Architectural heritage recognition and spatial visualization analysis based on SE-Mask-RCNN
The technology for building recognition based on deep learning is relatively mature,but there are limited studies on urban architectural heritage recognition using street view images.This paper introduces a novel architectural heritage recognition method,the SE-Mask-RCNN algorithm,for identifying architectural heritage in the street view image.This method addresses issues related to remote sensing image recognition accuracy and the timeliness of traditional exploration.The results are visually displayed in the space corresponding to the street attractions,effectively determing the location of architectural heritage and the core protection area,thereby providing decision support for architectural heritage preservation.This paper utilizes road network data to crawl street view data based on the characteristics of Chinese architectural heritage.The Mask-RCNN model with the SE mechanism is employed for architectural heritage The recognition results are compared with U-Net,FCN,Mask-RCNN,and other algorithms to analyze the feasibility of the SE-Mask-RCNN algorithm.Additionally,spatial visualization display and nuclear density analysis of the corresponding street attractions are conducted.Experimental results show that the proposed method efficiently and accurately identifies architectural heritage in street view images,with the mAP value of the extraction result being 2%higher than that of Mask-RCNN.The method exhibits favorable characteristics in image recognition results,mainly due to the flatter edge of architectural heritage and lower error rate.Compared with other algorithms,the accuracy and robustness of the method surpass the comparison algorithm.The identified architectural heritage street attractions are mapped onto the road network,reflecting the spatial location of the architectural heritage.From the perspective of spatial distribution,the distribution of architectural heritage in the Xicheng study area shows the following characteristics.① Overall distribution characteristics:analysis of the overall picture reveals that street attractions in the southern part are more abundant than in the northern part,and those in the western part are more concentrated than in the eastern part.This indicates that architectural heritage is mainly distributed in the eastern and southern parts of the study area.② Characteristics of some concentrated gathering areas:In the southeast region,some architectural heritages show a state of aggregation.The Protection of architectural heritages on the north and south sides is better than on the east and west sides.Small areas of street attractions in the northeast suggest well preserved architectural heritage in these hutongs,achieving integrity from single building preservation to the protection of building group.③ Lack of architectural protection in some areas:Street attractions in the eastern central region are scattered,indicating imperfect protection measures for architectural heritage in these alley.Strengthening the construction of architectural heritage protection areas is necessary.The distribution of architectural heritage in the western region is sparse,indicating significant loss due to urban modernization.To address the need for rapid and accurate identification of urban architectural heritage and the effective determination of protection areas,this paper proposes the SE-Mask-RCNN architectural heritage identification method.It visualizes the spatial distribution of architectural heritage on the map,providing a new way for the study of architectural heritage.The SE-Mask-RCNN model facilitate the visual distribution of architectural heritage for the intelligent recognition of large-scale street view image datasets,offering decision-making suggestions for tourism planning and architectural heritage protection.
traditional architecturearchitectural heritagedeep learningMask-RCNNstreet view data
NETL
NSTL
万方数据
