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Acquisition and Analysis of Spatial Data for Sustainability Assessment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 2108

Special Issue Editors


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Guest Editor
Department of Civil Engineering, Architecture, and Environment, Mineral and Energy Resources Engineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Interests: geographic modeling; spatial data algorithms and computational geometry; geographical information in planning; building information modeling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
Interests: remote sensing applications; building information modelling; digital elevation models; vulnerability and hazard assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spatial data are a critical resource, enabling the study of patterns and relations between locations and variables in geographic information systems (GIS). Spatial data can be acquired by using a variety of techniques, devices, and sources, influencing the models later used in GIS-based analyses, such as those within the scope of sustainability assessments. Digital tools that support the integration of heterogeneous data and the production of spatial analytics also enable the effective presentation of results, and have become essential in the multidisciplinary aspects involved in sustainability-related research.

This Special Issue of Sustainability aims to be a platform for researchers to publish innovative high-quality research papers, reviews, case studies, and position papers focusing on spatial data acquisition or spatial data analyses in a sustainability assessment context. A nonexhaustive list of potential topics is provided below:

  • Spatial data acquisition methodologies to obtain inputs for resources, energy, and land use studies;
  • Planar, 3D, and spatiotemporal simulation or modelling of data in such studies;
  • Methodological aspects of geospatial data analysis impacting sustainability assessments;
  • Data handling techniques for the spatialization of sustainability-related indicators;
  • Case studies of GIS-based resources and environmental evaluations;
  • Impacts of spatial data models, quality, transformation, and processing in sustainability assessments;
  • Applications for spatial data mining, geovisualization, or spatial decision-support systems in sustainability-related case studies.

Dr. Alexandre B. Gonçalves
Dr. Ana Paula Falcão
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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. Sustainability 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 2400 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.

Keywords

  • spatial data
  • remote sensing applications
  • GIS
  • geomatics
  • spatial indicators
  • geovisualization
  • sustainability assessment

Published Papers (1 paper)

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Research

19 pages, 8428 KiB  
Article
SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring
by Dora Roque, Ana Paula Falcão, Daniele Perissin, Conceição Amado, José V. Lemos and Ana Fonseca
Sustainability 2023, 15(4), 3728; https://doi.org/10.3390/su15043728 - 17 Feb 2023
Viewed by 1635
Abstract
Interferometric Synthetic Aperture Radar (InSAR) has proved its efficiency for displacement monitoring in urban areas. However, the large volume of data generated by this technology turns the retrieval of information useful for structure monitoring into a big data problem. In this study, a [...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) has proved its efficiency for displacement monitoring in urban areas. However, the large volume of data generated by this technology turns the retrieval of information useful for structure monitoring into a big data problem. In this study, a new tool (SARClust) to analyze InSAR displacement time series is proposed. The tool performs the clustering of persistent scatterers (PSs) based on dissimilarities between their displacement time series evaluated through dynamic time warping. This strategy leads to the formation of clusters containing PSs with similar displacements, which can be analyzed together, reducing data dimensionality, and facilitating the identification of displacement patterns potentially related to structural damage. A proof of concept was performed for downtown Lisbon, Portugal, where ten distinct displacement patterns were identified. A relationship between clusters presenting centimeter-level displacements and buildings located on steep slopes was observed. The results were validated through visual inspections and comparison with another tool for time series analysis. Agreement was found in both cases. The innovation in this study is the attention brought to SARClust’s ability to (i) analyze vertical and horizontal displacements simultaneously, using an unsupervised procedure, and (ii) characterize PSs assisting the displacement interpretation. The main finding is the strategy to identify signs of structure damage, even on isolated buildings, in a large amount of InSAR data. In conclusion, SARClust is of the utmost importance to detect potential signs of structural damage in InSAR displacement time series, supporting structure safety experts in more efficient and sustainable monitoring tasks. Full article
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