AUTOMATIC IDENTIFICATION METHOD OF CONSTRUCTION AND DEMOLITION WASTE BASED ON DEEP LEARNING AND GAOFEN-2 DATA
Keywords: C&DW, DeepLabv3+, Image Automatic Identification
Abstract. Due to the relatively complex construction and demolition waste (C&DW) spectrum and texture, it is difficult to identify C&DW by simply constructing a remote sensing index. Therefore, this study proposes an automatic identification method of C&DW based on deep learning and the Gaofen-2 (GF-2) Data. Pingdingshan City and Jining City in China were selected as the research areas in the study. The dataset used for deep learning training and testing in the study area was captured by the GF-2 Data. On the basis of this dataset, the deep learning model DeepLabv3+ is used to identify C&DW. The overall accuracy rate of the deep learning model for identifying C&DW is 82.02%, and the overall mIoU is 82.39%. The accuracy of the model for the identification of C&DW areas is further verified by ground verification. The results of this study are helpful for the survey and management of C&DW, which is beneficial to the study of spatial and temporal distribution of urban C&DW, resource utilization and environmental pollution risk reduction.