WangLibo1995/GeoSeg
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.
This project helps urban planners, environmental scientists, and GIS analysts automatically identify and map different features in satellite, aerial, and drone imagery. By inputting raw remote sensing images, it outputs detailed pixel-level segmentation masks that categorize elements like buildings, roads, vegetation, and water bodies. This allows for efficient analysis of urban scenes and environmental changes.
1,046 stars. No commits in the last 6 months.
Use this if you need to accurately segment and classify objects within large remote sensing images from satellites, airplanes, or UAVs, especially for urban planning or environmental monitoring.
Not ideal if your primary need is general-purpose image segmentation for common photographic images or non-geospatial applications.
Stars
1,046
Forks
150
Language
Python
License
GPL-3.0
Category
Last pushed
Aug 19, 2024
Commits (30d)
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