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

arXiv 2507.18099 ↗First posted 24 Jul 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 38 references that are in the map. First posted on arXiv 24 Jul 2025.

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References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  3. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 463
  4. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  5. 2019Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networkscited by 116
  6. 2021Continental-Scale Building Detection from High Resolution Satellite Imagerycited by 50
  7. 2022TINYCD: A (Not So) Deep Learning Model For Change Detectioncited by 36
  8. 2023Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentationcited by 35
  9. 2020Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Studycited by 28
  10. 2023DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentationcited by 25
  11. 2020Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunitiescited by 15
  12. 2021A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagerycited by 9
4 more
  1. 2022Fast building segmentation from satellite imagery and few local labelscited by 9
  2. 2019Weakly Supervised Semantic Segmentation of Satellite Imagescited by 8
  3. 2024Cross Pseudo Supervision Framework for Sparsely Labelled Geospatial Imagescited by 2
  4. 2024An Atmospheric Correction Integrated LULC Segmentation Model for High-Resolution Satellite Imagerycited by 1

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