Deep Learning for Urban and Landscape Mapping from Remotely Sensed Imagery

2021 ◽  
pp. 153-174
Author(s):  
Feilin Lai ◽  
Atharva Sharma ◽  
Xiuwen Liu ◽  
Xiaojun Yang
Keyword(s):  
Deep Learning ◽  
Remotely Sensed ◽  
Remotely Sensed Imagery ◽  
Landscape Mapping

scholarly journals Road Extraction from Remotely Sensed Data: A Review

2021 ◽  
Author(s):  
Mohd Jawed Khan ◽  
Pankaj Pratap Singh
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Remotely Sensed ◽  
Future Research ◽  
Learning Approaches ◽  
Road Extraction ◽  
Remotely Sensed Data ◽  
Remotely Sensed Imagery ◽  
Urban Rural ◽  
Future Research Directions

Up-to-date road networks are crucial and challenging in computer vision tasks. Road extraction is yet important for vehicle navigation, urban-rural planning, disaster relief, traffic management, road monitoring and others. Road network maps facilitate a great number of applications in our everyday life. Therefore, a systematic review of deep learning approaches applied to remotely sensed imagery for road extraction is conducted in this paper. Four main types of deep learning approaches, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models are presented in this paper. We also compare these various deep learning models applied to remotely sensed imagery to show their performances in extracting road parts from high-resolution remote sensed imagery. Later future research directions and research gaps are described.

Remotely sensed imagery revealing the effects of hurricanes Gustav and Ike on coastal Louisiana

Data Series ◽  
10.3133/ds566 ◽  
2010 ◽  
Author(s):  
John A. Barras ◽  
John C. Brock ◽  
Robert A. Morton ◽  
Laurinda J. Travers
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Imagery ◽  
Coastal Louisiana

Map-guided interpretation of remotely-sensed imagery

1980 ◽  
Author(s):  
J. M. Tenenbaum ◽  
H. G. Barrow ◽  
R. C. Bolles ◽  
M. A. Fischler ◽  
H. C. Wolf
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Imagery
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