Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover

TL;DR

This paper compares advanced remote sensing land use land cover mapping techniques, including atmospheric correction and supervised learning models, demonstrating their effectiveness in urban change detection and planning.

Contribution

It introduces a comprehensive evaluation of LUT-based atmospheric correction combined with deep learning models like DeeplabV3+ and CPS for LULC mapping.

Findings

CPS with dynamic weighting improves pseudo-label accuracy

Significant land use changes identified in Hyderabad case study

Enhanced LULC mapping accuracy for urban planning applications

Abstract

Land Use Land Cover (LULC) mapping is essential for urban and resource planning, and is one of the key elements in developing smart and sustainable cities.This study evaluates advanced LULC mapping techniques, focusing on Look-Up Table (LUT)-based Atmospheric Correction applied to Cartosat Multispectral (MX) sensor images, followed by supervised and semi-supervised learning models for LULC prediction. We explore DeeplabV3+ and Cross-Pseudo Supervision (CPS). The CPS model is further refined with dynamic weighting, enhancing pseudo-label reliability during training. This comprehensive approach analyses the accuracy and utility of LULC mapping techniques for various urban planning applications. A case study of Hyderabad, India, illustrates significant land use changes due to rapid urbanization. By analyzing Cartosat MX images over time, we highlight shifts such as urban sprawl, shrinking green spaces, and expanding industrial areas. This demonstrates the practical utility of these techniques for urban planners and policymakers.