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Applied Sciences
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GeoBigData, GeoAI, and GeoModeling Applications in Geo-Information Systems
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GeoBigData, GeoAI, and GeoModeling Applications in Geo-Information Systems

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

Deadline for manuscript submissions: 20 November 2025 | Viewed by 988

Special Issue Editors

Dr. Baoyi Zhang

E-Mail Website
Guest Editor
School of Geosciences & Info-Physics, Central South University, Changsha 410083, China
Interests: three-dimensional geological modeling; geologic big data; machine learning
Prof. Dr. Salvador García-Ayllón Veintimilla

E-Mail Website
Guest Editor
Department of Civil Engineering, Technical University of Cartagena, 30203 Cartagena, Spain
Interests: GIS analysis; landscape planning; sustainable mobility; environmental planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite manuscripts that explore novel applications and advancements in the integration of GeoBigData, GeoAI, and GeoModeling within GIS frameworks. We welcome original research papers, review articles, and case studies that contribute to the development of innovative techniques for analyzing complex geospatial data, optimizing decision-making, and supporting sustainable management of Earth's resources and environments.

The integration of GeoBigData, GeoAI, and GeoModeling has significantly advanced Geo-Information Systems (GISs), enabling more efficient data processing, enhanced analytical capabilities, and improved predictive modeling. GeoBigData refers to the vast volumes of geospatial data generated from sources such as Earth Observation (EO), remote sensing, and Internet of Things (IoT). The challenge lies in effectively managing, storing, and analyzing these data to extract meaningful insights. Advancements in cloud computing, artificial intelligence (AI), and GeoModeling techniques have been pivotal in addressing these challenges. GeoAI allows for the identification of complex patterns and relationships within geospatial datasets, enhancing geospatial output evaluation and map quality, as well as the improvement of image object detection, map generalization, and map design. GeoModeling encompasses the creation of digital representations of the Earth's subsurface and other geospatial phenomena. These models integrate various data types—geological, geophysical, and geochemical—to simulate and predict geological processes, resource distribution, and environmental changes.

Research areas may include (but are not limited to) the following:

  • Big data analytics and management in geospatial science.
  • Machine learning and artificial intelligence for GeoAI applications.
  • GeoModeling techniques for resource exploration and environmental monitoring.
  • Integration of GeoBigData with GIS platforms and applications.
  • Geospatial data fusion and advanced visualization methods.
  • Applications of GeoBigData and GeoAI in sustainable resource management and environmental monitoring.

Dr. Baoyi Zhang
Prof. Dr. Salvador García-Ayllón Veintimilla
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

  • GeoBigData
  • GeoAI
  • GIS
  • geospatial data fusion
  • machine learning

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Published Papers (2 papers)

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Research

16 pages, 3385 KB  
Article
Optimizing Text Recognition in Borehole Log Images Using a Multi-Layout Adjustment Voting Mechanism
by Zhiyong Guo, Yiwei Guo, Jiqiu Deng and Hassan Ali Fattah
Appl. Sci. 2025, 15(16), 9171; https://doi.org/10.3390/app15169171 - 20 Aug 2025
Viewed by 237
Abstract
The borehole log image contains valuable text information, encompassing key geological data such as structural composition, orebody distribution, and lithological characteristics. These data are important for mineral prediction, GeoBigData, and GeoModeling. However, text recognition in borehole log images is challenging due to complex [...] Read more.
The borehole log image contains valuable text information, encompassing key geological data such as structural composition, orebody distribution, and lithological characteristics. These data are important for mineral prediction, GeoBigData, and GeoModeling. However, text recognition in borehole log images is challenging due to complex structures, image noise, and diverse fonts, leading to low accuracy with traditional OCR methods. As a result, substantial manual intervention is often required for verification and correction, hindering efficient application. This study proposes an optimization method based on the multi-layout adjustment voting mechanism to improve text recognition accuracy in borehole log images. During the recognition process, multiple OCR results are generated by adjusting text layouts, and a voting mechanism integrates these results to produce the most accurate output. Experimental results on the Dayingezhuang and Dingjiashan datasets demonstrate the effectiveness of the proposed method, achieving F1 scores of 97.96% and 94.36%, respectively. This optimization method improves text recognition accuracy and recall without modifying the OCR algorithm or applying post-processing, providing a new technical approach to enhancing text recognition precision in borehole log images. This improvement in text extraction accuracy from geological borehole data not only facilitates large-scale integration and analysis of subsurface geological information but also provides essential foundational data for GeoBigData and GeoModeling applications. Full article
(This article belongs to the Special Issue GeoBigData, GeoAI, and GeoModeling Applications in Geo-Information Systems)
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14 pages, 2532 KB  
Article
Machine Learning for Spatiotemporal Prediction of River Siltation in Typical Reach in Jiangxi, China
by Yong Fu, Jin Luo, Die Zhang, Lingjia Liu, Gan Luo and Xiaofang Zu
Appl. Sci. 2025, 15(15), 8628; https://doi.org/10.3390/app15158628 - 4 Aug 2025
Viewed by 267
Abstract
Accurate forecasting of river siltation is essential for ensuring inland waterway navigability and guiding sustainable sediment management. This study investigates the downstream reach of the Shihutang navigation power hub along the Ganjiang River in Jiangxi Province, China, an area characterized by pronounced seasonal [...] Read more.
Accurate forecasting of river siltation is essential for ensuring inland waterway navigability and guiding sustainable sediment management. This study investigates the downstream reach of the Shihutang navigation power hub along the Ganjiang River in Jiangxi Province, China, an area characterized by pronounced seasonal sedimentation and hydrological variability. To enable fine-scale prediction, we developed a data-driven framework using a random forest regression model that integrates high-resolution bathymetric surveys with hydrological and meteorological observations. Based on the field data from April to July 2024, the model was trained to forecast monthly siltation volumes at a 30 m grid scale over a six-month horizon (July–December 2024). The results revealed a marked increase in siltation from July to September, followed by a decline during the winter months. The accumulation of sediment, combined with falling water levels, was found to significantly reduce the channel depth and width, particularly in the upstream sections, posing a potential risk to navigation safety. This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. The proposed framework provides a practical tool for early warning, targeted dredging, and adaptive channel management. Full article
(This article belongs to the Special Issue GeoBigData, GeoAI, and GeoModeling Applications in Geo-Information Systems)
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