Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.4
Journals
IJGI
Special Issues
Selected Papers from the International Conference on Geographical Information Systems...
share announcement

Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management-Spatial Data Analysis: Methods and Techniques (GISTAM 2021)

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 9342

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.
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 2021. 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 (UAVs), mobile phones, GNSS technology, remote sensing, and photogrammetry enable real-time data acquisition. Cloud computing architecture opens a new era in the retrieval of and access to spatial data. The combination of data from different data sources provides a different approach for the analysis of data. 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. This Special Issue in the ISPRS International Journal of Geo-Information will present high-quality research achievements. Authors are kindly invited to submit a paper on, 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 (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 14825 KiB  
Article
Urban Change Detection from Aerial Images Using Convolutional Neural Networks and Transfer Learning
by Tautvydas Fyleris, Andrius Kriščiūnas, Valentas Gružauskas, Dalia Čalnerytė and Rimantas Barauskas
ISPRS Int. J. Geo-Inf. 2022, 11(4), 246; https://doi.org/10.3390/ijgi11040246 - 10 Apr 2022
Cited by 4 | Viewed by 3746
Abstract
Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of [...] Read more.
Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different resolution with practically acceptable accuracy after a short training process. Remote sensing data can be used to detect changes in number of buildings, forest areas, and other landscape objects. In this paper, aerial images of a digital raster orthophoto map at scale 1:10,000 of the Republic of Lithuania (ORT10LT) of three periods (2009–2010, 2012–2013, 2015–2017) were analyzed. Because of the developing technologies, the quality of the images differs significantly and should be taken into account while preparing the dataset for training the semantic segmentation model DeepLabv3 with a ResNet50 backbone. In the data preparation step, normalization techniques were used to ensure stability of image quality and contrast. Focal loss for the training metric was selected to deal with the misbalanced dataset. The suggested model training process is based on the transfer learning technique and combines using a model with weights pretrained in ImageNet with learning on coarse and fine-tuning datasets. The coarse dataset consists of images with classes generated automatically from Open Street Map (OSM) data and the fine-tuning dataset was created by manually reviewing the images to ensure that the objects in images match the labels. To highlight the benefits of transfer learning, six different models were trained by combining different steps of the suggested model training process. It is demonstrated that using pretrained weights results in improved performance of the model and the best performance was demonstrated by the model which includes all three steps of the training process (pretrained weights, training on coarse and fine-tuning datasets). Finally, the results obtained with the created machine learning model enable the implementation of different approaches to detect, analyze, and interpret urban changes for policymakers and investors on different levels on a local map, grid, or municipality level. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management-Spatial Data Analysis: Methods and Techniques (GISTAM 2021))
►▼ Show Figures

Figure 1

21 pages, 6352 KiB  
Article
Evaluation of Drifting Snow Susceptibility Based on GIS and GA-BP Algorithms
by Bohu He, Mingzhou Bai, Binglong Liu, Pengxiang Li, Shumao Qiu, Xin Li and Lusheng Ding
ISPRS Int. J. Geo-Inf. 2022, 11(2), 142; https://doi.org/10.3390/ijgi11020142 - 16 Feb 2022
Cited by 2 | Viewed by 2922
Abstract
Drifting snow, the flow of dispersed snow particles near ground level under the action of wind, is a major form of snow damage. When drifting snow occurs on railways, highways, and other transportation lines, it seriously affects their operational safety and results in [...] Read more.
Drifting snow, the flow of dispersed snow particles near ground level under the action of wind, is a major form of snow damage. When drifting snow occurs on railways, highways, and other transportation lines, it seriously affects their operational safety and results in drifting snow disasters. Drifting snow disasters frequently occur in the high latitudes of northwest China. At present, most scholars are committed to studying the prevention and control measures of drifting snow, but the prerequisite for prevention is to effectively evaluate the susceptibility of drifting snow along railways and highways to identify areas with a high risk of occurrence. Taking the Xinjiang Afukuzhun Railway as an example, this study uses a geographic information system (GIS) combined with on-site monitoring and surveys to establish a drifting snow susceptibility evaluation index system. The drifting snow susceptibility index (DSSI) is calculated through the weight of an evidence (WOE) model, and a genetic algorithm backpropagation (GA-BP) algorithm is used to obtain optimised evaluation index weights to improve the accuracy of model evaluation. The results show that the accuracies of the WOE model, WOE backpropagation (WOE-BP) model, and weight of evidence genetic algorithm backpropagation (WOE-GA-BP) model are 0.747, 0.748, and 0.785, respectively, indicating that the method can be effectively applied to evaluate drifting snow susceptibility. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management-Spatial Data Analysis: Methods and Techniques (GISTAM 2021))
►▼ Show Figures

Figure 1

14 pages, 2678 KiB  
Article
A Comparative Study about Vertical Accuracy of Four Freely Available Digital Elevation Models: A Case Study in the Balsas River Watershed, Brazil
by Zuleide Alves Ferreira and Pedro Cabral
ISPRS Int. J. Geo-Inf. 2022, 11(2), 106; https://doi.org/10.3390/ijgi11020106 - 02 Feb 2022
Cited by 5 | Viewed by 1951
Abstract
Digital elevation models (DEMs) provide important support to research since these data are freely available for almost all areas of the terrestrial surface. Thus, it is important to assess their accuracy for correct applicability regarding the correct use scale. Therefore, this paper aims [...] Read more.
Digital elevation models (DEMs) provide important support to research since these data are freely available for almost all areas of the terrestrial surface. Thus, it is important to assess their accuracy for correct applicability regarding the correct use scale. Therefore, this paper aims to assess the vertical accuracy of ALOS PALSAR, GMTED2010, SRTM, and Topodata DEMs according to the Brazilian Cartographic Accuracy Standard through the official high accuracy network data of the Brazilian Geodetic System. This study also seeks to investigate whether the altimetric error is correlated with altitude and slope in the study area. Our results showed that the four assessed DEMs in this study demonstrated satisfactory accuracy to provide mappings in scales up to 1:100,000 because more than 90% of the extracted points presented altimetric errors of less than 25 m when compared with the reference points from the high accuracy network of the Brazilian Geodetic System. Regarding the altimetric error, we could not find a significant correlation with altitude or slope in the study area. In this sense, future DEMs assessments should be based on the investigation of other factors that may influence altimetric error. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Geographical Information Systems Theory, Applications and Management-Spatial Data Analysis: Methods and Techniques (GISTAM 2021))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop