Remote Sensing Based Building ExtractionMohammad Awrangjeb, Xiangyun Hu, Bisheng Yang, Jiaojiao Tian Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D |
Common terms and phrases
accuracy achieved aerial images algorithm analysis applied approach architecture areas automatic band better boundary building extraction classification color combined compared comparison complex Computer Conference connected convolutional corresponding CrossRef dataset deep DeepResUnet detection edges effect efficiency evaluation experimental F1 score feature maps filter follows four fully function fusion ground height high-resolution IEEE Trans imagery improve increase indicates input Inria ISPRS labeling layers learning LiDAR loss metrics neural network objects obtained operation optimization original outlines output parameters Pattern performance Photogramm pixels planes point clouds positive precision prediction Proceedings proposed method Recall recognition reconstruction reference regions regularization Remote Sens remote sensing images represents resolution respectively roof samples selected semantic segmentation sensing shown in Figure shows similar spatial spectral structure Table threshold U-Net urban vegetation Web-Net Zhang