or predictions to be retrieved in specific locations and at different time levels.

Example: 

The service NOAA OGC API – EDR, which provides access to weather data and atmospheric variables from the National Weather Service (United States).

This API allows data to be consulted at points, areas or trajectories, facilitating the integration of meteorological observations into external applications, models or services based on open data.

The use of OGC API in meteorology allows data from sensors, models, and satellites to be consumed through a unified interface, making it easy to reuse for forecasting, atmospheric analysis, decision support systems, and climate applications.

Best Practices for Publishing Open Geospatial Data in Real-Time 

The publication of dynamic geospatial data requires adopting practices that ensure its accessibility, interoperability, and sustainability. Unlike static data, real-time streams have additional requirements related to the quality of observations, API stability, and documentation of the update process. Here are some best practices for governments and organizations that manage this type of data.

• Stable open formats and APIs: The use of OGC standards – such as OGC API, SensorThings API or EDR – makes it easy for data to be consumed from multiple tools without the need for specific adaptations. APIs must be stable over time, offer well-defined versions, and avoid dependencies on proprietary technologies. For raster data or dynamic models, OGC services such as WMS, WMTS, or WCS are still suitable for visualization and programmatic access.
• DCAT-AP and OGC Models Compliant Metadata: Catalog interoperability requires describing datasets using profiles such as DCAT-AP, supplemented by O&M-based geospatial and observational metadata or SensorML. This metadata should document the nature of the sensor, the unit of measurement, the sampling rate, and possible limitations of the data.
• Quality, update frequency and traceability policies: dynamic datasets must explicitly indicate their update frequency, the origin of the observations, the validation mechanisms applied and the conditions under which they were generated. Traceability is essential for third parties to correctly interpret data, reproduce analyses and integrate observations from different sources.
• Documentation, usage limits, and service sustainability: Documentation should include usage examples, query parameters, response structure, and recommendations for managing data volume. It is important to set reasonable query limits to ensure the stability of the service and ensure that management can maintain the API over the long term.
• Licensing aspects for dynamic data: The license must be explicit and compatible with reuse, such as CC BY 4.0 or CC0. This allows dynamic data to be integrated into third-party services, mobile applications, predictive models or services of public interest without unnecessary restrictions. Consistency in the license also facilitates the cross-referencing of data from different sources.

These practices allow dynamic data to be published in a way that is reliable, accessible, and useful to the entire reuse community.

Dynamic geospatial data has become a structural piece for understanding urban, environmental and climatic phenomena. Its publication through open standards allows this information to be integrated into public services, technical analyses and reusable applications without the need for additional development. The convergence of observation models, OGC APIs, and best practices in metadata and licensing provides a stable framework for administrations and reusers to work with sensor data reliably. Consolidating this approach will allow progress towards a more coherent, connected public data ecosystem that is prepared for increasingly demanding uses in mobility, energy, risk management and territorial planning.

Content created by Mayte Toscano, Senior Consultant in Technologies related to the data economy. The content and views expressed in this publication are the sole responsibility of the author.

From today, September 15, registration is open for one of the most important events in the geospatial sector in the Iberian Peninsula. The XVI Iberian Conference on Spatial Data Infrastructures (JIIDE 2025) will be held in Oviedo from 12 to 14 November 2025. This annual meeting represents a unique opportunity to explore the latest trends in spatial data reuse, especially in the context of the application of artificial intelligence to territorial knowledge.

Since its first edition in 2011, the JIIDEs have evolved as a result of collaboration between the Direção-Geral do Território de Portugal, the National Geographic Institute of Spain through the National Center for Geographic Information, and the Government of Andorra. In this sixteenth edition, the Ministry of Territorial Planning, Urban Planning, Housing and Citizens' Rights of  the Principality of Asturias and the University of Oviedo also join, thus consolidating an initiative that brings together hundreds of professionals from the Public Administration, the private sector and the academic field every year.

For three days, experts with proven experience and technical knowledge in geographic information will share their most innovative developments, work methodologies and success stories in the management and reuse of spatial data.

Two axes: artificial intelligence and the INSPIRE and HVDS regulatory framework

The central theme of this edition, "AI and territory: exploring the new frontiers of spatial knowledge", reflects the natural evolution of the sector towards the incorporation of emerging technologies. Artificial intelligence, machine learning, and advanced analytics algorithms are radically transforming the way we process, analyze, and extract value from geospatial data.

This orientation towards AI is not accidental. The publication and use of geospatial data makes it possible to harness one of the most valuable digital assets for economic development, environmental monitoring, competitiveness, innovation and job creation. When this data is combined with artificial intelligence techniques, its potential multiplies exponentially.

The conference takes place at a particularly relevant time for the open data ecosystem. The INSPIRE Directive, together with Directive (EU) 2019/1024 on open data and re-use of public sector information, has established a regulatory framework that explicitly recognises the economic and social value of digital geospatial data.

The evolution in the publication of high-value datasets marks an important milestone in this process. These sets, characterized by their great potential for reuse, should be available free of charge, in machine-readable formats and through application programming interfaces (APIs). Geospatial data occupies a central position in this categorisation, underlining its strategic importance for the European open data ecosystem.

JIIDE 2025 will devote particular attention to presenting practical examples of re-use of these high-value datasets , both through the new OGC APIs and through traditional download services and established interoperable formats. This practical approach will allow attendees to learn about real cases of implementation and their tangible results.

Miscellaneous Program: Use Cases, AI, and Geospatial Data Reuse

You can also check the program here. Among the planned activities, there are sessions ranging from fundamental technical aspects to innovative applications that demonstrate the transformative potential of this data. The activities are organized into five main themes:

1. Spatial data structure and metadata.

2. Data management and publication.

3. Development of spatial  software.

4. Artificial intelligence.

5. Cooperation between agents.

Some of the highlighted topics are project management and coordination, where corporate systems such as the SIG of the Junta de Andalucía or the SITNA of the Government of Navarra will be presented. Earth observation will also feature prominently, with presentations on the evolution of the National Plan for Aerial Orthophotography (APNOA) programme  and advanced deep learning image processing techniques.

On the other hand, thematic visualisers also represent another fundamental axis, showing how spatial data can be transformed into accessible tools for citizens. From eclipse visualizers to tools for calculating the solar potential of rooftops, developments will be presented that demonstrate how the creative reuse of data can generate services of high social value.

Following the annual theme, the application of AI to geospatial data will be approached from multiple perspectives. Use cases will be presented in areas as diverse as the automatic detection of sports facilities, the classification of LiDAR point clouds, the identification of hazardous materials such as asbestos, or the optimization of urban mobility.

One of the most relevant sessions for the open data community will focus specifically on "Reuse and Open Government". This session will address the integration of spatial data infrastructures into open data portals, spatial data metadata according to the GeoDCAT-AP standard, and the application of data quality regulations.

Local governments play a key role in the generation and publication of spatial data. For this reason, the JIIDE 2025 will dedicate a specific session to the publication of local data, where municipalities such as Barcelona, Madrid, Bilbao or Cáceres will share their experiences and developments.

In addition to the theoretical sessions, the conferences include practical workshops on specific tools, methodologies and technologies. These workshops, lasting 45 minutes to an hour, allow attendees to experiment directly with the solutions presented. Some of them address the creation of custom web geoportals and others, for example, the implementation of OGC APIs, through advanced visualization techniques and metadata management tools.

Participate in person or online

The JIIDEs maintain their commitment to open participation, inviting both researchers and professionals to present their tools, technical solutions, work methodologies and success stories. In addition, the JIIDE 2025 will be held in hybrid mode, allowing both face-to-face participation in Oviedo and virtual monitoring.

This flexibility, maintained from the experiences of recent years, ensures that professionals throughout the Iberian territory and beyond can benefit from shared knowledge. Participation remains free, although prior registration is required for each session, roundtable or workshop.

