|
|
|
|
| LEADER |
05060nma 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 high-resolution aerial imagery
|
| 653 |
|
|
|a 3-D
|
| 653 |
|
|
|a imagery
|
| 653 |
|
|
|a U-Net
|
| 653 |
|
|
|a feature fusion
|
| 653 |
|
|
|a 5G signal simulation
|
| 653 |
|
|
|a elevation map
|
| 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 Inria aerial image labeling dataset
|
| 653 |
|
|
|a data fusion
|
| 653 |
|
|
|a deep learning
|
| 653 |
|
|
|a generative adversarial network
|
| 653 |
|
|
|a occlusion
|
| 653 |
|
|
|a developing city
|
| 653 |
|
|
|a multiscale Siamese convolutional networks (MSCNs)
|
| 653 |
|
|
|a unmanned aerial vehicle (UAV)
|
| 653 |
|
|
|a richer convolution features
|
| 653 |
|
|
|a urban building extraction
|
| 653 |
|
|
|a simple linear iterative clustering (SLIC)
|
| 653 |
|
|
|a building edges detection
|
| 653 |
|
|
|a modelling
|
| 653 |
|
|
|a History of engineering and technology / bicssc
|
| 653 |
|
|
|a LiDAR point cloud
|
| 653 |
|
|
|a indoor modelling
|
| 653 |
|
|
|a digital building height
|
| 653 |
|
|
|a outline extraction
|
| 653 |
|
|
|a land-use
|
| 653 |
|
|
|a method comparison
|
| 653 |
|
|
|a very high resolution imagery
|
| 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 open data
|
| 653 |
|
|
|a high spatial resolution remotely sensed imagery
|
| 653 |
|
|
|a Gabor filter
|
| 653 |
|
|
|a building extraction
|
| 653 |
|
|
|a web-net
|
| 653 |
|
|
|a LiDAR
|
| 653 |
|
|
|a remote sensing
|
| 653 |
|
|
|a point clouds
|
| 653 |
|
|
|a high-resolution aerial images
|
| 653 |
|
|
|a morphological profile
|
| 653 |
|
|
|a building boundary extraction
|
| 653 |
|
|
|a Massachusetts buildings dataset
|
| 653 |
|
|
|a building
|
| 653 |
|
|
|a ultra-hierarchical sampling
|
| 653 |
|
|
|a semantic segmentation
|
| 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 change detection
|
| 653 |
|
|
|a fully convolutional network
|
| 653 |
|
|
|a mobile laser scanning
|
| 653 |
|
|
|a feature-level-fusion
|
| 653 |
|
|
|a active contour model
|
| 653 |
|
|
|a regularization
|
| 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 |
0 |
|u https://www.mdpi.com/books/pdfview/book/2139
|7 0
|x Verlag
|3 Volltext
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/58168
|z DOAB: description of the publication
|
| 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
|