Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks

Abstract

In the cities of the Global South, slum settlements are growing in size and number, but their locations and characteristics are often missing in official statistics and maps. Although several studies have focused on detecting slums from satellite images, only a few captured their variations. This study addresses this gap using an integrated approach that can identify a slums’ degree of deprivation in terms of socio-economic variability in Bangalore, India using image features derived from very high resolution (VHR) satellite images. To characterize deprivation, we use multiple correspondence analysis (MCA) and quantify deprivation with a data-driven index of multiple deprivation (DIMD). We take advantage of spatial features learned by a convolutional neural network (CNN) from VHR satellite images to predict the DIMD. To deal with a small training dataset of only 121 samples with known DIMD values, insufficient to train a deep CNN, we conduct a two-step transfer learning approach using 1461 delineated slum boundaries as follows. First, a CNN is trained using these samples to classify slums and formal areas. The trained network is then fine-tuned using the 121 samples to directly predict the DIMD. The best prediction is obtained by using an ensemble non-linear regression model, combining the results of the CNN and models based on hand-crafted and geographic information system (GIS) features, with R2 of 0.75. Our findings show that using the proposed two-step transfer learning approach, a deep CNN can be trained with a limited number of samples to predict the slums’ degree of deprivation. This demonstrates that the CNN-based approach can capture variations of deprivation in VHR images, providing a comprehensive understanding of the socio-economic situation of slums in Bangalore.

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

“…Semi-automatic "supervised" imagery classification is commonly performed on satellite, aerial, and drone imagery, using machine-learning and statistical models. Developments in this field show that well-trained models can achieve a high classification accuracy of more than 90% [61,66]. However, such methods, and more particularly deep learning methods, typically require a large number of high-quality training data.…”

“…Given resources, computer-based models can also be updated frequently with the most recent imagery. In principle, this approach to deprived area modeling can be performed either as a categorical task (e.g., deprived/nondeprived binary classes) or a continuous task (e.g., "deprivation" index [61]), providing a continuous probability for small units within the area of interest.…”

“…The aggregated maps will also have a model error as compared to detailed community-based maps. To deal with privacy and model errors, the different information needs at city and national scales, and the different types and resolution of physical and social datasets used as model covariates, the proposed IDeAMapS is meant to produce a high-resolution gridded dataset, where each grid cell is characterized by an estimated degree of "deprivation" (relating to differences in physical, environmental, and socioeconomic conditions (e.g., [61] To deal with privacy and model errors, the different information needs at city and national scales, and the different types and resolution of physical and social datasets used as model covariates, the proposed IDeAMapS is meant to produce a high-resolution gridded dataset, where each grid cell is characterized by an estimated degree of "deprivation" (relating to differences in physical, environmental, and socioeconomic conditions (e.g., [61]). Gridded datasets offer high operational flexibility, because they can be aggregated within spatial or statistical units of different extents and scales, such as census enumeration areas or administrative units.…”

“…While the body of EO literature about deprived area mapping is rapidly increasing (e.g., [50,51,[53][54][55][56][57][58][59][60][61][62][63][64][65]), several challenges in this area still exist. For example, most studies are not addressing global information needs (e.g., producing data in support of SDG 11 [66]), with most deprived area mapping approaches mainly focusing on small areas below the city scale and for very specific sites.…”

Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks

“…CNNs and FCNs have been recently used for land cover and land use classification (LCLU). For instance, in [14], CNNs are applied to identify slums' degree of deprivation, considering different levels of deprivation including socio-economical aspects. FCN applications include detection of cadastral boundaries [15] and delineation of agricultural fields in smallholder farms [4].…”