Starting today, you can sign up and take advantage of this opportunity to learn and exchange experiences on geospatial data. Registration is available on the official website of the event: https://www.jiide.org/web/portal/inicio

Synthetic images are visual representations artificially generated by algorithms and computational techniques, rather than being captured directly from reality with cameras or sensors. They are produced from different methods, among which the antagonistic generative networks (Generative Adversarial NetworksGAN), the Dissemination models, and the 3D rendering techniques. All of them allow you to create images of realistic appearance that in many cases are indistinguishable from an authentic photograph.

When this concept is transferred to the field of Earth observation, we are talking about synthetic satellite images. These are not obtained from a space sensor that captures real electromagnetic radiation, but are generated digitally to simulate what a satellite would see from orbit. In other words, instead of directly reflecting the physical state of the terrain or atmosphere at a particular time, they are computational constructs capable of mimicking the appearance of a real satellite image.

The development of this type of image responds to practical needs. Artificial intelligence systems that process remote sensing data require very large and varied sets of images. Synthetic images allow, for example, to recreate areas of the Earth that are little observed, to simulate natural disasters – such as forest fires, floods or droughts – or to generate specific conditions that are difficult or expensive to capture in practice. In this way, they constitute a valuable resource for training detection and prediction algorithms in agriculture, emergency management, urban planning or environmental monitoring.

Its value is not limited to model training. Where high-resolution images do not exist – due to technical limitations, access restrictions or economic reasons – synthesis makes it possible to fill information gaps and facilitate preliminary studies. For example, researchers can work with approximate synthetic images to design risk models or simulations before actual data are available.

However, synthetic satellite imagery also poses significant risks. The possibility of generating very realistic scenes opens the door to manipulation and misinformation. In a geopolitical context, an image showing non-existent troops or destroyed infrastructure could influence strategic decisions or international public opinion. In the environmental field, manipulated images could be disseminated to exaggerate or minimize the impacts of phenomena such as deforestation or melting ice, with direct effects on policies and markets.

Therefore, it is convenient to differentiate between two very different uses. The first is use as a support, when synthetic images complement real images to train models or perform simulations. The second is use as a fake, when they are deliberately presented as authentic images in order to deceive. While the former uses drive innovation, the latter threatens trust in satellite data and poses an urgent challenge of authenticity and governance.

Risks of satellite imagery applied to Earth observation

Synthetic satellite imagery poses significant risks when used in place of images captured by real sensors. Below are examples that demonstrate this.

A new front of disinformation: "deepfake geography"

The term deepfake geography has already been consolidated in the academic and popular literature to describe fictitious satellite images, manipulated with AI, that appear authentic, but do not reflect any existing reality. Research from the University of Washington, led by Bo Zhao, used algorithms such as CycleGAN to modify images of real cities—for example, altering the appearance of Seattle with non-existent buildings or transforming Beijing into green areas—highlighting the potential to generate convincing false landscapes.

One OnGeo Intelligence (OGC) platform article stresses that these images are not purely theoretical, but real threats affecting national security, journalism and humanitarian work. For its part, the OGC warns that fabricated satellite imagery, AI-generated urban models, and synthetic road networks have already been observed, and that they pose real challenges to public and operational trust.

Strategic and policy implications

Satellite images are considered "impartial eyes" on the planet, used by governments, media and organizations. When these images are faked, their consequences can be severe:

• National security and defense: if false infrastructures are presented or real ones are hidden, strategic analyses can be diverted or mistaken military decisions can be induced.
• Disinformation in conflicts or humanitarian crises: An altered image showing fake fires, floods, or troop movements can alter the international response, aid flows, or citizens' perceptions, especially if it is spread through social media or media without verification.
• Manipulation of realistic images of places: not only the general images are at stake. Nguyen et al. (2024) showed that it is possible to generate highly realistic synthetic satellite images of very specific facilities such as nuclear plants.

Crisis of trust and erosion of truth

For decades, satellite imagery has been perceived as one of the most objective and reliable sources of information about our planet. They were the graphic evidence that made it possible to confirm environmental phenomena, follow armed conflicts or evaluate the impact of natural disasters. In many cases, these images were used as "unbiased evidence," difficult to manipulate, and easy to validate. However, the emergence of synthetic images generated by artificial intelligence has begun to call into question that almost unshakable trust.

Today, when a satellite image can be falsified with great realism, a profound risk arises: the erosion of truth and the emergence of a crisis of confidence in spatial data.

The breakdown of public trust

When citizens can no longer distinguish between a real image and a fabricated one, trust in information sources is broken. The consequence is twofold:

• Distrust of institutions: if false images of a fire, a catastrophe or a military deployment circulate and then turn out to be synthetic, citizens may also begin to doubt the authentic images published by space agencies or the media. This "wolf is coming" effect generates skepticism even in the face of legitimate evidence.
• Effect on journalism: traditional media, which have historically used satellite imagery as an unquestionable visual source, risk losing credibility if they publish doctored images without verification. At the same time, the abundance of fake images on social media erodes the ability to distinguish what is real and what is not.
• Deliberate confusion: in contexts of disinformation, the mere suspicion that an image may be false can already be enough to generate doubt and sow confusion, even if the original image is completely authentic.

The following is a summary of the possible cases of manipulation and risk in satellite images:

Figure 2. Table showing possible cases of manipulation and risk in satellite images

Impact on decision-making and public policies

The consequences of relying on doctored images go far beyond the media arena:

• Urbanism and planning: decisions about where to build infrastructure or how to plan urban areas could be made on manipulated images, generating costly errors that are difficult to reverse.
• Emergency management: If a flood or fire is depicted in fake images, emergency teams can allocate resources to the wrong places, while neglecting areas that are actually affected.
• Climate change and the environment: Doctored images of glaciers, deforestation or polluting emissions could manipulate political debates and delay the implementation of urgent measures.
• Markets and insurance: Insurers and financial companies that rely on satellite imagery to assess damage could be misled, with significant economic consequences.

In all these cases, what is at stake is not only the quality of the information, but also the effectiveness and legitimacy of public policies based on that data.

The technological cat and mouse game

The dynamics of counterfeit generation and detection are already known in other areas, such as  video or audio deepfakes: every time a more realistic generation method emerges, a more advanced detection algorithm is developed, and vice versa. In the field of satellite images, this technological career has particularities:

• Increasingly sophisticated generators: today's broadcast models can create highly realistic scenes, integrating ground textures, shadows, and urban geometries that fool even human experts.
• Detection limitations: Although algorithms are developed to identify fakes (analyzing pixel patterns, inconsistencies in shadows, or metadata), these methods are not always reliable when faced with state-of-the-art generators.
• Cost of verification: independently  verifying a satellite image requires access to alternative sources or different sensors, something that is not always available to journalists, NGOs or citizens.
• Double-edged swords: The same techniques used to detect fakes can be exploited by those who generate them, further refining synthetic images and making them more difficult to differentiate.

From visual evidence to questioned evidence

The deeper impact is cultural and epistemological: what was previously assumed to be objective evidence now becomes an element subject to doubt. If satellite imagery is no longer perceived as reliable evidence, it weakens fundamental narratives around scientific truth, international justice, and political accountability.

• In armed conflicts, a satellite image showing possible war crimes can be dismissed under the accusation of being a deepfake.
• In international courts, evidence based on satellite observation could lose weight in the face of suspicion of manipulation.
• In public debate, the relativism of "everything can be false" can be used as a rhetorical weapon to delegitimize even the strongest evidence.

Strategies to ensure authenticity

The crisis of confidence in satellite imagery is not an isolated problem in the geospatial sector, but is part of a broader phenomenon: digital disinformation in the age of artificial intelligence. Just as  video deepfakes have called into question the validity of audiovisual evidence, the proliferation of synthetic satellite imagery threatens to weaken the last frontier of perceived objective data: the unbiased view from space.

Ensuring the authenticity of these images requires a combination of technical solutions and governance mechanisms, capable of strengthening traceability, transparency and accountability across the spatial data value chain. The main strategies under development are described below.

Robust metadata: Record origin and chain of custody

 Metadata is the first line of defense against manipulation. In satellite imagery, they should include detailed information about:

• The sensor used (type, resolution, orbit).
• The exact time of acquisition (date and time, with time precision).
• The precise geographical location (official reference systems).
• The applied processing chain (atmospheric corrections, calibrations, reprojections).

