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Selected Papers from the International Conference on Geographical Information Systems...
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Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020)

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 6101

Special Issue Editors

Dr. Lemonia Ragia

E-Mail Website
Guest Editor
ATHENA Research & Innovation Center in Information Technologies, Artemidos 6 & Epidavrou 15124, Marousi, Athens, Greece
Interests: photogrammetry; computer vision; surveying; GIS; spatial analysis; virtual and augmented reality
Special Issues, Collections and Topics in MDPI journals
Dr. Cédric Grueau

E-Mail Website
Guest Editor
Polytechnic Institute of Setúbal/IPS, Setúbal, Portugal
Interests: geographic information science; multi-agent systems; model-driven development; social simulation; requirement engineering.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Robert Laurini

E-Mail Website
Guest Editor
Knowledge Systems Institute and INSA Lyon, University of Lyon, Villeurbanne, France
Interests: theoretical aspects of GIS and knowledge engineering for urban applications, and more generally how to cross-fertilize artificial intelligence and urban and environmental planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue includes selected papers from the GISTAM 2020. It is mainly devoted to data knowledge extraction and management, especially using real time data. Collecting spatial data has emerged as a challenge due to new technologies and instruments. Unmanned aerial vehicles (UAV), mobile phones, GNSS technology, remote sensing, and photogrammetry enable real time data acquisition. Cloud computing architecture opens a new era in retrieval and access to spatial data. The combination of data from different data sources provides a different approach to data analysis. Large data volumes require new spatial data processing methodologies, such as data mining and machine learning. Geospatial analysis has to ensure reliable and accurate results. Currently, many different applications are based on these results, such as in agriculture, transportation, disaster management, tourism, archaeology, and public health. The aim of this Special Issue in ISPRS International Journal of Geo-Information is to present high-quality research achievements. Authors are kindly invited to submit a paper in, but not limited to, one of the following topics: 

  1. Spatial databases and data integration;
  2. 3D modeling and geolocation;
  3. Statistical analysis and decision making;
  4. Natural hazard assessment;
  5. Data mining and machine learning in geosciences.

Dr. Lemonia Ragia
Dr. Cédric Grueau
Prof. Dr. Robert Laurini
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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
  • urban environment
  • geolocation
  • optimization of travel time
  • georeferencing
  • statistical analysis
  • data mining
  • 3D modeling

Published Papers (2 papers)

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Research

17 pages, 14005 KiB  
Article
Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data
by Thomas Graichen, Julia Richter, Rebecca Schmidt and Ulrich Heinkel
ISPRS Int. J. Geo-Inf. 2021, 10(4), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040216 - 01 Apr 2021
Cited by 5 | Viewed by 2004
Abstract
In recent years, there is a growing interest in indoor positioning due to the increasing amount of applications that employ position data. Current approaches determining the location of objects in indoor environments are facing problems with the accuracy of the sensor data used [...] Read more.
In recent years, there is a growing interest in indoor positioning due to the increasing amount of applications that employ position data. Current approaches determining the location of objects in indoor environments are facing problems with the accuracy of the sensor data used for positioning. A solution to compensate inaccurate and unreliable sensor data is to include further information about the objects to be positioned and about the environment into the positioning algorithm. For this purpose, occupancy grid maps (OGMs) can be used to correct such noisy data by modelling the occupancy probability of objects being at a certain location in a specific environment. In that way, improbable sensor measurements can be corrected. Previous approaches, however, have focussed only on OGM generation for outdoor environments or require manual steps. There remains need for research examining the automatic generation of OGMs from detailed indoor map data. Therefore, our study proposes an algorithm for automated OGM generation using crowd-sourced OpenStreetMap indoor data. Subsequently, we propose an algorithm to improve positioning results by means of the generated OGM data. In our study, we used positioning data from an Ultra-wideband (UWB) system. Our experiments with nine different building map datasets showed that the proposed method provides reliable OGM outputs. Furthermore, taking one of these generated OGMs as an example, we demonstrated that integrating OGMs in the positioning algorithm increases the positioning accuracy. Consequently, the proposed algorithms now enable the integration of environmental information into positioning algorithms to finally increase the accuracy of indoor positioning applications. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020))
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25 pages, 11279 KiB  
Article
ADAtools: Automatic Detection and Classification of Active Deformation Areas from PSI Displacement Maps
by J. A. Navarro, R. Tomás, A. Barra, J. I. Pagán, C. Reyes-Carmona, L. Solari, J. L. Vinielles, S. Falco and M. Crosetto
ISPRS Int. J. Geo-Inf. 2020, 9(10), 584; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100584 - 06 Oct 2020
Cited by 24 | Viewed by 3596
Abstract
This work describes the set of tools developed, tested, and put into production in the context of the H2020 project Multi-scale Observation and Monitoring of Railway Infrastructure Threats (MOMIT). This project, which ended in 2019, aimed to show how the use of various [...] Read more.
This work describes the set of tools developed, tested, and put into production in the context of the H2020 project Multi-scale Observation and Monitoring of Railway Infrastructure Threats (MOMIT). This project, which ended in 2019, aimed to show how the use of various remote sensing techniques could help to improve the monitoring of railway infrastructures, such as tracks or bridges, and thus, consequently, improve the detection of ground instabilities and facilitate their management. Several lines of work were opened by MOMIT, but the authors of this work concentrated their efforts in the design of tools to help the detection and identification of ground movements using synthetic aperture radar interferometry (InSAR) data. The main output of this activity was a set of tools able to detect the areas labelled active deformation areas (ADA), with the highest deformation rates and to connect them to a geological or anthropogenic process. ADAtools is the name given to the aforementioned set of tools. The description of these tools includes the definition of their targets, inputs, and outputs, as well as details on how the correctness of the applications was checked and on the benchmarks showing their performance. The ADAtools include the following applications: ADAfinder, los2hv, ADAclassifier, and THEXfinder. The toolset is targeted at the analysis and interpretation of InSAR results. Ancillary information supports the semi-automatic interpretation and classification process. Two real use-cases illustrating this statement are included at the end of this paper to show the kind of results that may be obtained with the ADAtools. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020))
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