Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Naveen; Somanathan, Chidambaram; Fowdur, Tulsi Pawan; Jogee, Deejaysing; Cowlessur, Maneeraj Title A name given to the resource Deep Learning Analysis of Satellite Images for UN SDG Monitoring in Mauritius' Black River District Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2025 - Conference Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICARES67579.2025.11371462" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICARES67579.2025.11371462</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034855700?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034855700?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar N., ECE Deptt, CHRIST University, RF &amp; 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 Description An account of the resource 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. Subject The topic of the resource Classification; Deep learning; Earth observation; Mauritius; UN SDGs Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155707-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online