Recording this metadata in secure repositories allows the chain of custody to be reconstructed, i.e. the history of who, how and when an image has been manipulated. Without this traceability, it is impossible to distinguish between authentic and counterfeit images.

EXAMPLE: The  European Union's Copernicus program  already implements standardized and open metadata for all its Sentinel images, facilitating subsequent audits and confidence in the origin.

Digital signatures and blockchain: ensuring integrity

Digital signatures allow you to verify that an image has not been altered since it was captured. They function as a cryptographic seal that is applied at the time of acquisition and validated at each subsequent use.

Blockchain technology  offers an additional level of assurance: storing acquisition and modification records on an immutable chain of blocks. In this way, any changes in the image or its metadata would be recorded and easily detectable.

EXAMPLE: The ESA – Trusted Data Framework project  explores the use of blockchain to protect the integrity of Earth observation data and bolster trust in critical applications such as climate change and food security.

 Invisible watermarks: hidden signs in the image

Digital watermarking involves embedding imperceptible signals in the satellite image itself, so that any subsequent alterations can be detected automatically.

• It can be done at the pixel level, slightly modifying color patterns or luminance.
• It is combined with cryptographic techniques to reinforce its validity.
• It allows you to validate images even if they have been cropped, compressed, or reprocessed.

EXAMPLE: In the audiovisual sector, watermarks have been used for years in the protection of digital content. Its adaptation to satellite images is in the experimental phase, but it could become a standard verification tool.

Open Standards (OGC, ISO): Trust through Interoperability

Standardization is key to ensuring that technical solutions are applied in a coordinated and global manner.

• OGC (Open Geospatial Consortium) works on standards for metadata management, geospatial data traceability, and interoperability between systems. Their work on geospatial APIs and FAIR (Findable, Accessible, Interoperable, Reusable) metadata is essential to establishing common trust practices.
• ISO develops standards on information management and authenticity of digital records that can also be applied to satellite imagery.

EXAMPLE: OGC Testbed-19 included specific experiments on geospatial data authenticity, testing approaches such as digital signatures and certificates of provenance.

Cross-check: combining multiple sources

A basic principle for detecting counterfeits is to contrast sources. In the case of satellite imagery, this involves:

• Compare images from different satellites (e.g. Sentinel-2 vs. Landsat-9).
• Use different types of sensors (optical, radar SAR, hyperspectral).
• Analyze time series to verify consistency over time.

EXAMPLE: Damage verification in Ukraine following the start of the Russian invasion in 2022 was done by comparing images from several vendors (Maxar, Planet, Sentinel), ensuring that the findings were not based on a single source.

AI vs. AI: Automatic Counterfeit Detection

The same artificial intelligence that allows synthetic images to be created can be used to detect them. Techniques include:

• Pixel Forensics: Identify patterns generated by GANs or broadcast models.
• Neural networks trained to distinguish between real and synthetic images based on textures or spectral distributions.
• Geometric inconsistencies models: detect impossible shadows, topographic inconsistencies, or repetitive patterns.

EXAMPLE: Researchers at the University of Washington and other groups have shown that specific algorithms can detect satellite fakes with greater than 90% accuracy under controlled conditions.

Current Experiences: Global Initiatives

Several international projects are already working on mechanisms to reinforce authenticity:

• Coalition for Content Provenance and Authenticity (C2PA): A partnership between Adobe, Microsoft, BBC, Intel, and other organizations to develop an open standard for provenance and authenticity of digital content, including images. Its model can be applied directly to the satellite sector.
• OGC work: the organization promotes the debate on trust in geospatial data and has highlighted the importance of ensuring the traceability of synthetic and real satellite images (OGC Blog).
• NGA (National Geospatial-Intelligence Agency) in the US has publicly acknowledged the threat of synthetic imagery in defence and is driving collaborations with academia and industry to develop detection systems.

Towards an ecosystem of trust

The strategies described should not be understood as alternatives, but as complementary layers in a trusted ecosystem:

Figure 3. Layers to ensure confidence in synthetic satellite images

Success will depend on these mechanisms being integrated together, under open and collaborative frameworks, and with the active involvement of space agencies, governments, the private sector and the scientific community.

Conclusions

Synthetic images, far from being just a threat, represent a powerful tool that, when used well, can provide significant value in areas such as simulation, algorithm training or innovation in digital services. The problem arises when these images are presented as real without proper transparency, fueling misinformation or manipulating public perception.

The challenge, therefore, is twofold: to take advantage of the opportunities offered by the synthesis of visual data to advance science, technology and management, and to minimize the risks associated with the misuse of these capabilities, especially in the form of deepfakes or deliberate falsifications.

In the particular case of satellite imagery, trust takes on a strategic dimension. Critical decisions in national security, disaster response, environmental policy, and international justice depend on them. If the authenticity of these images is called into question, not only the reliability of the data is compromised, but also the legitimacy of decisions based on them.

The future of Earth observation will be shaped by our ability to ensure authenticity, transparency and traceability across the value chain: from data acquisition to dissemination and end use. Technical solutions (robust metadata, digital signatures, blockchain, watermarks, cross-verification, and AI for counterfeit detection), combined with governance frameworks and international cooperation, will be the key to building an ecosystem of trust.

In short, we must assume a simple but forceful guiding principle:

"If we can't trust what we see from space, we put our decisions on Earth at risk."

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.

Cities account for more than two-thirds of Europe's population and consume around 80% of energy. In this context, climate change is having a particularly severe impact on urban environments, not only because of their density, but also because of their construction characteristics, their energy metabolism and the scarcity of vegetation in many consolidated areas. One of the most visible and worrying effects is the phenomenon known as urban heat island (UHI).

Heat islands occur when the temperature in urban areas is significantly higher than in nearby rural or peri-urban areas, especially at night. This thermal differential can easily exceed five degrees Celsius under certain conditions. The consequences of this phenomenon go beyond thermal discomfort: it directly affects health, air quality, energy consumption, urban biodiversity and social equity.

In recent years, the availability of open data—especially geospatial data—has made it possible to characterize, map, and analyze urban heat islands with unprecedented accuracy. This article explores how this data can be used to design urban solutions adapted to climate change, with heat island mitigation as its focus.

What are urban heat islands and why do they occur?

Figure 1. Illustrative element on heat islands.

To intervene effectively in heat islands, it is necessary to know where, when and how they occur. Unlike other natural hazards, the heat island effect is not visible to the naked eye, and its intensity varies depending on the time of day, time of year, and specific weather conditions. It therefore requires a solid and dynamic knowledge base, which can only be built through the integration of diverse, up-to-date and territorialized data.

At this point, open geospatial data is a critical tool. Through satellite images, urban maps, meteorological data, cadastral cartography and other publicly accessible sets, it is possible to build urban thermal models, identify critical areas, estimate differential exposures and evaluate the impact of the measures adopted.

The main categories of data that allow us to address the phenomenon of heat islands from a territorial and interdisciplinary perspective are detailed below.

Types of geoespatial data applicable to the study of the phenomenon

1. Earth observation satellite data

Thermal sensors on satellites  such as Landsat 8/9 (NASA/USGS) or Sentinel-3 (Copernicus) make it possible to generate urban surface temperature maps with resolutions ranging from 30 to 1,000 metres. Although these images have spatial and temporal limitations, they are sufficient to detect patterns and trends, especially if combined with time series.

This data, accessible through platforms such as the Copernicus Open Access Hub or the USGS EarthExplorer, is essential for comparative studies between cities or for observing the temporal evolution of the same area.

2. Urban weather data

The network of AEMET stations, together with other automatic stations managed by autonomous communities or city councils, allows the evolution of air temperatures in different urban points to be analysed. In some cases, there are also citizen sensors or networks of sensors distributed in the urban space that allow real-time heat maps to be generated with high resolution.

