Remote Sensing based Building Extraction

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...

Full description

Bibliographic Details
Main Author: Yang, Bisheng
Other Authors: Awrangjeb, Mohammad, Hu, Xiangyun, Tian, Jiaojiao
Format: eBook
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:
3-d
N/a
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
LEADER 05047nma a2201309 u 4500
001 EB001964530
003 EBX01000000000000001127432
005 00000000000000.0
007 cr|||||||||||||||||||||
008 210512 ||| eng
020 |a 9783039283835 
020 |a books978-3-03928-383-5 
020 |a 9783039283828 
100 1 |a Yang, Bisheng 
245 0 0 |a Remote Sensing based Building Extraction  |h Elektronische Ressource 
260 |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2020 
300 |a 1 electronic resource (442 p.) 
653 |a 3-D 
653 |a high-resolution aerial imagery 
653 |a imagery 
653 |a U-Net 
653 |a feature fusion 
653 |a elevation map 
653 |a 5G signal simulation 
653 |a mathematical morphology 
653 |a n/a 
653 |a 3D urban expansion 
653 |a high-resolution satellite images 
653 |a accuracy analysis 
653 |a high resolution optical images 
653 |a data fusion 
653 |a Inria aerial image labeling dataset 
653 |a developing city 
653 |a occlusion 
653 |a generative adversarial network 
653 |a deep learning 
653 |a unmanned aerial vehicle (UAV) 
653 |a multiscale Siamese convolutional networks (MSCNs) 
653 |a richer convolution features 
653 |a simple linear iterative clustering (SLIC) 
653 |a urban building extraction 
653 |a building edges detection 
653 |a modelling 
653 |a History of engineering & technology / bicssc 
653 |a indoor modelling 
653 |a LiDAR point cloud 
653 |a digital building height 
653 |a land-use 
653 |a outline extraction 
653 |a very high resolution imagery 
653 |a method comparison 
653 |a feature extraction 
653 |a binary decision network 
653 |a morphological attribute filter 
653 |a building detection 
653 |a aerial images 
653 |a attention mechanism 
653 |a image fusion 
653 |a straight-line segment matching 
653 |a boundary extraction 
653 |a building regularization technique 
653 |a high spatial resolution remote sensing imagery 
653 |a deep convolutional neural network 
653 |a object recognition 
653 |a building reconstruction 
653 |a high spatial resolution remotely sensed imagery 
653 |a open data 
653 |a building extraction 
653 |a Gabor filter 
653 |a web-net 
653 |a LiDAR 
653 |a remote sensing 
653 |a point clouds 
653 |a morphological profile 
653 |a high-resolution aerial images 
653 |a Massachusetts buildings dataset 
653 |a building boundary extraction 
653 |a building 
653 |a semantic segmentation 
653 |a ultra-hierarchical sampling 
653 |a boundary regulated network 
653 |a GIS data 
653 |a roof segmentation 
653 |a convolutional neural network 
653 |a VHR remote sensing imagery 
653 |a very high resolution 
653 |a DTM extraction 
653 |a building index 
653 |a point cloud 
653 |a reconstruction 
653 |a fully convolutional network 
653 |a change detection 
653 |a mobile laser scanning 
653 |a regularization 
653 |a active contour model 
653 |a feature-level-fusion 
653 |a 3D reconstruction 
700 1 |a Awrangjeb, Mohammad 
700 1 |a Hu, Xiangyun 
700 1 |a Tian, Jiaojiao 
041 0 7 |a eng  |2 ISO 639-2 
989 |b DOAB  |a Directory of Open Access Books 
500 |a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/ 
024 8 |a 10.3390/books978-3-03928-383-5 
856 4 2 |u https://directory.doabooks.org/handle/20.500.12854/58168  |z DOAB: description of the publication 
856 4 0 |u https://www.mdpi.com/books/book/2139  |x Verlag  |3 Volltext 
082 0 |a 900 
082 0 |a 414 
082 0 |a 500 
082 0 |a 600 
082 0 |a 620 
520 |a 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