association-rosia/flair-2
Engage in a semantic segmentation challenge for land cover description using multimodal remote sensing earth observation data, delving into real-world scenarios with a dataset comprising 70,000+ aerial imagery patches and 50,000 Sentinel-2 satellite acquisitions.
This project helps environmental scientists and land use planners analyze satellite and aerial imagery to understand land cover. It takes in multimodal remote sensing earth observation data, including aerial photos and Sentinel-2 satellite acquisitions, and outputs detailed maps with different land cover types clearly segmented. Anyone who needs to identify and map land features from satellite images, such as for urban planning or ecological monitoring, would use this.
No commits in the last 6 months.
Use this if you need to accurately classify and map different land cover types from diverse aerial and satellite imagery.
Not ideal if your primary need is not land cover mapping but rather object detection or change detection within a single image type.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 28, 2024
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