Remote Sensing as a Tool for Urban Planning and Sustainability

2007 ◽  
pp. 1-23 ◽  
Author(s):  
Maik Netzband ◽  
William L. Stefanov ◽  
Charles L. Redman
Keyword(s):  
Remote Sensing ◽  
Urban Planning

Applications and Trends of Remote Sensing in Professional Urban Planning

2011 ◽  
Vol 5 (5) ◽  
pp. 249-261 ◽  
Author(s):  
Nancy Hoalst-Pullen ◽  
Mark W. Patterson
Keyword(s):  
Remote Sensing ◽  
Urban Planning

scholarly journals Editorial for Special Issue: “Remote Sensing based Building Extraction”

2020 ◽  
Vol 12 (3) ◽  
pp. 549
Author(s):  
Mohammad Awrangjeb ◽  
Xiangyun Hu ◽  
Bisheng Yang ◽  
Jiaojiao Tian
Keyword(s):  
Remote Sensing ◽  
Urban Planning ◽  
Disaster Management ◽  
Remote Sensing Data ◽  
Building Extraction ◽  
Special Issue ◽  
Sensing Data ◽  
Geographic Databases

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [...]

Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning

2016 ◽  
Vol 186 ◽  
pp. 637-651 ◽  
Author(s):  
Andrew M. Coutts ◽  
Richard J. Harris ◽  
Thu Phan ◽  
Stephen J. Livesley ◽  
Nicholas S.G. Williams ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Planning ◽  
Thermal Infrared ◽  
Thermal Infrared Remote Sensing ◽  
Infrared Remote Sensing ◽  
Urban Heat

Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning

2018 ◽  
Vol 28 (2) ◽  
pp. 274-286 ◽  
Author(s):  
Inoka Sandamali Serasinghe Pathiranage ◽  
Lakshmi N. Kantakumar ◽  
Sivanantharajah Sundaramoorthy
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Urban Planning ◽  
Growth Model ◽  
Urban Growth ◽  
Remote Sensing Data ◽  
Decision Support Tools ◽  
Sensing Data ◽  
Urban Growth Model ◽  
Support Tools

scholarly journals Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yao Yao ◽  
Chen Qian ◽  
Ye Hong ◽  
Qingfeng Guan ◽  
Jingmin Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Planning ◽  
Reference Value ◽  
Vital Role ◽  
Training Data ◽  
Fine Scale ◽  
Housing Type ◽  
Training Strategy ◽  
Socioeconomic Characteristics ◽  
User Data

The spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction. However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data and useful models. With user data acquired from a location-based service provider in China, this study employs a deep bag-of-features network (BagNet) to classify remote-sensing (RS) images into various jobs-housing types. Considering Wuhan, one of the fastest developing cities in China, as a case study area, three jobs-housing types (i.e., only working, only living, and both working and living) at the land-parcel level are obtained. We demonstrate that the multiscale random sampling method can reduce the influence of image noise, increase the utilization of training data, and reduce network overfitting. By altering the network structure and the training strategy, BagNet achieved excellent fitting accuracy for identifying each jobs-housing type (overall accuracy > 0.84 and kappa > 0.8). For the first time, we demonstrate that urban socioeconomic characteristics can be obtained from high-resolution RS images using deep learning techniques. Additionally, we conclude that the total level of mixing within Wuhan is not high at present; however, Wuhan is continuously improving the mixture of jobs and housing. This study has reference value for extracting urban socioeconomic characteristics from RS images and could be used in urban planning as well as government management.

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