This paper presents an approach to process raw unmanned aircraft vehicle(UAV)image-derived point clouds for automatically detecting,segmenting and regularizing buildings of complex urban landscapes.For regularizing,we...This paper presents an approach to process raw unmanned aircraft vehicle(UAV)image-derived point clouds for automatically detecting,segmenting and regularizing buildings of complex urban landscapes.For regularizing,we mean the extraction of the building footprints with precise position and details.In the first step,vegetation points were extracted using a support vector machine(SVM)classifier based on vegetation indexes calculated from color information,then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings.In the second step,we first determined the building boundary points with a modified convex hull algorithm.Then,we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm.Then,two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints.Eventually,the building edges were regularized to form the final building footprints.Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.展开更多
In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derive...In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,respectively.For the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 countries.For the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 countries.Grouped k-fold,3-repeat cross validation was used to ensure out-of-sample predictive performance.Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility.The 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme events.However,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of 0.93.Therefore,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 41471341,41301430]the Young Scientists Foundation of RADI[grant numbers Y5SJ1000CX]‘135’Strategy Planning[grant numbers Y3SG1500CX]of the Institute of Remote Sensing and Digital Earth,Chinese Academy of Science。
文摘This paper presents an approach to process raw unmanned aircraft vehicle(UAV)image-derived point clouds for automatically detecting,segmenting and regularizing buildings of complex urban landscapes.For regularizing,we mean the extraction of the building footprints with precise position and details.In the first step,vegetation points were extracted using a support vector machine(SVM)classifier based on vegetation indexes calculated from color information,then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings.In the second step,we first determined the building boundary points with a modified convex hull algorithm.Then,we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm.Then,two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints.Eventually,the building edges were regularized to form the final building footprints.Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.
基金funded by the Engineering&Physical Sciences Research Council(EPSRC)Impact Acceleration Account Award EP/R511742/1。
文摘In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,respectively.For the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 countries.For the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 countries.Grouped k-fold,3-repeat cross validation was used to ensure out-of-sample predictive performance.Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility.The 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme events.However,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of 0.93.Therefore,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.
