JoshuaBillson/Waterbody-Detection-Via-Deep-Learning
Source code for the paper, "Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation".
This project helps environmental scientists, urban planners, and GIS specialists accurately map water bodies from satellite images. By feeding Sentinel-2 multispectral imagery into the system, you get a precise, pixel-by-pixel map identifying lakes, rivers, and other water features. This is ideal for monitoring changes in water resources or assessing flood risk.
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Use this if you need to precisely identify and map water bodies from Sentinel-2 satellite images for environmental monitoring, hydrological studies, or land use planning.
Not ideal if you need to classify other land cover types, work with different satellite data sources (e.g., Landsat), or require real-time water detection.
Stars
19
Forks
3
Language
Python
License
MIT
Category
Last pushed
Mar 09, 2023
Commits (30d)
0
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