3. Urban mapping and digital terrain models

Digital surface models (DSM), digital terrain models (DTM) and mappings derived from LIDAR allow the study of urban morphology, building density, street orientation, terrain slope and other factors that affect natural ventilation and heat accumulation. In Spain, this data is accessible through the National Center for Geographic Information (CNIG).

4. Land cover and land use databases

Databases such as Corine Land Cover of the Copernicus Programme, or land use maps at the regional level make it possible to distinguish between urbanised areas, green areas, impermeable surfaces and bodies of water. This information is key to calculating the degree of artificialization of an area and its relationship with the heat balance.

5. Inventories of urban trees and green spaces

Some municipalities publish on their open data portals the detailed inventory of urban trees, parks and gardens. These georeferenced data make it possible to analyse the effect of vegetation on thermal comfort, as well as to plan new plantations or green corridors.

6. Socioeconomic and vulnerability data

Data from the National Institute of Statistics (INE), together with the social information systems of autonomous communities and city councils, make it possible to identify the most vulnerable neighbourhoods from a social and economic point of view. Its cross-referencing with thermal data allows a climate justice dimension to be incorporated into decision-making.

Practical applications: how open data is used to act

Once the relevant data has been gathered and integrated, multiple analysis strategies can be applied to support public policies and urban projects with sustainability and equity criteria. Some of the main applications are described below.

·        Heat zone mapping and vulnerability maps: Using thermal imagery, weather data, and urban layers together, heat island intensity maps can be generated at the neighborhood or block level. If these maps are combined with social, demographic and public health indicators, it is possible to build thermal vulnerability maps, which prioritize intervention in areas where high temperatures and high levels of social risk intersect. These maps allow, for example:

·        Identify priority neighborhoods for urban greening.

·        Plan evacuation routes or shaded areas during heat waves.

·        Determine the optimal location of climate refuges.

·        Assessing the impact of nature-based solutions: Open data also makes it possible to monitor the effects of certain urban actions. For example, using time series of satellite images or temperature sensors, it is possible to assess how the creation of a park or the planting of trees on a street has modified the surface temperature. This ex-post evaluation approach  allows justifying public investments, adjusting designs and scaling effective solutions to other areas with similar conditions.

·        Urban modelling and climate simulations: three-dimensional urban models, built from open LIDAR data or cadastral mapping, make it possible to simulate the thermal behaviour of a neighbourhood or city under different climatic and urban scenarios. These simulations, combined with tools such as ENVI-met or Urban Weather Generator, are essential to support decision-making in urban planning.

Existing studies and analysis on urban heat islands: what has been done and what we can learn

During the last decade, multiple studies have been carried out in Spain and Europe that show how open data, especially geospatial data, allow the phenomenon of urban heat islands to be characterised and analysed. These works are fundamental not only because of their specific results, but also because they illustrate replicable and scalable methodologies. Some of the most relevant are described below.

Polytechnic University of Madrid study on surface temperature in Madrid

A team from the Department of Topographic Engineering and Cartography of the UPM analysed the evolution of surface temperature in the municipality of Madrid using thermal images from the Landsat 8 satellite in the summer period. The study focused on detecting spatial changes in warmer areas and relating them to land use, urban vegetation and building density.

Figure 2. Illustrative image. Source: generated with AI

Methodology:

Remote sensing techniques were applied to extract the surface temperature from the TIRS thermal channel of the Landsat. Subsequently, a statistical analysis of correlation between thermal values and variables such as NDVI (vegetation index), type of land cover (CORINE data) and urban morphology was carried out.

Main results:

Areas with higher building density, such as the central and southern neighborhoods, showed higher surface temperatures. The presence of urban parks reduced the temperature of their immediate surroundings by 3 to 5 °C. It was confirmed that the heat island effect intensifies at night, especially during persistent heat waves.

This type of analysis is especially useful for designing urban greening strategies and for justifying interventions in vulnerable neighbourhoods.

Barcelona Climate Vulnerability Atlas

Barcelona City Council, in collaboration with experts in public health and urban geography, developed a Climate Vulnerability Atlas which includes detailed maps of heat exposure, population sensitivity, and adaptive capacity. The objective was to guide municipal policies against climate change, especially in the field of health and social services.

Figure 3. Image containing fence, exterior, buildings and grass. Source: generated with AI

Methodology:

The atlas was developed by combining open and administrative data at the census tract level. Three dimensions were analysed: exposure (air temperature and surface data), sensitivity (advanced age, density, morbidity) and adaptive capacity (access to green areas, quality of housing, facilities). The indicators were normalized and combined through multi-criteria spatial analysis to generate a climate vulnerability index. The result made it possible to locate the neighbourhoods most at risk from extreme heat and to guide municipal measures.

Main results:

Based on the atlas, the network of "climate shelters" was designed, which includes libraries, civic centers, schools and conditioned parks, activated during episodes of extreme heat. The selection of these spaces was based directly on the atlas data.

Multitemporal analysis of the heat island effect in Seville

Researchers from the University of Seville used satellite data from Sentinel-3 and Landsat 8 to study the evolution of the heat island phenomenon in the city between 2015 and 2022. The aim was to evaluate the effectiveness of certain urban actions – such as the "Green your neighbourhood" plan – and to anticipate the effects of climate change on the city.

Methodology:

Thermal imaging and NDVI data were used to calculate temperature differences between urban areas and surrounding rural areas. Supervised classification techniques were also applied to identify land uses and their evolution. Open data from tree inventories and urban shade maps were used to interpret the results.

Main results:

Specific renaturation actions have a very positive local impact, but their effect on the city as a whole is limited if they are not integrated into a metropolitan-scale strategy. The study concluded that a continuous network of vegetation and bodies of water is more effective than isolated actions.

European comparison of the Urban Heat Island Atlas (Copernicus) project

Although it is not a Spanish study, the viewer developed by Copernicus for the European Urban Atlas programme offers a comparative analysis between European cities.

Methodology:

The viewer integrates Sentinel-3 thermal imagery, land cover data, and urban mapping to assess the severity of the heat island effect.

Figure 4. Illustration: Infographic showing the main factors causing the urban heat island effect (UHI). Urban areas retain heat due to tall buildings, impermeable surfaces and heat-retaining materials, while green areas are cooler Source: Urban heat islands.

Main results:

This type of tool allows smaller cities to have a first approximation of the phenomenon without the need to develop their own models. As it is based on open and free data, the viewer allows direct consultations by technicians and citizens.

Current limitations and challenges

Despite progress in opening up data, there are still significant challenges:

• Territorial inequality: not all cities have the same quality and quantity of data.

• Irregular update: Some sets are released on a one-off basis and are not updated regularly.

• Low granularity: Data is often aggregated by districts or census tracts, making street-scale interventions difficult.

• Lack of technical capacities: Many local governments do not have staff specialized in geospatial analysis.

• Little connection with citizens: the knowledge generated from data does not always translate into visible or understandable actions for the population.

Conclusion: building climate resilience from geoespatial data

Urban heat islands are not a new phenomenon, but in the context of climate change they take on a critical dimension. Cities that do not plan based on data will be increasingly exposed to episodes of extreme heat, with unequal impacts among their populations.

Open data—and in particular geospatial data—offers a strategic opportunity to transform this threat into a lever for change. With them we can identify, anticipate, intervene and evaluate. But for this to happen, it is essential to:

·        Consolidate accessible, up-to-date and quality data infrastructures.

·        To promote collaboration between levels of government, research centres and citizens.

·        Train municipal technicians in the use of geospatial tools.

·        Promote a culture of evidence-based decision-making and climate sensitivity.

Data does not replace politics, but it allows it to be founded, improved and made more equitable. In a global warming scenario, having open geospatial data is a key tool to make our cities more livable and better prepared for the future.

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.

In an increasingly interconnected and complex world, geospatial intelligence (GEOINT) has become an essential tool for defence and security decision-making . The ability to collect, analyse and interpret geospatial data enables armed forces and security agencies to better understand the operational environment, anticipate threats and plan operations more effectively.

In this context, satellite data, classified but also open data, have acquired significant relevance. Programmes such as Copernicus of the European Union provide free and open access to a wide range of Earth observation data, which democratises access to critical information and fosters collaboration between different actors.

