Summary of Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation For Global Solar Mapping, by Vishal Batchu et al.
Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping
by Vishal Batchu, Alex Wilson, Betty Peng, Carl Elkin, Umangi Jain, Christopher Van Arsdale, Ross Goroshin, Varun Gulshan
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed paper aims to improve Google’s Solar API by expanding its reach using satellite imagery, enabling global solar potential assessment. The current API relies on aerial imagery, which limits its geographical coverage. To tackle this challenge, the authors propose building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Their approach involves training models on aligned satellite and aerial datasets to produce 25cm DSMs and roof segments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Solar API estimates solar potential from aerial imagery, but its impact is constrained by geographical coverage. The proposed paper expands the API’s reach using satellite imagery, enabling global solar potential assessment. The authors use deep learning models to build a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views. |
Keywords
» Artificial intelligence » Deep learning » Instance segmentation
