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Applied Sciences
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Web Geoprocessing Services and GIS for Various Applications
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Web Geoprocessing Services and GIS for Various Applications

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

Deadline for manuscript submissions: 26 August 2024 | Viewed by 5682

Special Issue Editors

Dr. Huaqiao Xing

E-Mail Website
Guest Editor
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Interests: geospatial web service; GIS; land cover; deep learning; service platform
Dr. Dongyang Hou

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: web service discovery and WebGIS development; spatio-temporal crowd-sourced data mining; remote sensing image retrieval; land cover mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, an increasing number of geographic information applications have been moved from stand-alone applications to applications requiring online collaboration. The advances in web services and service-oriented architectures have significantly improved the transformation process. At present, although international standards for geographic information services (i.e., web map services and web processing services) and advanced geographic information cloud computing platforms (, i.e., Google Earth engine and PIE engine) have emerged, challenges still remain for non-expert users. On the one hand, most of the current commercial software and open-source software focus on the release of geospatial data web services and less on the release of geoprocessing services. Therefore, how to quickly publish processing models of related fields such as web geoprocessing services is difficult for non-expert users. On the other hand, the collaboration and quality monitoring of web geoprocessing services are more complicated. Recently, the development of technologies such as knowledge graphs, cloud computing, edge computing, and artificial intelligence has further promoted the progress and application of web geoprocessing services. This Special Issue aims to provide the latest and future directions in web geoprocessing services, models, and systems.

Dr. Huaqiao Xing
Dr. Dongyang Hou
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

  • web geoprocessing services
  • geographic information service modeling
  • service-oriented architecture
  • web services collaboration
  • web services discovery
  • quality of service for web services
  • knowledge graph
  • high-performance computing
  • webGIS development
  • remote sensing applications using web geoprocessing services
  • other applications using web geoprocessing services

Published Papers (5 papers)

