HOUSE DAMAGE ASSESSMENT BASED ON SUPERVISED LEARNING METHOD: CASE STUDY ON HAITI

Assessing the damage caused by natural disasters requires fast and reliable information. Satellite imagery, especially high-resolution imagery, is recognized as an important source for wide-range and immediate data acquisition. Disaster assessment using satellite imagery is required worldwide. To assess damage caused by an earthquake, house changes or landslides are detected by comparing images taken before and after the earthquake. We have developed a method that performs this comparison using vector data instead of raster data. The proposed method can detect house changes without having to rely on various image acquisition situations and shapes of house shadows. We also developed a house-position detection method based on machine learning. It uses local features including not only pixel-by-pixel differences but also the shape information of the object area. The result of the house-position detection method indicates the likelihood of a house existing in that area, and changes to this likelihood between multi-temporal images indicate the damaged house area. We evaluated our method by testing it on two WorldView-2 (c) panchromatic images taken before and after the 2010 earthquake in Haiti. The highly accurate results demonstrate the effectiveness of the proposed method.