This article explores the role of data in geospatial intelligence applied to defence, highlighting its importance, applications and Spain's leadership in this field.

Geospatial intelligence (GEOINT) is a discipline that combines the collection, analysis and interpretation of geospatial data to support decision making in a variety of areas, including defence, security and emergency management. This data may include satellite imagery, remotely sensed information, geographic information system (GIS) data and other sources that provide information on the location and characteristics of the terrain.

In the defence domain, GEOINT enables military analysts and planners to gain a detailed understanding of the operational environment, identify potential threats and plan operations with greater precision. It also facilitates coordination between different units and agencies, improving the effectiveness of joint operations.

Defence application

The integration of open satellite data into geospatial intelligence has significantly expanded defence capabilities. Some of the most relevant applications are presented below:

Figure 1. GEOINT applications in defence. Source: own elaboration

Geospatial intelligence not only supports the military in making tactical decisions, but also transforms the way military, surveillance and emergency response operations are planned and executed. Here we present concrete use cases where GEOINT, supported by open satellite data, has had a decisive impact.

Monitoring of military movements in conflicts

Case. Ukraine War (2,022-2,024)

Organisations such as the EU Satellite Centre (SatCen) and NGOs such as the Conflict Intelligence Team have used Sentinel-1 and Sentinel-2 (Copernicus) imagery for the Conflict Intelligence Team :

• Detect concentrations of Russian troops and military equipment.
• Analyse changes to airfields, bases or logistics routes.
• Support independent verification of events on the ground.

This has been key to EU and NATO decision-making, without the need to resort to classified data.

Maritime surveillance and border control

Case. FRONTEX operations in the Mediterranean

GEOINT powered by Sentinel-1 (radar) and Sentinel-3 (optical + altimeter) allows:

• Identify unauthorised vessels, even under cloud cover or at night.
• Integrate alerts with AIS (automatic ship identification system).
• Coordinate rescue and interdiction operations.

Advantage: Sentinel-1's synthetic aperture radar (SAR) can see through clouds, making it ideal for continuous surveillance.

Support to peace missions and humanitarian aid

Case. Earthquake in Syria/Turkey (2,023)

Open data (Sentinel-2, Landsat-8, PlanetScope free after catastrophe) were used for: 

• Detect collapsed areas and assess damage.
• Plan safe access routes.

Coordinate camps and resources with military support.

Spain's role

Spain has demonstrated a significant commitment to the development and application of geospatial intelligence in defence.

Figure 2. Comparative table of Spain's participation in different satellite projects. Source: own elaboration

Challenges and opportunities

While open satellite data offers many advantages, it also presents certain challenges that need to be addressed to maximise its usefulness in the defence domain.

• Data quality and resolution: While open data is valuable, it often has limitations in terms of spatial and temporal resolution compared to commercial or classified data. This may affect its applicability in certain operations requiring highly accurate information.

• Data integration: The integration of data from multiple sources, including open, commercial and classified data, requires systems and processes to ensure interoperability and consistency of information. This involves technical and organisational challenges that must be overcome.

• Security and confidentiality: The use of open data in defence contexts raises questions about the security and confidentiality of information. It is essential to establish security protocols and measures to protect sensitive information and prevent its misuse.

• Opportunities for collaboration: Despite these challenges, open satellite data offer significant opportunities for collaboration between different actors, including governments, international organisations, the private sector and civil society. Such collaboration can improve the effectiveness of defence operations and contribute to greater global security.

Recommendations for strengthening the use of open data in defence

Based on the above analysis, some key recommendations can be drawn to better exploit the potential of open data:

1. Strengthening open data infrastructures: consolidate national platforms integrating open satellite data for both civilian and military use, with a focus on security and interoperability.

2. Promotion of open geospatial standards (OGC, INSPIRE): Ensure that defence systems integrate international standards that allow the combined use of open and classified sources.

3. Specialised training: foster the development of capabilities in GEOINT analysis with open data, both in the military domain and in collaboration with universities and technology centres.

4. Civil-military cooperation: establish protocols to facilitate the exchange of data between civilian agencies (AEMET, IGN, Civil Protection) and defence actors in crisis or emergency situations.

5. Support to R&D&I: to foster research projects exploring the advanced use of open data (e.g. AI applied to Sentinel) with dual applications (civilian and security).

Conclusion

Geospatial intelligence and the use of open satellite data have transformed the way armed forces and security agencies plan and execute their operations. In a context of multidimensional threats and constantly evolving scenarios, having accurate, accessible and up-to-date information is more than an advantage: it is a strategic necessity.

Open data has established itself as a fundamental asset not only because it is free of charge, but also because of its ability to democratise access to critical information, foster transparency and enable new forms of collaboration between military, civilian and scientific actors. In particular:

• Improve the resilience of defence systems by enabling broader, cross-cutting analysis of the operating environment.
• Increase interoperability, as open formats and standards facilitate exchange between countries and agencies.
• They drive innovationby providing startups, research centres and universities with access to quality data that would otherwise be inaccessible.

In this context, Spain has demonstrated a clear commitment to this strategic vision, both through its national institutions and its active role in European programmes such as Copernicus, Galileo and the common defence missions.

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.

Geospatial data has driven improvements in a number of sectors, and energy is no exception. This data allows us to better understand our environment in order to promote sustainability, innovation and informed decision-making.

One of the main providers of open geospatial data is Copernicus, the European Union's Earth observation programme. Through a network of satellites called Sentinel and data from ground, sea and airborne sources, Copernicus provides geospatial information freely accessible through various platforms.

Although Copernicus data is useful in many areas, such as fighting climate change, urban planning or agriculture, in this article we will focus on its role in driving sustainability and energy efficiency. The availability of high quality open data fosters innovation in this sector by promoting the development of new tools and applications that improve energy management and use. Here are some examples.

Climate prediction to improve production

Geospatial data provide detailed information on weather conditions, air quality and other factors, which are essential for understanding and predicting environmental phenomena, such as storms or droughts, that affect energy production and distribution.

One example is this project which provides high-resolution wind forecasts to serve the oil and gas, aviation, shipping and defence sectors. It uses data from satellite observations and numerical models, including information on ocean currents, waves and sea surface temperature from the "Copernicus Marine Service". Thanks to its granularity, it can provide an accurate weather forecasting system at a very local scale, allowing a higher level of accuracy in the behaviour of extreme weather and climate phenomena.

Optimisation of resources

The data provided by Copernicus also allows the identification of the best locations for the installation of energy generation centres, such as solar and wind farms, by facilitating the analysis of factors such as solar radiation and wind speed. In addition, they help monitor the efficiency of these facilities, ensuring that they are operating at maximum capacity.

In this regard, a project has been developed to find the best site for a combined floating wind and wave energy system (i.e. based on wave motion). By obtaining both energies with a single platform, this solution saves space and reduces the impact on the ground, while improving efficiency. Wind and waves arrive at different times at the platform, so capturing both elements helps reduce variability and smoothes overall electricity production. Thanks to the Copernicus data (obtained from the Atlantic Service - Biscay Iberia Ireland - Ocean Wave Reanalysis), the provider of this situation was able to obtain separate components of wind and wave waves, which allowed a more complete understanding of the directionality of both elements. This work led to the selection of Biscay Marine Energy Platform (BiMEP). for the deployment of the device.

Another example is Mon Toit Solaire, an integrated web-based decision support system for the development of rooftop photovoltaic power generation. This tool simulates and calculates the energy potential of a PV project and provides users with reliable technical and financial information. It uses solar radiation data produced by the "Copernicus Atmospheric Monitoring Service", together with three-dimensional urban topographic data and simulations of tax incentives, energy costs and prices, allowing the return on investment to be calculated.

Environmental monitoring and impact assessment.

Geospatial information allows for improved environmental monitoring and accurate impact assessments in the energy sector. This data allows energy companies to identify environmental risks associated with their operations, design strategies to mitigate their impact and optimise their processes towards greater sustainability. In addition, they support environmental compliance by providing objective data-driven reporting, encouraging more responsible and environmentally friendly energy development.

