Mapping Informal Settlements with Low‑Resolution Data: Challenges, Techniques, and Innovations

Informal settlements, often referred to as slums or shantytowns, are home to over a billion people worldwide. These areas are typically characterized by inadequate infrastructure, poor access to basic services, and precarious housing conditions. Despite their significant social and economic impact, informal settlements are frequently underrepresented in official statistics and maps, which hampers efforts for inclusive urban development and disaster management.

One of the main challenges in mapping informal settlements is the scarcity of high-resolution satellite imagery or detailed ground data, especially in low-income regions. However, advancements in data science and machine learning have opened new avenues to extract meaningful insights from low-resolution data sources. In this blog, we explore how low-resolution satellite data, combined with innovative mapping techniques, is being used to identify and monitor informal settlements across the globe.

Why Mapping Informal Settlements Matters

Mapping informal settlements is crucial for:

• Urban Planning & Infrastructure Development: Understanding where these communities are located helps governments plan better roads, water, sanitation, and healthcare services.
• Disaster Risk Management: Informal settlements are often in high-risk zones (floodplains, unstable slopes). Accurate maps aid in early warning systems and evacuation planning.
• Policy & Inclusion: Data-driven evidence can influence policy decisions and ensure these communities are not left out of developmental initiatives.

However, high-resolution satellite imagery (sub-meter accuracy) is expensive, often unavailable for large-scale mapping, and updated infrequently. This creates a need to utilize low-resolution, freely available data sources effectively.

Challenges of Using Low‑Resolution Data

Low-resolution imagery (typically 10–30m per pixel, such as from Sentinel-2 or Landsat satellites) presents several challenges:

1. Mixed Pixels (Spectral Confusion): One pixel might contain a mix of roofs, vegetation, roads, and open spaces.
2. Visual Similarity to Surroundings: Informal settlements often blend into peri-urban areas or dense vegetation in low-res images.
3. Temporal Variability: Informal settlements can expand rapidly, while satellite images may be months or years old.
4. Lack of Ground Truth Data: Validating low-resolution mapping efforts requires ground surveys, which are limited or non-existent in many regions.

Despite these hurdles, researchers and organizations have developed innovative methods to leverage low-res data effectively.

Techniques to Map Informal Settlements with Low‑Resolution Data

1. Spectral Signature Analysis

Even at low resolution, informal settlements exhibit unique spectral signatures. For example:

• Corrugated metal roofs reflect light differently compared to vegetation or concrete.
• Bare soil patches (common in informal areas) can also be detected via multispectral bands.

By training classification algorithms (like Random Forests or Support Vector Machines) on known spectral signatures, large areas can be scanned to flag potential informal settlements.

2. Texture and Morphological Features

Informal settlements have distinct textures:

• Irregular patterns of small, dense structures.
• Lack of organized street grids.

Texture filters (e.g., GLCM — Grey Level Co-occurrence Matrix) can extract these patterns even from low-res images, improving classification accuracy.

3. Time Series Analysis

Low-res satellites like Landsat and Sentinel have archives spanning decades. By analyzing how land cover changes over time, sudden urban expansion into undeveloped areas can hint at the formation of informal settlements.

Temporal metrics such as NDVI (Normalized Difference Vegetation Index) drop-offs can be proxies for land occupation.

4. Integrating Ancillary Data Sources

Combining low-res imagery with:

• Night-time Light Data (e.g., VIIRS, DMSP-OLS): Presence of low-intensity lights often correlates with informal settlements.
• OpenStreetMap Data: Community-contributed data can provide rough outlines.
• Population Density Grids (WorldPop, HRSL): Helps in pinpointing densely populated, unmapped areas.

5. Deep Learning with Weak Labels

Recent advances in AI allow for semi-supervised learning where high-resolution labeled data from small sample areas is used to train models on low-resolution, unlabeled regions.
Approaches like:

• Convolutional Neural Networks (CNNs) with patch-level classification.
• Transfer Learning from pre-trained urban land use models.

Case Studies & Real-World Applications

– Google’s “Open Buildings” Project

Using satellite data and AI, Google has created an open-source dataset of building footprints across Africa. While primarily trained on high-res data, the methodology is applicable to refining low-res mapping.

– UN Habitat & Humanitarian OpenStreetMap

Crowdsourcing validation for low-res detected informal settlements by involving local communities and NGOs.

– Global Human Settlement Layer (GHSL) by the EU

Provides built-up area layers from Landsat imagery, which indirectly highlight informal settlement expansions.

