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Special Issue "Point Cloud and Image Analysis for the Measurement of the Physical Form of Cities"

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 August 2021) | Viewed by 3645

Special Issue Editors

Dr. Lucía Díaz-Vilariño
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Guest Editor
Applied Geotechnologies Research Group, Campus Universitario de Vigo, Universidade de Vigo, CINTECX, As Lagoas, Marcosende, 36310 Vigo, Spain
Interests: point cloud processing; 3D digital modeling; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Fernández
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Guest Editor
Department of Engineering Design, Universidade de Vigo, Rúa Maxwell s/n, 36310 Vigo, Spain
Interests: image processing; machine learning; computer vision; texture analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Vítor Oliveira
E-Mail Website
Guest Editor
CITTA (Centro de Investigação do Território, Transportes e Ambiente), Faculdade de Engenharia, Universidade do Porto, s/n, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: urban morphology; urban planning; architecture; cities

Special Issue Information

Dear Colleagues,

In the recent years, remote sensing has become a de facto technology for documenting and modelling the physical form of cities. Remote sensing—terrestrial, aerial, and satellite—has proven to be a suitable approach to effectively collect data at a large scale. Indeed, advances in the integration of sensors in terrestrial mobile platforms, together with the increasingly lower costs of technology, have significantly improved the availability and quality of data. As a result, automated data analysis has become a hot research topic.

Despite the potential of remote sensing for accurately measuring and modelling the physical form of cities, its use has not yet been adopted by the urban morphology community. Researchers in this area have traditionally used simple, two-dimensional representations to characterize city forms. In contrast, the remote sensing approach to urban modelling relies on complex, three-dimensional models that are built from a huge amount of data. Moving from two- to three-dimensional representations requires careful considerations of the costs and benefits associated with such a transition. Complex 3D models may provide rich insights not only on city forms, but also on the process of urban landscape formation, but in order for this approach to be effective, it is of paramount importance to clearly distinguish which data are relevant and which are superfluous.

This Special Issue aims at collecting the recent advances in the use of remote sensing data for the measurement of the physical form of cities. We welcome submissions on the integration of measurement techniques with existing morphological theories, concepts, and methods. Specific topics include, but are not limited to, the following:

  • New remote sensing technologies for urban measurement;
  • Image processing for large-scale urban modelling;
  • 3D modelling of urban areas from point cloud processing;
  • Spatial analysis of urban-landscape changes;
  • 3D analysis of urban landscape;
  • 3D space syntax;
  • Urban structure analysis based on 2D and/or 3D morphology;
  • Impacts of 3D morphology on the urban environment and ecology.

Dr. Lucía Díaz-Vilariño
Dr. Antonio Fernández
Dr. Vítor Oliveira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2500 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.

Published Papers (1 paper)

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Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net
Remote Sens. 2020, 12(10), 1574; - 15 May 2020
Cited by 42 | Viewed by 3098
Unplanned urban settlements exist worldwide. The geospatial information of these areas is critical for urban management and reconstruction planning but usually unavailable. Automatically characterizing individual buildings in the unplanned urban village using remote sensing imagery is very challenging due to complex landscapes and [...] Read more.
Unplanned urban settlements exist worldwide. The geospatial information of these areas is critical for urban management and reconstruction planning but usually unavailable. Automatically characterizing individual buildings in the unplanned urban village using remote sensing imagery is very challenging due to complex landscapes and high-density settlements. The newly emerging deep learning method provides the potential to characterize individual buildings in a complex urban village. This study proposed an urban village mapping paradigm based on U-net deep learning architecture. The study area is located in Guangzhou City, China. The Worldview satellite image with eight pan-sharpened bands at a 0.5-m spatial resolution and building boundary vector file were used as research purposes. There are ten sites of the urban villages included in this scene of the Worldview image. The deep neural network model was trained and tested based on the selected six and four sites of the urban village, respectively. Models for building segmentation and classification were both trained and tested. The results indicated that the U-net model reached overall accuracy over 86% for building segmentation and over 83% for the classification. The F1-score ranged from 0.9 to 0.98 for the segmentation, and from 0.63 to 0.88 for the classification. The Interaction over Union reached over 90% for the segmentation and 86% for the classification. The superiority of the deep learning method has been demonstrated through comparison with Random Forest and object-based image analysis. This study fully showed the feasibility, efficiency, and potential of the deep learning in delineating individual buildings in the high-density urban village. More importantly, this study implied that through deep learning methods, mapping unplanned urban settlements could further characterize individual buildings with considerable accuracy. Full article
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