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Evaluating green cover and open spaces in informal settlements of Mumbai using deep learning

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Abstract

Haphazard urbanization has resulted in development of unplanned informal settlements, also called slums. Citizens residing in these dense settlements are generally deprived of essential sustainable drivers, i.e., green cover and open spaces (GOS). Such limitations can have an adverse impact on various aspects such as physiological, economical, health, and quality of life. Much research has been done on applying Machine Learning (ML) techniques for identifying slums at the city scale. However, no study explicitly addressed the problem of GOS extraction inside the informal settlements. This paper aims to fill this gap by training three modified Convolution Neural Network (CNN) models, i.e., VGG16-UNet, MobileNetV2-UNet, DeepLabV3 + on manually labeled high-resolution satellite imagery for the city of Mumbai. All three models performed excellently and achieved the state-of-the art overall accuracy of around 95%, for the considered classes. Out of the three models, VGG16-UNet performed the best and hence was applied to the whole city for developing green, open, and green and open indices. The indices were further used to establish a quantitative relation between various built-up typologies and GOS. It was found that informal settlements have significantly less green and open space than other built-up typologies. Stark GOS difference between slums and its neighborhood planned residential was observed, and hence, requires urgent attention of the city managers. The outcome presented in the paper can be used as input to various decision-making systems concerning better informal space planning to better plan the GOS, which eventually can lead to development of sustainable cities.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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  1. Indian Institute of Science Education and Research, Bhopal, India

    Ayush Dabra & Vaibhav Kumar

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  1. Ayush Dabra
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Correspondence to Vaibhav Kumar.

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The study was approved by the Ethics committee of the Indian Institute of Science Education and Research Bhopal, India.

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Dabra, A., Kumar, V. Evaluating green cover and open spaces in informal settlements of Mumbai using deep learning. Neural Comput & Applic 35, 11773–11788 (2023). https://doi.org/10.1007/s00521-023-08320-7

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