Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover
By: Naman Srivastava , Joel D Joy , Yash Dixit and more
Potential Business Impact:
Maps cities to plan growth and save nature.
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.
Similar Papers
Learning with less: label-efficient land cover classification at very high spatial resolution using self-supervised deep learning
CV and Pattern Recognition
Maps land from space with less data.
Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji
CV and Pattern Recognition
Tracks city growth in Fiji from space.
Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior
CV and Pattern Recognition
Maps cities by looking at street pictures.