Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/maning/robosat-viz
Visualization of Robosat🤖 trained imagery
https://github.com/maning/robosat-viz
image-processing machine-learning philippines
Last synced: about 1 month ago
JSON representation
Visualization of Robosat🤖 trained imagery
- Host: GitHub
- URL: https://github.com/maning/robosat-viz
- Owner: maning
- License: cc0-1.0
- Created: 2018-07-09T10:48:27.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-07-10T04:33:24.000Z (over 5 years ago)
- Last Synced: 2024-10-27T22:00:41.799Z (3 months ago)
- Topics: image-processing, machine-learning, philippines
- Language: CSS
- Homepage:
- Size: 56.6 KB
- Stars: 2
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# robosat-viz
## What
* Visualization of [Robosat](https://github.com/mapbox/robosat/)🤖 trained imagery in the Philippines 🇵ðŸ‡.
* TMS are hosted in s3.
* Imagery are in webp format, use a webp compliant browser.
* Use it in anyway you want **except when it violates human rights and the destruction of our home planet.**## List
* [American Red Cross drone footage in Visayas](https://maning.github.io/robosat-viz/arc.html) - building detection from drone captured by the American Red Cross.
* [Narvacan Sulvec Port Road, Philippines](https://maning.github.io/robosat-viz/narvacan-sulvec.html) - testing pre-trained model on another drone footage at Narvacan.
* [Villa Imelda, MacArthur, Philippines](https://maning.github.io/robosat-viz/villaimelda-macarthur.html) - detection using a z21 pre-trained model to a z20-21 imagery tiles.
* [Leda Coz Bazar, Bangladesh](https://maning.github.io/robosat-viz/leda-cox-bazar.html) - detection using a z21 pre-trained model to a z20 imagery tiles taken by IOM Drone Imagery project for the Rohingya refugees.
* [Can-Avid Eastern Samar, Philippines](https://maning.github.io/robosat-viz/can-avid.html) - detection from post-typhoon imagery (2014-12-01) captured by SkyEye (4cm).## Notes
* [OSM diary on data preparation](https://www.openstreetmap.org/user/maning/diary/44462)