Paper Info
Title
Urban Building Extraction And Modeling Using Gf-7 Dlc And Mux Images
Abstract
Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications.
Year
DOI
Venue
2021
10.3390/rs13173414
REMOTE SENSING
Keywords
DocType
Volume
building extraction, building modeling, Gaofen-7 image, deep learning, digital surface model
Journal
13
Issue
Citations 
PageRank 
17
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Heng Luo142.76
Biao He200.34
Renzhong Guo34111.41
Weixi Wang415.77
Xi Kuai500.34
Bilu Xia600.34
Yuan Wan700.34
Ding Ma8113.35
Linfu Xie901.01