Summary of Cabuar: California Burned Areas Dataset For Delineation, by Daniele Rege Cambrin et al.
CaBuAr: California Burned Areas dataset for delineation
by Daniele Rege Cambrin, Luca Colomba, Paolo Garza
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel open dataset for burned area delineation using satellite imagery. The dataset consists of pre- and post-fire Sentinel-2 L2A acquisitions from California forest fires starting in 2015, along with raster annotations generated from data released by the California Department of Forestry and Fire Protection. To facilitate the development of tools for automatic identification of burned areas, three baselines are also provided: spectral indexes analyses, SegFormer, and U-Net models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to stop forest wildfires by creating a new tool that uses satellite pictures. The tool will help people quickly find out which parts of the forest were damaged in a fire. This is important because fires can release a lot of bad things into the air and also cause landslides. The tool uses special computer vision techniques and lots of information from satellites to make it work. It’s like a big puzzle, and this paper helps people solve that puzzle by providing a new dataset with pictures taken before and after the fire. |
