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Mapping river corridors remains challengin … Mapping river corridors remains challenging due to the dynamic interactions between water, sediment, and vegetation. Existing land cover maps often misclassify fluvial sediments, limiting their use in river system studies. We present a deep learning framework using incremental learning to refine river corridor mapping by integrating Sentinel-2 imagery with global land cover datasets (ESRI, Google Dynamic World, ESA WorldCover). Our method builds on existing classifications to improve differentiation between fluvial sediment, bare ground, and mining-related disturbances. The results show that incremental learning can enhance river mapping accuracy, providing a customizable approach to better capture riverine landscapes.ach to better capture riverine landscapes. +
Customizing River Corridor Segmentation from Satellite Imagery Using Deep Learning +
Edinburgh +
United Kingdom +
University of Edinburgh +
qiuyangschen@gmail.com +
Qiuyang +
University of Edinburgh +
Chen +
07422924390 +
Image: +
4) Get lazy with LLMs +
2) From Exploration to Publication: Geospatial Research in the Jupyter Ecosystem +
1) Landlab’s NetworkSedimentTransporter: A Lagrangian Model for River Bed Material Transport Dynamics +
United Kingdom +
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14:23:12, 18 March 2025 +
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14:25:48, 18 March 2025 +
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