TY - JOUR
AU - Guo, Tao
AB - Information of disaster damage assessment is very significant to disaster mitigation, aid and post disaster redevelopment planning. Remotely sensed data, especially very high resolution image data from aircraft and satellite have been long recognized very essential and objective source for disaster mapping. However feature extraction from these data remains a very challenge task currently. In this paper, we present a method to extract building damage caused by earthquake from two pairs of Worldview-2 high resolution satellite image. Targeting at implementing a practically operational system, we develop a novel framework integrating semi-automatic building extraction with machine learning mechanism to maximize the automation level of system. We also present a rectilinear building model to deal with a wide variety of rooftops. Through the study case of Haiti earthquake, we demonstrate our method is highly effective for detecting building damage from high resolution satellite image.
TI - Towards automation of building damage detection using WorldView-2 satellite image: the case of the Haiti earthquake
JF - Proceedings of SPIE
DO - 10.1117/12.867232
DA - 2010-10-12
UR - https://www.deepdyve.com/lp/spie/towards-automation-of-building-damage-detection-using-worldview-2-URqnK5MOOe
SP - 783108
EP - 783108-13
VL - 7831
IS - 1
DP - DeepDyve
ER -