Special Issue "Spatiotemporal Platforms for Addressing Social and Physical Challenges"

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

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Prof. Chaowei Yang
E-Mail Website
Guest Editor
NSF Spatiotemporal Innovation Center and Department of Geography & GeoInformation Science, George Mason University, Fairfax, VA, 22030-4444 USA
Tel. 703-993-4742
Interests: spatiotemporal intelligence; big earth data; spatial cloud computing; ML & DL for geosciences; knowledge base and applications; spatiotemporal computing
Special Issues and Collections in MDPI journals
Assoc. Prof. Mei Li
E-Mail Website
Guest Editor
School of Earth and Space Sciences, Peking University, Beijing, China
Interests: Geology; 3D GIS; Digital Earth; Emergency Response

Special Issue Information

Dear Colleagues,

Social and physical challenges, such as natural disasters, environmental problems, public security, and human activities, occur in both space and time. Many technologies have been developed in the past decade to address these challenges. Especially in the past few years, systematic investigation of spatiotemporal issues has provided new opportunities to address these challenges with better solutions. For example, the utilization of spatiotemporal principles and patterns in optimizing hybrid computing (cloud, grid, edge/fog, quantum, and GPU) has greatly improved our leveraging of limited computing devices for complex computing tasks. The extraction and application of spatiotemporal methodologies in dealing with big spatiotemporal data has opened new doors to enable many real-time and new approaches. Unprecedent applications can be tackled with these latest advances. New platforms leveraging these spatiotemporal advancements have been developed in the past few years and drive the advancement of relevant physical and social sciences. These platforms also shed new light on where the GIS is heading in the next decade with the tight coupling of spatiotemporal thinking and cutting-edge IT technologies, such as the cloud computing, Internet of Things, big data, artificial intelligence, block chain mobility, and virtual reality. This Special Issue of IJGI (ISPRS International Journal of Geo-Information) seeks to capture the latest advancements in platform research and development to reform the architecture of GIS. Topics include, but are not limited to, the following:

  • Spatiotemporal cloud platforms;
  • Big spatiotemporal data platforms;
  • AI technologies for spatiotemporal data;
  • Smart cities;
  • Real-time platforms;
  • Spatiotemporal platforms for geosciences (environment, earth, ecosystem, etc.);
  • Spatiotemporal platforms for social sciences (public security, etc.);
  • Internet of Things;
  • Cyber-physical systems.

Prof. Dr. Chaowei Yang
Assoc. Prof. Mei Li
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 papers will be 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 1000 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

  • geospatial solution
  • spatiotemporal platform
  • cloud computing
  • big data analytics
  • geosciences, interoperability and fusion
  • natural hazards
  • model simulation
  • public security

Published Papers (2 papers)

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Research

Open AccessArticle
Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning
ISPRS Int. J. Geo-Inf. 2020, 9(2), 104; https://doi.org/10.3390/ijgi9020104 - 09 Feb 2020
Abstract
This article aims to implement a prototype screening system to identify flooding-related photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decision-makers. This screening system, designed for application [...] Read more.
This article aims to implement a prototype screening system to identify flooding-related photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decision-makers. This screening system, designed for application to social media images, includes several key modules: tweet/image downloading, flooding photo detection, and a WebGIS application for human verification. In this study, a training dataset of 4800 flooding photos was built based on an iterative method using a convolutional neural network (CNN) developed and trained to detect flooding photos. The system was designed in a way that the CNN can be re-trained by a larger training dataset when more analyst-verified flooding photos are being added to the training set in an iterative manner. The total accuracy of flooding photo detection was 93% in a balanced test set, and the precision ranges from 46–63% in the highly imbalanced real-time tweets. The system is plug-in enabled, permitting flexible changes to the classification module. Therefore, the system architecture and key components may be utilized in other types of disaster events, such as wildfires, earthquakes for the damage/impact assessment. Full article
Open AccessArticle
A Sightseeing Support System Using Augmented Reality and Pictograms within Urban Tourist Areas in Japan
ISPRS Int. J. Geo-Inf. 2019, 8(9), 381; https://doi.org/10.3390/ijgi8090381 - 30 Aug 2019
Abstract
Though tourists can search for necessary information on the internet while sightseeing, it takes effort and is inconvenient to obtain available information related to specific sightseeing spots among the copious amount of information online. Targeting urban tourist areas in Japan, the present study [...] Read more.
Though tourists can search for necessary information on the internet while sightseeing, it takes effort and is inconvenient to obtain available information related to specific sightseeing spots among the copious amount of information online. Targeting urban tourist areas in Japan, the present study aims to develop a system that can provide guidance and information concerning sightseeing spots by integrating location-based augmented reality (AR) and object-recognition AR and by using pictograms. The system enables users to efficiently obtain the directions to sightseeing spots and nearby facilities within urban tourist areas and sightseeing spot information. Additionally, the city of Chofu in the metropolis of Tokyo was selected as the operation target area. The operation of the system was conducted for 1 month, targeting those inside and outside the operation target area, and a web questionnaire survey was conducted with a total number of 50 users. From the evaluation results of the web questionnaire survey, the usefulness of the original functions of integrating location-based AR and object-recognition AR and by using pictograms, as well as of the entire system, was analyzed. From the results of the access analysis of users’ log data, it is expected that users will further utilize each function. Additionally, it is evident that location-based AR was used more often than was object-recognition AR. Full article
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