The Road Ahead: Opportunities & Innovations

While low-resolution data alone is not a silver bullet, it can be a powerful first-pass tool for:

• Prioritizing areas for field surveys.
• Providing up-to-date maps in data-scarce regions.
• Supporting rapid assessments after disasters.

Future opportunities include:

• Fusion of multiple low-res datasets (optical, radar, LiDAR proxies).
• Better AI models that adapt to region-specific informal settlement patterns.
• Real-time monitoring platforms for NGOs and governments.

Mapping informal settlements with low-resolution data is a testament to doing more with less. Through creative methodologies and cross-sector collaboration, it’s possible to bridge the data gap and bring these often invisible communities into the global development dialogue.

While challenges remain, the combination of open data, AI, and community participation is paving the way for more inclusive, equitable urban planning — even in the world’s most under-mapped regions.

Case Study: Mapping Colonias in Texas, USA with Low-Resolution Data

Background: What are Colonias?

“Colonias” are unincorporated, low-income communities located along the U.S.–Mexico border, predominantly in Texas, New Mexico, Arizona, and California. These settlements often lack basic infrastructure such as potable water, sewage systems, and safe housing. In Texas alone, there are an estimated 2,000+ colonias, home to over 500,000 residents.

Despite being within a developed country, colonias face chronic underrepresentation in official datasets and urban planning initiatives. Mapping these areas is essential for directing infrastructure development, health programs, and disaster preparedness.

The Challenge of Mapping Colonias

Unlike urban slums in megacities, colonias are:

• Scattered over a vast, rural landscape.
• Small in size, often comprising clusters of 10–100 households.
• Rapidly evolving, with new developments occurring informally.

High-resolution commercial satellite imagery is not always available or updated frequently enough. This necessitates creative methods using low-resolution, publicly available satellite data, such as Sentinel-2 (10m resolution) or Landsat 8 (30m resolution).

Approach: Using Low-Resolution Data to Detect Colonias

1. Spectral & Land Cover Analysis

• Colonias often exhibit a “bare-earth” signature due to the lack of paved roads and yards.
• Sentinel-2’s Red Edge and Shortwave Infrared (SWIR) bands were analyzed to differentiate:
  • Exposed soil surfaces (indicative of unpaved lots).
  • Sparse vegetation patterns compared to adjacent farmlands or urban areas.

2. Morphological Features & Settlement Patterns

• Colonias have irregular, sparse layouts compared to structured suburban neighborhoods.
• Using GLCM (Grey Level Co-occurrence Matrix) texture metrics, small patches of dense but disorganized structures were highlighted.

3. Temporal Change Detection

• By analyzing Landsat time-series data from 2000 to 2020, areas of sudden land-use change (from scrubland to built-up) were flagged as potential colonia expansions.

4. Ancillary Data Integration

• Night-time Light Data (VIIRS, 500m resolution):
  • Weak but detectable light emissions in rural settings correlate with inhabited colonias.
• OpenStreetMap (OSM):
  • Mapped roads and user-contributed points of interest helped refine detection.
• County-Level Parcel Data:
  • Used to mask out officially developed subdivisions and focus on unregistered land parcels.

Results: Detecting Colonias in Hidalgo & Starr Counties, Texas

By integrating these methods:

• Over 85% of known colonias were successfully identified using Sentinel-2 data.
• Additionally, 30+ previously unmapped clusters showed characteristics matching informal settlement patterns, warranting field validation.

The methodology proved especially effective in distinguishing colonias from nearby agricultural worker housing or RV parks, which can visually resemble informal settlements in low-res imagery.

Limitations & Challenges

• Pixel mixing remained a challenge in smaller colonias with fewer structures.
• Differentiating colonias from peri-urban sprawl required manual checks.
• Cloud cover in historical Landsat imagery occasionally reduced time-series continuity.

Impact & Use Cases

• Local governments used the derived colonia maps to prioritize areas for infrastructure upgrades.
• Public health agencies identified target zones for sanitation and disease prevention programs.
• Disaster management teams integrated the colonia maps into flood risk assessments.

Key Takeaways

• Even in a high-income country like the U.S., low-resolution data combined with smart analytics can effectively bridge gaps in mapping underrepresented communities.
• The Texas Colonias case illustrates that contextual understanding of settlement patterns is as crucial as data resolution in informal settlement detection.

Next Steps

1. Field verification and ground-truth data collection with community organizations.
2. Automating the detection pipeline to monitor colonia expansions in near real-time.
3. Scaling the methodology to other U.S.–Mexico border states.