Abstract: In order to improve the efficiency and the accuracy of information extraction about earthquake damage building, based on high resolution remote sensing image after the earthquake, and according to the features of earthquake damage buildings in remote sensing images, we took a case study of M_S7.0 Haiti earthquake in 2010, through the ESP algorithm automatically chose the optimal segmentation scale to multi-scale segmentation of images, used object-oriented method to Haiti high-resolution remote sensing image information extraction of earthquake damage buildings. At the same time, it is compared with Support Vector Machine based on pixel, BP neural network and Decision Tree classification method based on CART algorithm, the experimental results show that the object-oriented classification method has better visual effect and higher classification accuracy, which is beneficial to the accurate extraction and rapid evaluation of earthquake damage information after the earthquake.