Deep Learning Analysis of Satellite Images for UN SDG Monitoring in Mauritius' Black River District
subject: Classification; Deep learning; Earth observation; Mauritius; UN SDGs
description: This paper proposes an integrated strategy to analyse the progress of selected United Nations Sustainable Development Goals (SDGs 1, 2, and 13) using Earth Observation (EO) data and deep learning (DL) based classification. The research focuses on Mauritius's Black River district, which is facing growing urbanization, agricultural land demand, forest conservation needs, and land degradation. These challenges are closely related to the reduction of poverty (SDG 1) through settlement monitoring, food security (SDG 2) through green farmland analysis, and climate action (SDG 13) through forest cover and bare land tracking. High-resolution satellite images from the Satellogic constellation were pre-processed, classified, and mapped to the SDGs' key land cover categories. A convolutional neural network (CNN) model was trained to distinguish city structures, agriculture farmland, forest land, and barren land, with up to 99% overall precision. DL-based image analysis has the ability to monitor the UN SDGs in specific regions and provide actionable information for the sustainable development plans of small island governments. 2025 IEEE.
creator: Kumar, Naveen; Somanathan, Chidambaram; Fowdur, Tulsi Pawan; Jogee, Deejaysing; Cowlessur, Maneeraj
source: 2025 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2025 - Conference Proceedings;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155707-2;
language: English
type: Conference paper
coverage: Kumar N., ECE Deptt, CHRIST University, RF & Microwave Research Lab, Bangalore, India; Somanathan C., CHRIST University, ECE Department, Bangalore, India; Fowdur T.P., University of Mauritius, Faculty of Engineering, EEE Department, Reduit, Mauritius; Jogee D., University of Mauritius, Civil Engineering, Faculty of Engineering, Reduit, Mauritius; Cowlessur M., Government of Mauritius, Civil Engineering MNICD, Mauritius