Satellites help track where buildings appear and how built-up areas change over time. Analysts compare images from multiple dates, use classification methods to identify likely built-up patterns, and review flagged areas. Those images show physical change—not whether a place is legally informal, what services residents have, or what households need. Reliable monitoring therefore combines imagery with clear local definitions, validation, and careful reporting.
What can satellite images tell us about informal settlements?
Repeated Earth-observation images can reveal where structures or built-up surfaces have appeared, expanded, or changed. Comparing observations over time can help analysts identify candidate areas for further review. The UN-Habitat Earth Observation Toolkit documents urban monitoring uses, including analysis of Landsat imagery to map built-up growth.
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The European Commission Joint Research Centre describes a 2020 Global Human Settlement Layer built-up grid derived from Sentinel-2 at 10 m spatial resolution. A grid at that resolution can support broad built-up mapping, but it should not be treated as proof that every individual structure is visible or distinguishable. The JRC also describes Sentinel-1 radar data as publicly, openly, and freely available through Copernicus; check current release metadata and access terms before using a dataset. JRC Global Human Settlement Layer
Satellite imagery does not directly reveal residents’ tenure, household circumstances, legal status, or access to services. The UN’s indicator methodology notes that definitions of informal settlements can vary and that the ephemeral nature of such settlements makes trend analysis difficult. A built-up signal is therefore evidence of physical change, not a standalone determination that an area is informal. UN indicator methodology for SDG 11.1.1
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How can you tell when a settlement is growing?
- Define the question and local criteria. Decide whether you are measuring built-up expansion, identifying candidate informal areas, or tracking a locally defined indicator. State the geographic boundary, period, and operational definition; an image classification cannot supply those choices.
- Gather imagery from multiple dates. Use observations that cover the same area at different times, noting the dates, sensor, resolution, and gaps. Optical imagery records reflected light; radar offers another kind of observation and can be useful when clouds interfere with optical views. Dataset availability and suitable historical coverage depend on the source and location.
- Extract signals that may indicate built-up land. Depending on the data and task, analysts can examine spectral values, derived indices, texture patterns, or radar responses. They compare classified areas across dates to identify places that appear to have changed.
- Flag candidates for review. Classification models can produce probability maps or candidate polygons. These are prompts for investigation, not declarations that a place is an informal settlement.
- Check the candidates against other evidence. Review historical high-resolution imagery and, where appropriate, use field observations and community knowledge. Record what was checked and where validation could not be done.
- Report what the map establishes. Identify the imagery and dates used, the definition applied, the validation method, and remaining uncertainty. Keep measured built-up change separate from any inference about settlement status.
Why do satellite classifications differ?
Results depend on the imagery, location, training labels, features, and classification method. A 2021 study by Agatha C. H. de Mattos, Gavin McArdle, and Michela Bertolotto compared texture and multispectral methods on labelled Sentinel-2 data from four cities. In that particular evaluation, the texture method reported 97% average accuracy for the slum class and 94% mean intersection over union; its multispectral method reported 75% average accuracy and 64% mean intersection over union. These are study-specific results, not expected accuracy figures for other places or projects. de Mattos, McArdle, and Bertolotto, 2021
The Colombia case study by Tingzon and colleagues used machine learning on Sentinel-2 time series to identify potential Venezuelan migrant settlements emerging between 2015 and 2020. The team produced probability maps, reviewed historical high-resolution imagery, and used a field-contributor platform for on-the-ground checks. Its experience shows why model outputs benefit from human and field review; it does not establish that the same workflow or error rate will transfer unchanged to another city. Tingzon et al., 2020
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What can look like settlement expansion when it is not?
Changes in an image can have several causes. In the Colombia study, cloud cover, vegetation changing to bare land, and vegetation or bare land becoming formal settlement all generated false positives. The authors found that biennial aggregation helped reduce cloud-related false positives in their setting and suggested texture features could help distinguish informal areas from bare land and formal settlement. These are findings from that case, not universal rules.
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- Clouds and image gaps: A cloud or its shadow can obscure the surface and confuse comparisons between dates.
- Vegetation and exposed soil: Seasonal or land-cover changes can resemble a newly built surface to a classifier.
- Formal construction: New buildings are physical growth, but imagery alone may not tell whether their development is formal or informal.
- Different observation conditions: Sensor, date, resolution, and processing choices can affect which changes are detectable.
How should a candidate map be validated and used?
Validation makes a candidate map more useful, but it does not turn an image into a complete account of people’s lives. Review earlier high-resolution imagery to check whether a flagged area’s physical pattern is consistent over time. Where the purpose, safety, and context allow, compare the map with field observations and knowledge from people who live or work in the area. Keep a record of the date, evidence, and limits of each check.
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Mapping choices also have social and political effects. Richard Sliuzas, in a UN-Habitat lecture synopsis, describes slum mapping as a technical process with socio-political dimensions, including choices about map legends, processes, and access to spatial information. In practical terms, explain who defined the categories, who can access the resulting data, and how affected communities can correct or contest interpretations. UN-Habitat lecture synopsis on slum mapping
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What has satellite monitoring shown at larger scales?
The UAE Federal Competitiveness and Statistics Centre, as reported by UN-Habitat’s Earth Observation Toolkit, used Landsat imagery spanning 1972–2020 to extract built-up areas and describe growth patterns in roads, infrastructure, and buildings nationally and by emirate. That is an example of long-term built-up monitoring; it is not a statistic for the expansion rate of informal settlements. The sources cited here do not establish a robust current global rate specifically for informal-settlement expansion.
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For any project, compare imagery resolution, repeat frequency and historical coverage, optical or radar inputs, access and licensing, computing requirements, local label quality, transferability between cities, and the availability of remote or field validation. A method that works well for one labelled dataset should not be assumed to work equally well elsewhere.
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