Abstract: This paper proposes a fully convolutional networks based method to intelligently exploit Sentinel-2 data for high-rise and low-rise building areas extraction. The building areas are divided into high-rise building areas and low-rise building areas according to their spatial structure in Sentinel-2 data and their real functional types. Four Sentinel-2 data covering the Xiong'an New Area and its surroundings in early 2017 is selected for experimental verification and analysis. The results show that the proposed algorithm can extract the high-rise and low-rise building areas from Sentinel-2 data in an effective and efficient manner. Overall accuracy of the two types of building areas is about 95.30%, of which the high-rise building areas accuracy is about 99.22%, and the accuracy of other building areas is about 91.38%. Compared with the existing texture-based method, the proposed method is more robust and fast. The high-rise and low-rise building areas in the study area covering about 44 000 km² are about 94 and 7351 km², respectively. The high-rise and low-rise building areas in the three core counties of Xiong'an New Area are about 1.25 and 312.24 km², respectively. Our method can be easily extend to extract finer types of building areas given proper training samples of finer types of buildings areas and can realize large scale dynamic monitoring with the large imaging width and the high-frequency observation of Sentinel-2 data.

Key words: Sentinel-2, fully convolutional networks, high-rise, low-rise, building areas, extraction