Cross Pseudo Supervision Framework for Sparsely Labelled Geospatial Images
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916355040608256 |
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| author | Dixit, Yash Srivastava, Naman Joy, Joel D Olikara, Rohan E, Swarup Ramesh, Rakshit |
| author_facet | Dixit, Yash Srivastava, Naman Joy, Joel D Olikara, Rohan E, Swarup Ramesh, Rakshit |
| contents | Land Use Land Cover (LULC) mapping is a vital tool for urban and resource planning, playing a key role in the development of innovative and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-resolution satellite images with a vast diversity of data distributions in different areas of India. Our approach ensures a robust generalization across different types of buildings, roads, trees, and water bodies within these distinct areas. We propose a modified Cross Pseudo Supervision framework to train image segmentation models on sparsely labelled data. The proposed framework addresses the limitations of the famous 'Cross Pseudo Supervision' technique for semi-supervised learning, specifically tackling the challenges of training segmentation models on noisy satellite image data with sparse and inaccurate labels. This comprehensive approach significantly enhances the accuracy and utility of LULC mapping, providing valuable insights for urban and resource planning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02382 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross Pseudo Supervision Framework for Sparsely Labelled Geospatial Images Dixit, Yash Srivastava, Naman Joy, Joel D Olikara, Rohan E, Swarup Ramesh, Rakshit Computer Vision and Pattern Recognition Land Use Land Cover (LULC) mapping is a vital tool for urban and resource planning, playing a key role in the development of innovative and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-resolution satellite images with a vast diversity of data distributions in different areas of India. Our approach ensures a robust generalization across different types of buildings, roads, trees, and water bodies within these distinct areas. We propose a modified Cross Pseudo Supervision framework to train image segmentation models on sparsely labelled data. The proposed framework addresses the limitations of the famous 'Cross Pseudo Supervision' technique for semi-supervised learning, specifically tackling the challenges of training segmentation models on noisy satellite image data with sparse and inaccurate labels. This comprehensive approach significantly enhances the accuracy and utility of LULC mapping, providing valuable insights for urban and resource planning applications. |
| title | Cross Pseudo Supervision Framework for Sparsely Labelled Geospatial Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.02382 |