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Urban Environments Modeling using Very-High-Resolution Imagery and Crowdsourced Geospatial Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 3959

Special Issue Editors

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Guest Editor

Special Issue Information

Dear Colleagues,

Very high resolution (VHR) remote sensing imagery and crowdsourced geospatial data provide innovative means for monitoring and modeling urban environments. The launches of commercial satellites with very high spatial resolution (VHR) sensors (e.g. IKONOS, QuickBird, Worldview and Gaofen), as well as unmanned aerial vehicles (VAVs) with VHR aerial photos and LiDAR data, bring a nonparallel opportunity for analyzing physical elements in urban environments. Moreover, crowdsourced geospatial data (e.g., OpenStreetMap, Point of Interest, and social media) bring new approaches to observe human-related characteristics of urban environments. Contrary to the availability of VHR (e.g. spatial, spectral, temporal, angle) imagery and crowdsourced geospatial data, the developments of state-of-the-art analytical techniques and novel applications in urban environments are still limited. It is highly necessary to develop innovative technologies and applications for creating a sustainable urban environment and alleviating negative impacts of urbanization.

This special issue calls for innovative techniques and novel applications for analyzing urban environments using VHR remote sensing imagery and crowdsourced geospatial data. Topics include but not limited to:

  • urban elements classification and information extraction from multi-source spatial data,
  • fusion and integration of VHR images and crowdsourced geospatial data,
  • urban change detection and dynamic analysis,
  • machine learning and spatiotemporal statistic methods,
  • image and data mining from multi-source, multi-scale, multi-temporal data,
  • urban growth and land use patterns and changes
  • urban population, energy consuming, impervious surfaces modeling
  • urban heat island and urban pollution
  • urban air, water and green space and their dynamics,
  • biodiversity loss and degradation,
  • human-environment interactions in urban environments, and
  • measuring indicators of sustainable urban development.

Prof. Shihong Du
Prof. Changshan Wu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Very-High-Resolution remote sensing
  • Crowdsourced geospatial data
  • Geographic object-based image analysis
  • Image and data mining
  • Spatial data analysis
  • Urban Environments
  • Land cover and land use
  • Human-environment interactions
  • Sustainable urban development

Published Papers (1 paper)

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10 pages, 3341 KiB  
Technical Note
Automatic Estimation of Urban Waterlogging Depths from Video Images Based on Ubiquitous Reference Objects
by Jingchao Jiang, Junzhi Liu, Changxiu Cheng, Jingzhou Huang and Anke Xue
Remote Sens. 2019, 11(5), 587; - 11 Mar 2019
Cited by 26 | Viewed by 3542
Video supervision equipment, which is readily available in most cities, can record the processes of urban floods in video form. Ubiquitous reference objects, which often appear in videos, can be used to indicate urban waterlogging depths. This makes video images a valuable data [...] Read more.
Video supervision equipment, which is readily available in most cities, can record the processes of urban floods in video form. Ubiquitous reference objects, which often appear in videos, can be used to indicate urban waterlogging depths. This makes video images a valuable data source for obtaining waterlogging depths. However, the urban waterlogging information contained in video images has not been effectively mined and utilized. In this paper, we present a method to automatically estimate urban waterlogging depths from video images based on ubiquitous reference objects. First, reference objects from video images are detected during the flooding and non-flooding periods using an object detection model with a convolutional neural network (CNN). Then, waterlogging depths are estimated using the height differences between the detected reference objects in these two periods. A case study is used to evaluate the proposed method. The results show that our proposed method could effectively mine and utilize urban waterlogging depth information from video images. This method has the advantages of low economic cost, acceptable accuracy, high spatiotemporal resolution, and wide coverage. It is feasible to promote this proposed method within cities to monitor urban floods. Full article
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