Summary of Openstreetmap: Challenges and Opportunities in Machine Learning and Remote Sensing, by John Vargas et al.
OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing
by John Vargas, Shivangi Srivastava, Devis Tuia, Alexandre Falcao
First submitted to arxiv on: 13 Jul 2020
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 reviews recent machine learning methods to improve and utilize OpenStreetMap (OSM) data, a community-based editable map service. OSM is widely used in geosciences, Earth Observation, and environmental sciences despite its heterogeneous completeness and quality across locations. The reviewed methods aim to either enhance OSM coverage and quality using GIS and remote sensing technologies or leverage existing OSM layers to train image-based models for applications like navigation and land use classification. By integrating machine learning with OSM, participatory map making can be scaled up to meet global needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can make maps better using computers. Maps are important because they help us understand the world. One type of map is called OpenStreetMap (OSM). It’s a special kind of map that people can edit themselves. The problem with OSM is that not all parts of it are as good as others. Some areas might have more details, while others might be missing some information. This paper looks at new ways to make OSM better using computers and other technologies. These methods can help us create more accurate maps for things like navigation or figuring out what’s on the ground. |
Keywords
* Artificial intelligence * Classification * Machine learning