Among the challenges posed by the conservation of ocean biodiversity, man-made underwater noise is recognised as a serious threat and is regulated at European level. In order to assess the impact on marine life of wind farms along the southern coast of France, this project uses high-resolution statistical sound maps, which provide a detailed view of coastal processes, with an hourly time frequency and a high spatial resolution of up to 1.8 km. In particular, they use information from the "Mediterranean Sea Physics Analysis and Forecasting" and "World Ocean Hourly Sea Surface Wind and Stress" services.

Emergency and environmental disaster management.

In disaster situations or extreme weather events, geospatial data can help quickly assess damage and coordinate emergency responses more efficiently.

They can also predict how spills will behave. This is the aim of the Marine Research Institute of the University of Klaipeda, which has developed a system for monitoring and forecasting chemical and microbiological pollution episodes using a high-resolution 3D operational hydrodynamic model. They use the Copernicus "Physical Analysis and Forecasts of the Baltic Sea". The model provides real-time, five-day forecasts of water currents, addressing the challenges posed by shallow waters and port areas. It aims to help manage pollution incidents, particularly in pollution-prone regions such as ports and oil terminals.

These examples highlight the usefulness of geospatial data, especially those provided by programmes such as Copernicus. The fact that companies and institutions can freely access this data is revolutionising the energy sector, contributing to a more efficient, sustainable and resilient system.

The value of open satellite data in Europe

Satellites have become essential tools for understanding the planet and managing resources efficiently. The European Union (EU) has developed an advanced space infrastructure with the aim of providing real-time data on the environment, navigation and meteorology.

This satellite network is driven by four key programmes:.

• Copernicus: Earth observation, environmental monitoring and climate change.
• Galileo: high-precision satellite navigation, alternative to GPS.
• EGNOS: improved positioning accuracy, key to aviation and navigation.
• Meteosat: padvanced meteorological prediction and atmospheric monitoring.

Through these programmes, Europe not only ensures its technological independence, but also obtains data that is made available to citizens to drive strategic applications in agriculture, security, disaster management and urban planning.

In this article we will explore each programme, its satellites and their impact on society, including Spain''s role in each of them.

Copernicus: Europe''s Earth observation network

Copernicus is the EU Earth observation programme, managed by the European Commission with the technical support of the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).. It aims to provide free and open data about the planet to monitor climate change, manage natural resources and respond to emergencies.

The programme is structured into three main components:

1. Space component: consists of a series of satellites called Sentinel, developed specifically for the needs of Copernicus. These satellites provide high quality data for various applications, such as land, sea and atmospheric monitoring.
2. Component in situ: includes data collected through ground, air and sea stations. These data are essential to calibrate and validate the information obtained by the satellites, ensuring its accuracy and reliability.
3. Operational Services: offers six thematic services that transform collected data into useful information for users:
   • Atmospheric monitoring
   • Marine monitoring
   • Terrestrial monitoring
   • Climate change
   • Emergency management
   • Safety

These services provide information in areas such as air quality, ocean status, land use, climate trends, disaster response and security, supporting informed decision-making in Europe.

Spain has played a key role in the manufacture of components for the Sentinel satellites. Spanish companies have developed critical structures and sensors, and have contributed to the development of data processing software.  Spain is also leading projects such as the Atlantic Constellation, which will develop small satellites for climate and oceanic monitoring.

Sentinel satellite

Figure 1. Table satellites Sentinel. Source: own elaboration

Galileo: the european GPS

Galileo is the global navigation satellite system developed by the European Union, managed by the European Space Agency (ESA) and operated by the European Union Space Programme Agency (EUSPA). It aims to provide a reliable and highly accurate global positioning service, independent of other systems such as the US GPS, China''s Beidou or Russia''s GLONASS. Galileo is designed for civilian use and offers free and paid services for various sectors, including transport, telecommunications, energy and finance.

Spain has played a leading role in the Galileo programme. The European GNSS Service Centre (GSC), located in Torrejón de Ardoz, Madrid, acts as the main contact point for users of the Galileo system. In addition, Spanish industry has contributed to the development and manufacture of components for satellites and ground infrastructure, strengthening Spain''s position in the European aerospace sector.

Figure 2. Satellite Galileo. Source: own elaboration

EGNOS: improving the accuracy of GPS and Galileo

 The European Geostationary Navigation Overlay Service (EGNOS) is the European satellite-based augmentation system (Satellite Based Augmentation System or SBAS) designed to improve the accuracy and reliability of global navigation satellite systems ( Global Navigation Satellite System, GNSS), such as GPS and, in the future, Galileo. EGNOS provides corrections and integrity data that allow users in Europe to determine their position with an accuracy of up to 1.5 metres, making it suitable for safety-critical applications such as aviation and maritime navigation.

Spain has played a leading role in the development and operation of EGNOS. Through ENAIRE, Spain hosts five RIMS Reference Stations located in Santiago, Palma, Malaga, Gran Canaria and La Palma. In addition, the Madrid Air Traffic Control Centre, located in Torrejón de Ardoz, hosts one of the EGNOS Mission Control Centres (MCC), operated by ENAIRE. The Spanish space industry has contributed significantly to the development of the system, with companies participating in studies for the next generation of EGNOS.

Figure 3. Table satellite EGNOS. Source: own elaboration

Meteosat: high precision weather forecasting

The Meteosat programme consists of a series of geostationary meteorological satellites initially developed by the European Space Agency (ESA) and currently operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). These satellites are positioned in geostationary orbit above the Earth''s equator, allowing continuous monitoring of weather conditions over Europe, Africa and the Atlantic Ocean. Its main function is to provide images and data to facilitate weather prediction and climate monitoring.

Spain has been an active participant in the Meteosat programme since its inception. Through the Agencia Estatal de Meteorología (AEMET), Spain contributes financially to EUMETSAT and participates in the programme''s decision-making and operations. In addition, the Spanish space industry has played a key role in the development of the Meteosat satellites. Spanish companies have been responsible for the design and supply of critical components for third-generation satellites, including scanning and calibration mechanisms.

Figure 4. Metosat satellite. Source: own elaboration

Access to the data of each programme

Each programme has different conditions and distribution platforms in terms of access to data:

• Copernicus: provides free and open data through various platforms.  Users can access satellite imagery and products through the Copernicus Data Space Ecosystem, which offers search, download and processing tools. Data can also be obtained through APIs for integration into automated systems.
• Galileo: its open service (Open Service - OS) allows free use of the navigation signals for any user with a compatible receiver, free of charge. However, direct access to raw satellite data is not provided. For information on services and documentation, access is via the European GNSS Services Centre (GSC):
  • Galileo Portal.
  • Registration for access to the High Accuracy Service (HAS) (registration required).
• EGNOS: This system improves navigation accuracy with GNSS correction signals.  Data on service availability and status can be found on the EGNOS User Support platform..
• Meteosat: Meteosat satellite data are available through the EUMETSAT platform. There are different levels of access, including some free data and some subject to registration or payment.  For imagery and meteorological products you can access the EUMETSAT Data Centre..

In terms of open access, Copernicus is the only programme that offers open and unrestricted data. In contrast, Galileo and EGNOS provide free services, but not access to raw satellite data, while Meteosat requires registration and in some cases payment for access to specific data.

Conclusions

The Copernicus, Galileo, EGNOS and Meteosat programmes not only reinforce Europe''s space sovereignty, but also ensure access to strategic data essential for the management of the planet. Through them, Europe can monitor climate change, optimise global navigation, improve the accuracy of its positioning systems and strengthen its weather predictioncapabilities, ensuring more effective responses to environmental crises and emergencies.

Spain plays a fundamental role in this space infrastructure, not only with its aerospace industry, but also with its control centres and reference stations, consolidating itself as a key player in the development and operation of these systems.

Satellite imagery and data have evolved from scientific tools to become essential resources for security, environmental management and sustainable growth. In a world increasingly dependent on real-time information, access to this data is critical for climate resilience, spatial planning, sustainable agriculture and ecosystem protection.

