ISPRS International Journal of Geo-Information

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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

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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

18 pages, 4238 KiB

Open AccessArticle

Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning

by Huan Ning, Zhenlong Li, Michael E. Hodgson and Cuizhen (Susan) Wang

ISPRS Int. J. Geo-Inf. 2020, 9(2), 104; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020104 - 09 Feb 2020

Cited by 33 | Viewed by 4492

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 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

(This article belongs to the Special Issue Spatiotemporal Platforms for Addressing Social and Physical Challenges)

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18 pages, 13547 KiB

Open AccessArticle

A Sightseeing Support System Using Augmented Reality and Pictograms within Urban Tourist Areas in Japan

by Ryo Sasaki and Kayoko Yamamoto

ISPRS Int. J. Geo-Inf. 2019, 8(9), 381; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8090381 - 30 Aug 2019

Cited by 13 | Viewed by 3943

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 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

(This article belongs to the Special Issue Spatiotemporal Platforms for Addressing Social and Physical Challenges)

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