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Research

20 pages, 10584 KiB  
Article
Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning
by Mingyang Yu, Fangliang Zhou, Haiqing Xu and Shuai Xu
Appl. Sci. 2023, 13(24), 13149; https://doi.org/10.3390/app132413149 - 11 Dec 2023
Cited by 1 | Viewed by 680
Abstract
Rural building automatic extraction technology is of great significance for rural planning and disaster assessment; however, existing methods face the dilemma of scarce sample data and large regional differences in rural buildings. To solve this problem, this study constructed an image dataset of [...] Read more.
Rural building automatic extraction technology is of great significance for rural planning and disaster assessment; however, existing methods face the dilemma of scarce sample data and large regional differences in rural buildings. To solve this problem, this study constructed an image dataset of typical Chinese rural buildings, including nine typical geographical regions, such as the Northeast and North China Plains. Additionally, an improved remote sensing image rural building extraction network called AGSC-Net was designed. Based on an encoder–decoder structure, the model integrates multiple attention gate (AG) modules and a context collaboration network (CC-Net). The AG modules realize focused expression of building-related features through feature selection. The CC-Net module models the global dependency between different building instances, providing complementary localization and scale information to the decoder. By embedding AG and CC-Net modules between the encoder and decoder, the model can capture multiscale semantic information on building features. Experiments show that, compared with other models, AGSC-Net achieved the best quantitative metrics on two rural building datasets, verifying the accuracy of the extraction results. This study provides an effective example for automatic extraction in complex rural scenes and lays the foundation for related monitoring and planning applications. Full article
(This article belongs to the Special Issue Web Geoprocessing Services and GIS for Various Applications)
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15 pages, 7792 KiB  
Article
Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
by Dong Zhang and Jiqiu Deng
Appl. Sci. 2023, 13(7), 4282; https://doi.org/10.3390/app13074282 - 28 Mar 2023
Viewed by 1153
Abstract
Different tile maps may use different coordinate systems, and it is difficult to superimpose maps from different sources. In this regard, we propose a progressive dynamic registration (PDR) method based on optimum features extracted from images of tile map screenshots of a certain [...] Read more.
Different tile maps may use different coordinate systems, and it is difficult to superimpose maps from different sources. In this regard, we propose a progressive dynamic registration (PDR) method based on optimum features extracted from images of tile map screenshots of a certain scale. Using this method, we can automatically register maps in roughly the same area from different sources without knowing the map project information. The better features among feature points and feature surfaces are selected for image registration based on the richness of the features in the map, and a new matching filter that combines the characteristics of the tile map and the features is proposed. In the progressive registration process during map zooming, the result of the adjacent scales are used as reference values for coarse registration. After experimental verification in different areas, the root mean square error of PDR is below 2.5 in different maps and is better than that of the registration method using feature points only. Moreover, the registration accuracies of remote sensing maps in all areas and vector maps in nonurban areas are better than that of the method based on coordinate system transformation. The calculation results of PDR can register not only the tile maps but also other nontiled vector or remote sensing data. Full article
(This article belongs to the Special Issue Web Geoprocessing Services and GIS for Various Applications)
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14 pages, 2478 KiB  
Article
A Focused Event Crawler with Temporal Intent
by Hao Wu and Dongyang Hou
Appl. Sci. 2023, 13(7), 4149; https://doi.org/10.3390/app13074149 - 24 Mar 2023
Cited by 1 | Viewed by 964
Abstract
Temporal intent is an important component of events. It plays an important role in collecting them from the web with focused crawlers. However, traditionally focused crawlers usually only consider factors such as topic keywords, web page content, and anchor text, ignoring the relationship [...] Read more.
Temporal intent is an important component of events. It plays an important role in collecting them from the web with focused crawlers. However, traditionally focused crawlers usually only consider factors such as topic keywords, web page content, and anchor text, ignoring the relationship between web pages and the temporal intent of events. This leads to their poor crawling performance. This paper aims to understand the temporal intent of events and apply it within focused crawlers. First, a new temporal intent identification method is proposed based on Google Trends data. The method can automatically identify the start time of an event and quantify the temporal distribution of the event. Then, a new focused event crawler with temporal intent is proposed. The crawler incorporates the start time of the event into the similarity calculation module, and a new URL (Uniform Resource Locator) priority assignment method is developed using the quantified temporal distribution of temporal intent as the independent variable of a natural exponential function. Experimental results show that our method is effective in identifying the start time of events at the month level and quantifying the temporal distribution of events. Furthermore, compared to the traditional best-first crawling method, the precision of our method improves by an average of 10.28%, and a maximum of 25.21%. These results indicate that our method performs better in retrieving relevant pages and assigning URL priority. This also illustrates the importance of the relationship between web pages and the temporal intent of events. Full article
(This article belongs to the Special Issue Web Geoprocessing Services and GIS for Various Applications)
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18 pages, 7138 KiB  
Article
A Novel Cycle Slips Detection and Repair Method with AR Model of BDS-3 Dual-Frequency Signal in Severe Multipath Environments
by Yipeng Ning, Junye Cui, Wenshuo Zhao, Dashuai Chai, Yingjun Sun, Jianping Xing and Shengli Wang
Appl. Sci. 2023, 13(1), 27; https://doi.org/10.3390/app13010027 - 20 Dec 2022
Viewed by 984
Abstract
High-level applications of geo-processing services generally lack accurate temporal and spatial information. BDS-3 provides high precision temporal and spatial reference for geoprocessing services, but their signal is prone to cycle slips in a severe multipath environment. Aiming at the problem of the reliable [...] Read more.
High-level applications of geo-processing services generally lack accurate temporal and spatial information. BDS-3 provides high precision temporal and spatial reference for geoprocessing services, but their signal is prone to cycle slips in a severe multipath environment. Aiming at the problem of the reliable detection and repair of cycle slips in BDS-3 (B1c + B2a) dual-frequency positioning in a severe multipath environment, an AR (autoregressive) model-assisted MW + GF BDS dual-frequency combined detection method (AMG method) is proposed in this research. A sliding-window autoregressive prediction strategy is introduced to correct the pseudorange observations interfered by a multipath, then an AR + MW + GF cycle slips detection model is constructed, and a cycle slips statistical completeness test index is established to verify the effectiveness of the algorithm. Six groups of cycle slips are artificially added into the different constellations and dual-frequency point phase observations of BDS-3 (B1c and B2a) in a multipath environment to demonstrate the cycle slips’ detection performance. The experimental results show that the traditional MW + GF method fails, but the proposed AMG method still maintains accurate cycle slip detection and repair capabilities. The detection success rate and repair success rate obtained by using the new method are significantly improved by 63.4%, and the cycle slips’ false detection rate and missed detection rate are reduced by 64.5% and 42.0%, respectively, even in harsh environments. Full article
(This article belongs to the Special Issue Web Geoprocessing Services and GIS for Various Applications)
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18 pages, 5565 KiB  
Article
A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
by Kaiqi Chen, Guowei Chu, Kaiyuan Lei, Yan Shi and Min Deng
Appl. Sci. 2022, 12(13), 6301; https://doi.org/10.3390/app12136301 - 21 Jun 2022
Cited by 1 | Viewed by 1241
Abstract
Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot [...] Read more.
Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot fully capture the complex traffic flow patterns that are attributed to human mobility in road networks. Moreover, real-world road networks usually contain millions of segments, which poses a great challenge regarding the memory usage and computational efficiency of existing methods. Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information. First, the road network was modeled as a multigraph, and a multiview random walk method was developed to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency. Finally, word2vec was applied to learn representations based on sequences that were generated by the multiview random walk. In the experiment, two real-world datasets were used to demonstrate the superior performance of our framework through a comparative analysis. Full article
(This article belongs to the Special Issue Web Geoprocessing Services and GIS for Various Applications)
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