AnkurDeria/MFT
Pytorch implementation of Multimodal Fusion Transformer for Remote Sensing Image Classification.
This project helps remote sensing analysts accurately identify and map land cover types from satellite or aerial imagery. It takes various types of remote sensing data, such as hyperspectral, LiDAR, or SAR images, and processes them to output classified maps of areas like urban regions, vegetation, or water bodies. Geoscientists, urban planners, and environmental researchers who work with satellite imagery would use this.
237 stars. No commits in the last 6 months.
Use this if you need to classify diverse land cover types using a combination of different remote sensing data sources like hyperspectral, LiDAR, and SAR images.
Not ideal if you only work with single-source remote sensing data or require real-time classification for highly dynamic environments.
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Dec 23, 2023
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