A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
[article]
2020
arXiv
pre-print
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected. Recent research combines machine learning with remote sensing to automatically extract such information from satellite imagery, reducing manual labor and turn-around time. A major impediment to using machine learning methods in real disaster
arXiv:2011.14004v1
fatcat:beiddlsfynddbf5ad7ayqrsyuy