Summary of Allclear: a Comprehensive Dataset and Benchmark For Cloud Removal in Satellite Imagery, by Hangyu Zhou et al.
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
by Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo, Utkarsh Mall, Bharath Hariharan, Kavita Bala
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a large public dataset for cloud removal from satellite imagery, called AllClear. The dataset features 23,742 regions of interest with diverse land-use patterns, comprising over 4 million images in total. It includes multi-spectral optical imagery, synthetic aperture radar imagery, and auxiliary remote sensing products. The effectiveness of the dataset is validated by benchmarking performance, demonstrating a scaling law for PSNR as more data is added, and conducting ablation studies on temporal length and individual modalities. This comprehensive dataset aims to promote better cloud removal results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Clouds in satellite images make it hard for computers to understand what’s on the ground. To help with this problem, scientists created a huge collection of cloud-free pictures called AllClear. It has over 4 million images from different parts of the world and uses multiple types of data like optical and radar images. The team tested their dataset and found that using more data makes it better at removing clouds. This will help computers understand what’s happening on our planet better. |
