Machine Learning for Missing Maps: A Collaboration to Improve Disaster Relief
Produktnummer:
181c6a1d646eb54da1ab9aea2f03f1716e
| Autor: | Sanam |
|---|---|
| Themengebiete: | AI-assisted mapping workflows Automated feature detection Disaster relief Humanitarian mapping Increased mapping efficiency Machine learning (ML) Missing Maps Project OpenStreetMap (OSM) Satellite imagery analysis Volunteer data validation |
| Veröffentlichungsdatum: | 22.06.2024 |
| EAN: | 9783384269126 |
| Sprache: | Englisch |
| Seitenzahl: | 88 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | tredition |
Produktinformationen "Machine Learning for Missing Maps: A Collaboration to Improve Disaster Relief"
Disaster strikes, but where's the map? Traditional maps often lack details in vulnerable areas. Here's where machine learning (ML) and a collaborative project called Missing Maps team up to improve disaster relief. Imagine vast regions missing from maps – areas critical for delivering aid after a crisis. Missing Maps uses volunteers to map these areas online. But the sheer volume of data can be overwhelming. This is where ML comes in. It analyzes satellite imagery, identifying potential roads, buildings, and landmarks. This "first draft" saves volunteers time by suggesting features to map, allowing them to focus on refining details. The collaboration is a win-win. ML lightens the load for volunteers, and Missing Maps gets more accurate maps faster. This translates to quicker aid delivery and a lifeline for people in desperate need.
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