Satellites track settlement expansion by comparing images of the same places taken on different dates, then identifying where buildings or other built-up patterns appear to have changed. Those maps show physical development—not whether residents have legal tenure, adequate services, or any particular needs. To identify informal settlements responsibly, analysts must combine image-based signals with locally defined criteria and human validation.
What satellite images can—and cannot—tell you
Repeated Earth-observation images can reveal where built structures have appeared, spread, or changed over time. Analysts may turn those observations into maps of built-up areas, change indicators, or probabilities that a location merits closer review.
An image does not directly establish that a place is an informal settlement. Roof density or a model’s classification cannot show tenure, household circumstances, or service access. The UN’s indicator methodology notes that informal housing may be missing from official statistics and that definitions—and the changing nature of informal settlements—can make trend analysis difficult. A locally appropriate definition is essential before mapping begins.
How analysts monitor change
- Define the question and area. Decide whether the goal is to map built-up expansion, identify candidate informal areas, or monitor a locally defined indicator. Record the boundary and the operational definition being used.
- Gather images from multiple dates. Optical imagery such as Sentinel-2 can show surface characteristics, while Sentinel-1 radar offers another observation type. UN-Habitat’s Earth Observation Toolkit documents urban-monitoring uses of Earth observation, including Landsat analysis of built-up growth. The European Commission Joint Research Centre (JRC) describes public, open, and free access to Copernicus Sentinel-1 radar data and a 2020 Global Human Settlement Layer Sentinel-2 built-up grid at 10 m spatial resolution. Check current release metadata and access terms before using a dataset.
- Extract candidate built-up signals. Depending on the imagery and method, analysts classify spectral values, derived indices, texture patterns, or radar responses. They compare the resulting built-up classifications across dates to locate possible changes.
- Make a map for review, not an automatic verdict. A classification can produce candidate areas or probability maps for people to examine. In a Colombia case study, researchers applied machine learning to Sentinel-2 time series and used GIS review to prioritize potential settlement locations.
- Check the candidates. Review historical, higher-resolution imagery and, when appropriate, compare the map with field observations and community knowledge. The Colombia study combined remote review of historical imagery with field checks through a contributor platform.
- Report what the map measures. State the image sources and dates, the definition and method, and the validation performed. Keep observed built-up change separate from any inference about settlement status.
How can you tell when a settlement is growing?
Look for a consistent change in built-up patterns across comparable images: new structures, expansion at the edge of an existing built area, or denser development. A single image is a snapshot; comparison over time is what reveals change. Even then, the result is a candidate signal, not proof that the area is informal or that every apparent change is construction.
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Errors can arise when clouds obscure optical images or when land-cover changes resemble buildings. In the Colombia study, cloud cover, vegetation becoming bare land, and vegetation or bare land becoming formal settlement generated false positives. The researchers found that biennial aggregation reduced cloud-related false positives in their setting and suggested texture features might help distinguish informal areas from bare land and formal settlement. These findings are specific to that study, not a universal rule.
Why resolution, method, and local validation matter
Mapping results depend on the imagery, time span, local conditions, training labels, and classification method. A JRC-described Sentinel-2 built-up grid has 10 m spatial resolution; that figure describes the product, not a guarantee that individual buildings can be separated in every location.
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One 2021 study by Agatha C. H. de Mattos, Gavin McArdle, and Michela Bertolotto compared classification methods on a labelled Sentinel-2 dataset covering four cities. For its grey-level co-occurrence texture features, it 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. The authors concluded that “the grey-level co-occurrence matrix performed better than multi-spectral data for all four cities.” These are results from that four-city evaluation, not expected performance for other places or datasets. See the 2021 study.
For a practical comparison of approaches, consider:
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- Resolution: whether the product can distinguish the scale of change the project needs to observe.
- Time coverage and repeat observations: whether usable images exist for the dates being compared.
- Sensor type: optical imagery and radar respond differently to conditions such as cloud cover.
- Labels and local fit: training examples and definitions from one city may not transfer cleanly to another.
- Validation: whether historical imagery, field observations, and community knowledge can check candidate areas.
- Practical constraints: imagery licensing or cost, computing needs, and staff capacity to review outputs.
What published examples show
In Colombia, Tingzon and colleagues used Sentinel-2 time series to identify potential Venezuelan migrant settlements emerging between 2015 and 2020. Their workflow included machine-learning probability maps, historical high-resolution image review, and field validation. The work illustrates how satellite analysis can narrow the search for places to inspect; it does not establish a universal method or accuracy level. Read the Colombia case study.
For a longer view of urban development, UN-Habitat’s toolkit reports that the UAE Federal Competitiveness and Statistics Centre extracted built-up areas from Landsat imagery spanning 1972–2020. The toolkit says the analysis showed growth patterns in roads, infrastructure, and buildings nationally and by emirate. This is an example of monitoring built-up change, not a global estimate of informal-settlement expansion.
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Use settlement maps with care
Maps can influence planning, services, recognition, and perceptions of residents. Richard Sliuzas, an urban planner and Associate Professor at the University of Twente, describes slum mapping as a process with socio-political dimensions: map categories, production methods, and access to spatial information reflect choices about urban development and residents’ entitlements. UN-Habitat’s lecture synopsis recommends treating mapping as socio-technical and involving settlement communities alongside public, private, and civil actors.
In practice, explain who chose the categories, who can see and use the data, and how residents can correct or contest an interpretation. Keep candidate classifications clearly identified as such, and do not present a physical change map as evidence of legal status or household needs.
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Is there a global rate of informal-settlement expansion?
The cited sources do not establish a robust, current global statistic for the rate at which informal settlements are expanding. Local and national image analyses can document built-up change for defined places and dates, but those findings should not be generalized into a worldwide growth rate.
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