Work

Uncertainty aware slum mapping in 55 heterogeneous cities

Year: 2025

Type: article

Abstract: Slums are densely populated urban areas characterized by substandard housing and squalor. These areas often lack basic infrastructure and services, making them challenging to manage and improve. Mapping slums on a large scale is particularly difficult due to their complex, dynamic, and non-uniform nature and the scarcity of available data. This study employs deep learning and uncertainty estimation to map slum areas in 55 diverse cities. We tackle the issues of scarce labels, noisy labels, and class imbalance to produce reliable slum probability maps. Applied to a large dataset from the Global South, our method approximates probability values to each image, yielding a more granular view of slum morphology within each city. We achieve this by combining Monte Carlo sampling through test-time augmentation, test-time dropout on overlapping image tiles, and an ensemble of four CNNs. The resulting probability maps not only quantify prediction uncertainty but also reveal morphological patterns of slum settlements. Our findings underscore the diversity and complexity of slums, contributing to a more complete understanding of these environments. A key result is our large-scale workflow, which, despite limited data, groups predictions by probability into multiple slum categories defined by their morphological traits. This approach offers a substantial improvement over traditional binary slum classification methods that focus solely on typical slum morphologies. • Using deep learning to map slums in 55 cities with limited data. • Uncertainty approximation using test-time augmentations, dropout and model ensembles. • Large scale slum dataset: First to use coherent methods and probability estimates for slum mapping. • Maps reveal different slum morphologies, offering crucial insights for urban planning.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104979

Authors: Thomas Stark, Michael Wurm, Henri Debray, Xiao Xiang Zhu, Hannes Taubenböck

Institutions: Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Technical University of Munich, University of Würzburg, Munich Center for Machine Learning

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Reduced inequalities

Open Access status: gold

Estimated APC paid: $3,550