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Applied Sciences
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Recent Advances in Real-Time and Dynamic GIS
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Recent Advances in Real-Time and Dynamic GIS

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 4743

Special Issue Editors

Prof. Dr. Chuli Hu

E-Mail Website
Guest Editor
National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: real-time and dynamic GIS; big data in sensor web; sensing capbility calculating and mining; smart city
Prof. Dr. Zeqiang Chen

E-Mail Website
Guest Editor
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China
Interests: real-time and dynamic GIS; sensor web; intelligence of spatio-temporal big data; smart basin; smart city
Prof. Dr. Xuefeng Guan

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: GIS

Special Issue Information

Dear Colleagues,

With the development of the Internet of Things, geographic information technology, high-performance computing, and smart cities, real-time and dynamic GIS has become a new trend of GIS research. Different from traditional static or temporal GIS, real-time dynamic GIS emphasizes the acquisition, storage, management, analysis, and visualization of GIS with real-time data stream. In recent decades, great progress has been made in the theory, key technologies, and system platform of real-time and dynamic GIS, which has been used in a series of application scenarios such as intelligent transportation, natural resource management, precision agriculture, and smart community.

This Special Issue has been organized to focus on the latest developments of real-time and dynamic GIS. We encourage original papers on the following topics to be contributed to this Special Issue. We also welcome original research on innovative technologies and interdisciplinary research, such as new applications and new ideas of artificial intelligence, high-performance computing, and next-generation communication technology in the field of real-time and dynamic GIS.

Prof. Dr. Chuli Hu
Prof. Dr. Zeqiang Chen
Prof. Dr. Xuefeng Guan
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. Applied Sciences 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

  • real-time and dynamic gis
  • internet of things and geospatial sensor web
  • intelligent sensing
  • real-time data model
  • real-time geographic data storage
  • real-time geographic computing and mining
  • real-time geographic simulation and analysis
  • real-time geographic visualization
  • smart city application

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Published Papers (2 papers)

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Research

23 pages, 5833 KiB  
Article
Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection
by Mu Duan, Yunbo Zhang, Ran Liu, Shen Chen, Guoquan Deng, Xiaowei Yi, Jie Li and Puwei Yang
Appl. Sci. 2023, 13(22), 12482; https://doi.org/10.3390/app132212482 - 18 Nov 2023
Cited by 3 | Viewed by 1472
Abstract
Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood [...] Read more.
Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information. Full article
(This article belongs to the Special Issue Recent Advances in Real-Time and Dynamic GIS)
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17 pages, 6072 KiB  
Article
Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception
by Xu Yang, Feiyang Wu, Ruchuan Li, Dong Yao, Lei Meng and Ankai He
Appl. Sci. 2023, 13(20), 11183; https://doi.org/10.3390/app132011183 - 11 Oct 2023
Cited by 4 | Viewed by 2323
Abstract
The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic [...] Read more.
The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers. Full article
(This article belongs to the Special Issue Recent Advances in Real-Time and Dynamic GIS)
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