Deep Learning for Slum Mapping in Remote Sensing Images: A Meta-analysis and Review

TL;DR

This paper reviews the use of deep learning techniques, especially CNNs, for mapping slums in remote sensing images, highlighting recent advancements, challenges, and the need for context-specific models to improve accuracy and explainability.

Contribution

It provides a comprehensive meta-analysis of deep learning methods for slum detection in remote sensing imagery from 2014 to 2024, emphasizing trends, challenges, and future directions.

Findings

Deep learning, especially CNNs, has significantly improved slum detection accuracy.

Advancements in data preprocessing and model training enhance performance.

No universal model exists; context-specific approaches are necessary.

Abstract

The major Sustainable Development Goals (SDG) 2030, set by the United Nations Development Program (UNDP), include sustainable cities and communities, no poverty, and reduced inequalities. However, millions of people live in slums or informal settlements with poor living conditions in many major cities around the world, especially in less developed countries. To emancipate these settlements and their inhabitants through government intervention, accurate data about slum location and extent is required. While ground survey data is the most reliable, such surveys are costly and time-consuming. An alternative is remotely sensed data obtained from very high-resolution (VHR) imagery. With the advancement of new technology, remote sensing based mapping of slums has emerged as a prominent research area. The parallel rise of Artificial Intelligence, especially Deep Learning has added a new dimension to this field as it allows automated analysis of satellite imagery to identify complex spatial patterns associated with slums. This article offers a detailed review and meta-analysis of research on slum mapping using remote sensing imagery from 2014 to 2024, with a special focus on deep learning approaches. Our analysis reveals a trend towards increasingly complex neural network architectures, with advancements in data preprocessing and model training techniques significantly enhancing slum identification accuracy. We have attempted to identify key methodologies that are effective across diverse geographic contexts. While acknowledging the transformative impact Convolutional Neural Networks (CNNs) in slum detection, our review underscores the absence of a universally optimal model, suggesting the need for context-specific adaptations. We also identify prevailing challenges in this field, such as data limitations and a lack of model explainability and suggest potential strategies for overcoming these.