The future of Earth observation and satellite navigation is constantly evolving, and Europe, with its advanced space programmes, is positioning itself as a leader in the exploration, analysis and management of the planet from space.

Access to this data allows researchers, businesses and governments to make more informed and effective decisions. With these systems, Europe and Spain guarantee their technological independence and strengthen their leadership in the space sector.

Ready to explore more? Access the links for each programme and discover how this data can transform our world.

Figure 5. Source: own elaboration

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.

Geospatial data capture is essential for understanding our environment, making informed decisions and designing effective policies in areas such as urban planning, natural resource management or emergency response. In the past, this process was mainly manual and labour-intensive, based on ground measurements made with tools such as total stations and levels. Although these traditional techniques have evolved significantly and are still widely used, they have been complemented by automated and versatile methods that allow more efficient and detailed data collection.

The novelty in the current context lies not only in technological advances, which have improved the accuracy and efficiency of geospatial data collection, but also because it coincides with a widespread shift in mindset towards transparency and accessibility. This approach has encouraged the publication of the data obtained as open resources, facilitating their reuse in applications such as urban planning, energy management and environmental assessment. The combination of advanced technology and an increased awareness of the importance of information sharing marks a significant departure from traditional techniques.

In this article, we will explore some of the new methods of data capture, from photogrammetric flights with helicopters and drones, to ground-based systems such as mobile mapping, which use advanced sensors to generate highly accurate three-dimensional models and maps. In addition, we will learn how these technologies have empowered the generation of open data, democratising access to key geospatial information for innovation, sustainability and public-private collaboration.

Aerial photogrammetry: helicopters with advanced sensors

In the past, capturing geospatial data from the air involved long and complex processes. Analogue cameras mounted on aircraft generated aerial photographs that had to be processed manually to create two-dimensional maps. While this approach was innovative at the time, it also had limitations, such as lower resolution, long processing times and greater dependence on weather and daylight. However, technological advances have reduced these restrictions, even allowing operations at night or in adverse weather conditions.

Today, aerial photogrammetry has taken a qualitative leap forward thanks to the use of helicopters equipped with state-of-the-art sensors. The high-resolution digital cameras allow images to be captured at multiple angles, including oblique views that provide a more complete perspective of the terrain. In addition, the incorporation of thermal sensors and LiDAR (Light Detection and Ranging) technologies adds an unprecedented layer of detail and accuracy. These systems generate point clouds and three-dimensional models that can be integrated directly into geospatial analysis software, eliminating much of the manual processing.

Figure 1. Table with advantages and disadvantages of aerial photogrammetry with helicopters.

Mobile mapping: from backpacks to BIM integration

The mobile mapping is a geospatial data capture technique using vehicles equipped with cameras, LiDAR scanners, GPS and other advanced sensors. This technology allows detailed information to be collected as the vehicle moves, making it ideal for mapping urban areas, road networks and dynamic environments.

In the past, topographic surveys required stationary measurements, which meant traffic disruptions and considerable time to cover large areas. In contrast, mobile mapping has revolutionised this process, allowing data to be captured quickly, efficiently and with less impact on the environment. In addition, there are portable versions of this technology, such as backpacks with robotic scanners, which allow access to pedestrian or hard-to-reach areas.

Figure 2. Image captured with mobile mapping techniques.

Figure 3. Table with advantages and disadvantages of mobile mopping.

The mobile mapping is presented as a versatile and efficient solution for capturing geospatial data on the move, becoming a key tool for the modernisation of urban and territorial management systems.

HAPS and ballons: new heights for information capture

HAPS (High-Altitude Platform Stations) and hot-air balloons represent an innovative and efficient alternative for capturing geospatial data from high altitudes. These platforms, located in the stratosphere or at controlled altitudes, combine features of drones and satellites, offering an intermediate solution that stands out for its versatility and sustainability:

• HAPS, like zeppelins and similar aircraft, operate in the stratosphere, at altitudes between 18 and 20 kilometres, allowing a wide and detailed view of the terrain.
• The aerostatic balloons, on the other hand, are ideal for local or temporary studies, thanks to their easiness of deployment and operation at lower altitudes.

Both technologies can be equipped with high-resolution cameras, LiDAR sensors, thermal instruments and other advanced technologies for data capture.

Figure 4. Table with advantages and disadvantages of HAPS and ballons

HAPS and balloons are presented as key tools to complement existing technologies such as drones and satellites, offering new possibilities in geospatial data collection in a sustainable, flexible and efficient way. As these technologies evolve, their adoption will expand access to crucial data for smarter land and resource management.

Satellite technology: PAZ satellite and its future with PAZ-2

Satellite technology is a fundamental tool for capturing geospatial data globally. Spain has taken significant steps in this field with the development and launch of the PAZ satellite. This satellite, initially designed for security and defence purposes, has shown enormous potential for civilian applications such as environmental monitoring, natural resource management and urban planning.

PAZ is an Earth observation satellite equipped with a synthetic aperture radar (SAR), which allows high-resolution imaging, regardless of weather or light conditions.

The upcoming launch of PAZ-2 (planned for 2030) promises to further expand Spain''s observation capabilities. This new satellite, designed with technological improvements, aims to complement the functions of PAZ and increase the availability of data for civil and scientific applications. Planned improvements include:

• Higher image resolution.
• Ability to monitor larger areas in less time.
• Increased frequency of captures for more dynamic analysis.

Figure 5. Table with advantages and disadvantages of PAZ and PAZ-2 satellite technology

With PAZ and the forthcoming PAZ-2, Spain strengthens its position in the field of satellite observation, opening up new opportunities for efficient land management, environmental analysis and the development of innovative solutions based on geospatial datas. These satellites are not only a technological breakthrough, but also a strategic tool to promote sustainability and international cooperation in data access.

Conclusion: challenges and opportunities in data management 

The evolution of geospatial data capture techniques offers a unique opportunity to improve the accuracy, accessibility and quality of data, and in the specific case of open data, it is essential to foster transparency and re-use of public information. However, this progress cannot be understood without analysing the role played by technological tools in this process.

Innovations such as LiDAR in helicopters, Mobile Mapping, SAM, HAPS and satellites such as PAZ and PAZ-2 not only optimise data collection, but also have a direct impact on data quality and availability.

In short, these technological tools generate high quality information that can be made available to citizens as open data, a situation that is being driven by the shift in mindset towards transparency and accessibility. This balance makes open data and technological tools complementary, essential to maximise the social, economic and environmental value of geospatial data.

You can see a summary of these techniques and their applications in the following infographic:

Download the infographic here

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.

In February 2024, the European geospatial community took a major step forward with the first major update of the INSPIRE implementation schemes in almost a decade. This update, which generates version 5.0 of the schemas, introduces changes that affect the way spatial data are harmonised, transformed and published in Europe. For implementers, policy makers and data users, these changes present both challenges and opportunities.

In this article, we will explain what these changes entail, how they impact on data validation and what steps need to be taken to adapt to this new scenario.

What is INSPIRE and why does it matter?

The INSPIRE Directive (Infrastructure for Spatial Information in Europe) determines the general rules for the establishment of an Infrastructure for Spatial Information in the European Community based on the Member States'' Infrastructures. Adopted by the European Parliament and the Council on March 14, 2007 (Directive 2007/2/EC), it is designed to achieve these objectives by ensuring that spatial information is consistent and accessible across EU member countries.

What changes with the 5.0 upgrade?

The transition to version 5.0 brings significant modifications, some of which are not backwards compatible. Among the most notable changes are:

• Removal of mandatory properties: this simplifies data models, but requires implementers to review their previous configurations and adjust the data to comply with the new rules.
• Renaming of types and properties: with the update of the INSPIRE schemas to version 5.0, some element names and definitions have changed. This means that data that were harmonised following the 4.x schemas no longer exactly match the new specifications. In order to keep these data compliant with current standards, it is necessary to re-transform them using up-to-date tools. This re-transformation ensures that data continues to comply with INSPIRE standards and can be shared and used seamlessly across Europe. The complete table with these updates is as follows: 

Figure 1. Latest INSPIRE updates.

