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.

Date Published: 23 October 2010
PDF: 13 pages
Proc. SPIE 7831, Earth Resources and Environmental Remote Sensing/GIS Applications, 783108 (23 October 2010); doi: 10.1117/12.867232

Published in SPIE Proceedings Vol. 7831:
Earth Resources and Environmental Remote Sensing/GIS Applications
Ulrich Michel; Daniel L. Civco, Editor(s)