To detect building-damage areas in post-seismic high-resolution remote sensing images, a building-damage detection method based on multi-features was proposed. Firstly, Morphological Attribute Profile (MAP) and Local Binary Pattern (LBP) operator were used to extract geometric features and texture features. Then, Random Forest (RF) classifier was applied to extract damaged building regions so as to form the preliminary results. At last, for segmented objects, the ultimate building-damage area was obtained by computing the damaged ratio of each object. Experiments were carried out on Yushu post-seismic aerial remote sensing images whose spatial resolution was 0.1 m. Results show that this method improves overall accuracy by 12% compared with Morphological Profile (MP)-based method. The results indicate that the proposed method can effectively detect building-damage areas with high accuracy in post-seismic high-resolution images.