Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques

no code implementations • 24 Nov 2020 • Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn, Wenhan Lu, David Berthelot, Izzeddin Gur, Pranav Khaitan, Ke-Wei, Huang, Kyriacos Koupparis, Bernhard Kowatsch

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.

BIG-bench Machine Learning Disaster Response +1

Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks

no code implementations • 14 Oct 2019 • Joseph Z. Xu, Wenhan Lu, Zebo Li, Pranav Khaitan, Valeriya Zaytseva

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response.

BIG-bench Machine Learning