摘要：With the expansion of a city,the urban green space is occupied and the urban heat island effect is *** the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological *** provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society *** weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis *** the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the *** roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in *** paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.