toelt-llc/cloud_segmentation_comparative
Officiel implementation of the paper "BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery"
This project helps remote sensing professionals accurately identify and outline clouds in satellite and aerial imagery. It takes raw satellite images as input and outputs processed images with clouds clearly segmented. Researchers, meteorologists, urban planners, and environmental analysts can use this to get clean, cloud-free data for their projects.
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Use this if you need to reliably remove or analyze cloud cover from satellite images for environmental monitoring, weather forecasting, or land use mapping.
Not ideal if your primary goal is general object detection in natural scene images, as this tool is specifically optimized for cloud segmentation in remote sensing imagery.
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Last pushed
Aug 03, 2024
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