Organizations involved in SDG monitoring face persistent, systemic challenges in collecting, integrating, and maintaining reliable environmental and socio-economic data. An observed obstacle is data availability and consistency, as many national statistical offices lack access to the spatially detailed, regularly updated datasets necessary to accurately measure environmental change. Compounding this is the issue of cost and resource constraints, where traditional, field-based data collection methods prove expensive and unsustainable for long-term, nationwide monitoring efforts. Furthermore, methodological uncertainty hinders cross-country comparability, as SDG indicators often aggregate data from multiple sources with widely varying quality.
Finally, these difficulties are exacerbated by limited technical capacity among end-users, who often lack the required training, infrastructure, and tools to effectively integrate modern data sources, such as EO data, into their national reporting frameworks, highlighting a critical need for stronger cross-sector collaboration between statistical, environmental, and planning authorities to produce timely, evidence-based reports and implement effective policy responses.
How EO can help
EO provides an essential, scalable, and cost-effective means of tracking the environmental and societal changes underpinning the SDGs. This capability stems from its unique capacity to provide spatially consistent, repeatable data—for instance, through open-access satellite missions like Copernicus Sentinel—which is crucial for producing standardized global indicators.
Furthermore, EO time series inherently support long-term monitoring, allowing countries to assess progress and trends over time with the necessary temporal consistency. EO plays a vital role in reducing data gaps by covering areas where ground-based measurements are sparse or unavailable, thereby ensuring inclusiveness in global monitoring efforts.
Critically, EO-derived indicators are objective and verifiable, significantly enhancing transparency and helping national agencies improve the credibility of their SDG reporting. By combining EO with in-situ and socio-economic data, countries gain a better understanding of where, how, and why progress is being made, effectively turning global commitments into measurable, actionable insights.
Key examples
1) IdeAtlas – EO for Inclusive Urban Development (SDG 11.1.1)
The IdeAtlas project is dedicated to supporting SDG Indicator 11.1.1 ("Proportion of urban population living in slums, informal settlements, or inadequate housing"). Its core objective is to develop and rigorously validate AI-based methods that can automatically map and characterize the spatial extent of informal settlements using EO data.
The project’s approach leverages sophisticated machine learning techniques to extract critical data points—such as settlement patterns, urban morphology, and building density—from high-resolution satellite imagery. These algorithms are implemented within a seamless, cloud-based, end-to-end processing system whose performance is being demonstrated and verified across eight diverse test cities worldwide. The resulting impact will enable national and local governments to quickly identify patterns of informal growth, directly supporting inclusive urban planning and providing a crucial, scalable tool for monitoring progress toward SDG 11.1.1.
The project builds on the lineage of prior initiatives, including IDEAMAPS and SLUMAP, and is defined by a user-centered co-design approach involving local authorities, NGOs, and international organizations.
2) Sen4LDN – EO for Land Degradation Neutrality (SDG 15.3.1)
The Sentinels for Land Degradation Neutrality Monitoring (Sen4LDN) project is strategically focused on supporting SDG Indicator 15.3.1: "Proportion of land that is degraded over total land area." Its core objective is to develop, demonstrate, and validate robust, automated EO methods for accurately assessing land degradation at both national and sub-national scales. The project's highly technical approach utilizes multi-sensor EO time series data from platforms such as Sentinel-1, Sentinel-2, and Landsat to track three critical indicators essential for measuring land health: land cover change, land productivity dynamics, and carbon stock trends.
Concentrating its efforts in the specific countries of Colombia, Uganda, and Portugal, the Sen4LDN project's methodology is aligned directly with the UNCCD (United Nations Convention to Combat Desertification) Land Degradation Neutrality (LDN) framework. The successful implementation of the project provides significant impact by delivering higher-resolution national assessments. This detailed data directly supports national restoration planning, facilitates accurate SDG reporting, and aids in developing climate-resilient land management strategies. Ultimately, the project's outcome is to demonstrate that EO can provide policy-ready, spatially detailed evidence to countries actively striving to achieve the goal of Land Degradation Neutrality by 2030.
3) EuMon – EO for Coastal Water Quality (SDG 14.1.1a)
The Eutrophication Monitoring (EuMon) project directly addresses the urgent need to support SDG Indicator 14.1.1a: "Index of coastal eutrophication." Its central objective is to create a robust, pre-operational Earth Observation (EO)-based system for reliably monitoring nutrient-driven pollution in coastal waters. The project employs a hybrid approach, combining ocean colour EO data from sources like Sentinel-3 OLCI with essential in-situ measurements to accurately detect and quantify the presence of harmful algal blooms. This system is designed for deployment through national EO platforms and is directly linked to existing SDG reporting mechanisms.
EuMon places a high priority on strong user engagement through a co-creation methodology, featuring Living Labs that facilitate collaboration with partners such as the Regional Environmental Centre (REC) Albania, the National Bureau of Statistics of Tanzania, and ISPRA (Italy). This ensures the EO outputs are aligned with user needs. The project's successful implementation has a vital impact by demonstrating how EO technology can make complex coastal water-quality evidence immediately accessible to national authorities and local decision-makers, supporting timely, evidence-based interventions needed to manage marine resources and significantly enhancing countries' ability to meet and report on the targets for SDG 14.1.1a.
