Summary of Tracking Progress Towards Sustainable Development Goal 6 Using Satellite Imagery, by Othmane Echchabi et al.
Tracking Progress Towards Sustainable Development Goal 6 Using Satellite Imagery
by Othmane Echchabi, Nizar Talty, Josh Manto, Aya Lahlou, Ka Leung Lam
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computers and Society (cs.CY); 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 A novel study combines Afrobarometer survey data, satellite imagery, and deep learning techniques to develop a framework for evaluating access to piped water and sewage systems across diverse African regions. The model utilizes Meta’s DINO model and achieves high accuracy, with over 96% and 97% accuracy in identifying areas with piped water and sewage system access respectively using Landsat 8 and Sentinel-2 satellite imagery. This framework can serve as a screening tool for policymakers to identify regions requiring targeted efforts to improve water and sanitation infrastructure, with potential applications in estimating national-level percentages of the population with access to these services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special data sources and computer models to help countries figure out who has access to clean water and toilets. It combines surveys from people in Africa with pictures taken by satellites, which helps create a map showing where people have these basic needs met. The model is very good at guessing when someone has access to clean water or sewers. This can be helpful for leaders who want to make sure everyone has what they need. Maybe one day it will even help us measure progress towards other important goals. |
Keywords
» Artificial intelligence » Deep learning
