• Corpus ID: 227227614

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

  title={Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques},
  author={Jihyeon Lee and Joseph Z. Xu and Kihyuk Sohn and Wenhan Lu and David Berthelot and Izzeddin Gur and Pranav Khaitan and Ke Huang and Kyriacos M. Koupparis and 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. 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… 
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