In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in thi...In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.展开更多
Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch...Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.展开更多
基金sponsored by National Key R&D Program of China(2018YFC1504504)Youth Foundation of Yunnan Earthquake Agency(2021K01)Project of Yunnan Earthquake Agency“Chuan bang dai”(CQ3-2021001).
文摘In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.
文摘Machine learning techniques can fill data gaps for urban-scale building simulations,particularly gaps around window-to-wall ratio(WWR).This study presents a comprehensive workflow to(1)automatically extract and stitch images from Google Street View(GSV);(2)label images with a custom Rhino-based tool to aid annotation of occluded glazing;(3)detect wall,garage,and glazing objects by training and validating a YOLOv9 deep learning model with three added post-scripts;(4)calculate WWR at façade,building,and district scales;and(5)simulate district energy consumption in an urban building energy model(UBEM).Results include a 96%image-capture rate from GSV,indicating a robust extraction and stitching algorithm.Converting model detections into WWR,94%and 100%of façades have detected WWRs within±5%and±10%of ground truth WWRs,respectively.A novel automatic algorithm upscales façade detection to estimate WWR at non-street-facing sides and rears,resulting in distinct WWRs for each face of each building.For a case study in Turin,Italy,WWR detections are+5.2%and+6.9%greater when upscaling based on OpenStreetMap and municipal GIS data,respectively,compared to TABULA,leading to 1.5%and 35.5%increases in heating and cooling energy need in the UBEM.The workflow is made openly available to support future research in other contexts.
