Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale buildin...Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.展开更多
基金supported by the National Science Foundation[grant numbers 1854502 and 1855902]Publication was made possible in part by support from the HKU Libraries Open Access Author Fund sponsored by the HKU Libraries.USDA is an equal opportunity provider and employer.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S.Department of Agriculture.
文摘Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.
