Remote Sensing based Building Extraction

Beschreibung

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

Autor*in

Mohammad Awrangjeb

Themen in »Remote Sensing based Building Extraction«

roof segmentation outline extraction convolutional neural network boundary regulated network very high resolution imagery building boundary extraction convolutional neural network active contour model high resolution optical images LiDAR richer convolution features building edges detection high spatial resolution remote sensing imagery building modelling

Stimmen zu »Remote Sensing based Building Extraction«

Details

Link teilen