• Major changes in version 4.0: although normally a major change in a schema would lead to a new major version (e.g. from 4.0 to 5.0), some INSPIRE schemas in version 4.0 have received significant updates without changing version number. A notable example of this is the Planned Land Use (PLU) scheme. These updates imply that projects and services using the PLU scheme in version 4.0 must be reviewed and modified to adapt to the new specifications. This is particularly relevant for those working with XPlanung, a standard used in urban and land use planning in some European countries. The changes made to the PLU scheme oblige implementers to update their transformation projects and republish data to ensure that they comply with the new INSPIRE rules.

Impact on validation and monitoring

Updating affects not only how data is structured, but also how it is validated. The official INSPIRE tools, such as the Validador, have incorporated the new versions of the schemas, which generates different validation scenarios:

• Data conforming to previous versions: data harmonised to version 4.x can still pass basic validation tests, but may fail specific tests requiring the use of the updated schemas.
• Specific tests for updated themes: some themes, such as Protected Sites, require data to follow the most recent versions of the schemas to pass all compliance tests.

In addition, the Joint Research Center (JRC) has indicated that these updated versions will be used in official INSPIRE monitoring from 2025 onwards, underlining the importance of adapting as soon as possible.

What does this mean for consumers?

To ensure that data conforms to the latest versions of the schemas and can be used in European systems, it is essential to take concrete steps:

• If you are publishing new datasets: use the updated versions of the schemas from the beginning.
• If you are working with existing data: update the schemas of your datasets to reflect the changes you have made. This may involve adjusting types of features and making new transformations.
• Publishing services: If your data is already published, you will need to re-transform and republish it to ensure it conforms to the new specifications.

These actions are essential not only to comply with INSPIRE standards, but also to ensure long-term data interoperability.

Conclusion

The update to version 5.0 of the INSPIRE schemas represents a technical challenge, but also an opportunity to improve the interoperability and usability of spatial data in Europe. Adopting these modifications not only ensures regulatory compliance, but also positions implementers as leaders in the modernisation of spatial data infrastructure.

Although the updates may seem complex, they have a clear purpose: to strengthen the interoperability of spatial data in Europe. With better harmonised data and updated tools, it will be easier for governments, businesses and organisations to collaborate and make informed decisions on crucial issues such as sustainability, land management and climate change.

Furthermore, these improvements reinforce INSPIRE''s commitment to technological innovation, making European spatial data more accessible, useful and relevant in an increasingly interconnected world.

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of its author.

In the field of geospatial data, encoding and standardisation play a key role in ensuring interoperability between systems and improving accessibility to information.

The INSPIRE Directive (Infrastructure for Spatial Information in Europe) determines the general rules for the establishment of an Infrastructure for Spatial Information in the European Community based on the Member States' Infrastructures.  Adopted by the European Parliament and the Council on March 14, 2007 (Directive 2007/2/EC), it is designed to achieve these objectives by ensuring that spatial information is consistent and accessible across EU member countries.

Among the various encodings available for INSPIRE datasets, the GeoPackage standard emerges as a flexible and efficient alternative to traditional formats such as GML or GeoJSON. This article will explore how GeoPackage can improve INSPIRE data management and how it can be implemented using tools such as hale studio, a visual platform that facilitates data transformation according to INSPIRE specifications.

What is GeoPackage?

GeoPackage es un estándar desarrollado por el Open Geospatial Consortium (OGC) que utiliza SQLite como base para almacenar información geoespacial de manera compacta y accesible. Unlike other formats that require intermediate transformation processes, the data in a GeoPackage file can be read and updated directly in its native format. This allows for more efficient read and write operations, especially in GIS applications.

Main features of GeoPackage

• Open format and standard: as an open standard, GeoPackage is suitable for the publication of open spatial data, facilitating access to geospatial data in formats that users can handle without costly licensing or usage restrictions.
• Unique container: a GeoPackage file can store vector data, image mosaics and non-spatial data.
• Compatibility: is supported by several GIS platforms, including QGIS and ArcGIS, as well as ETL tools such as FME.
• Spatial indexing: the format includes spatial indexes (RTree) that allow faster data search and manipulation.

For more technical details, please refer to the GeoPackage standard on the OGC website.

Why use GeoPackage in INSPIRE?

INSPIRE requires spatial data to be interoperable at European level, and its default encoding standard is GML. However, GeoPackage is offered as an alternative that can reduce complexity in certain use cases, especially those where performance and usability are crucial.

The use of GeoPackage within INSPIRE is supported by good practices developed to create optimised logical models for ease of use in GIS environments. These practices allow the creation of use-case specific schemas and offer a flexibility that other formats do not provide. In addition, GeoPackage is especially useful in scenarios where medium to large datasets are handled, as its compact format reduces file size and therefore facilitates data exchange.

Implementation of GeoPackage in INSPIRE using Hale Studio

One of the recommended tools to implement GeoPackage in INSPIRE is the software open-source hale studio. This data transformation software allows mapping and transforming data models visually and without programming.

The following describes the basic steps for transforming an INSPIRE-compliant dataset using hale studio:

1. Load the source model: import the dataset in its original format, such as GML.
2. Define the target model (GeoPackage): load a blank GeoPackage file to act as the target model for storing the transformed data.
3. Configure data mapping: through the hale visual interface, map attributes and apply transformation rules to ensure compliance with the INSPIRE GeoPackage model.
4. Export the dataset: once the transformation has been validated, export the file in GeoPackage format.

Hale studio facilitates this transformation and enables data models to be optimised for improved performance in GIS environments. More information about hale studio and its transformation capabilities is available on its official website.

Examples of implementation

The application of the GeoPackage standard in INSPIRE has already been tested in several use cases, providing a solid framework for future implementations.

1. Environmental Noise Directive (END): in this context, GeoPackage has been used to store and manage noise-related data, aligning the models with INSPIRE specifications. The European Environment Agency (EEA) provides templates and guidelines to facilitate this implementation, available in its repository.

2. Project GO-PEG: this project uses GeoPackage to develop 3D models in geology, allowing the detailed representation of geological areas, such as the Po basin in Italy. Guidelines and examples of GO-PEG implementation are available here.

These examples illustrate how GeoPackage can improve the efficiency and usability of INSPIRE data in practical applications, especially in GIS environments that require fast and direct manipulation of spatial data.

The implementation of GeoPackage in the framework of INSPIRE demonstrates its applicability for open data at the European level. Initiatives such as the Environmental Noise Directive (END) and the GO-PEG Project have shown how open data in GeoPackage can serve multiple sectors, from environmental management to geological surveys.

Benefits of GeoPackage for data providers and data users

The adoption of GeoPackage in INSPIRE offers benefits for both data generators and data consumers:

• For suppliers: GeoPackage's simplified model reduces coding errors and improves data harmonisation, making it easier to distribute data in compact formats.
• For users: compatibility with GIS tools allows access to data without the need for additional transformations, improving the consumption experience and reducing loading and consultation times.

Limitations and challenges

While GeoPackage is a robust alternative, there are some challenges to consider:

• Interoperability limitations: unlike GML, GeoPackage is not compatible with all network data publishing services, although advances in protocols such as STAC are improving these limitations.

• Optimisation for large datasets: although GeoPackage is optimal for medium to large datasets, file size can be a constraint on extremely large data or low bandwidth networks.

Conclusion

The incorporation of the GeoPackage standard into INSPIRE represents a significant advance for the management and distribution of spatial data in Europe, promoting a more efficient and accessible spatial data infrastructure. This approach contributes to the interoperability of data and facilitates its use in various GIS systems, improving the experience of both providers and users.

For those wishing to implement this format, tools such as hale studio offer practical and accessible solutions that simplify the INSPIRE data transformation process. With the adoption of best practices and the use of optimised data models, GeoPackage can play a crucial role in the future of spatial data infrastructure in Europe. In addition, this approach aligned with the principles of transparency and data reuse allows administrations and organisations to take advantage of open data to support informed decision-making and the development of innovative applications in various areas.

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of its author.