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Application of Machine Learning in Geoinformatics
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Application of Machine Learning in Geoinformatics

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

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1184

Editors

Dr. Zifu Wang

E-Mail Website
Guest Editor
Department of Geography and Geoinformation Science, NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
Interests: Coronavirinae; air pollution; COVID-19; air quality; nitrogen dioxide; atmospheric aerosol; engineering education; computer aided design; cloud computing
Prof. Dr. Cheng-Yu Ku

E-Mail Website
Guest Editor
Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, Taiwan
Interests: numerical methods; geotechnical engineering; groundwater modeling; groundwater; landslides modeling; slope stability; engineering geology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the cutting-edge integration of ML/AI techniques within the field of geoinformatics, highlighting novel methodologies, practical applications, and emerging challenges. We invite interdisciplinary contributions that leverage ML/AI—including deep learning, supervised/unsupervised learning, reinforcement learning, and large language models—to analyze, model, and interpret complex geospatial data. Topics of interest include ML-driven remote sensing image analysis and classification, spatial prediction and pattern recognition for land use and climate modeling, geospatial big data processing and scalability solutions, and natural language processing for geospatial metadata retrieval. We especially encourage submissions that demonstrate how AI-enhanced approaches can support urban planning, environmental monitoring, disaster management, conflict analysis, transportation safety, and public health—areas where geoinformatics provides direct input into evidence-based public policy. By showcasing technical innovation alongside societal relevance, this Special Issue aims to advance the transformative role of ML/AI in geoinformatics and to foster data-driven policy and decision-making for global resilience.

Dr. Zifu Wang
Prof. Dr. Cheng-Yu Ku
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • ML/AI in geoinformatics
  • remote sensing
  • spatial prediction and pattern recognition
  • geospatial big data
  • natural language processing (NLP)
  • large language models (LLMs)
  • urban planning and transportation safety
  • environmental monitoring and disaster management
  • public policy applications
  • ethical and interpretable AI

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Published Papers (2 papers)

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Research

25 pages, 11738 KB  
Article
Systematic Evaluation of Machine Learning Models for Regression-Based Error Refinement in SAR-to-Optical Image Translation for Cloud Removal
by Inseon Lee, Soyeon Park, Eui Ho Hwang and No-Wook Park
Appl. Sci. 2026, 16(11), 5283; https://doi.org/10.3390/app16115283 - 25 May 2026
Viewed by 305
Abstract
Generative deep learning-based synthetic aperture radar (SAR)-to-optical image translation (SOIT) has been widely employed for cloud removal. However, since cloud-contaminated regions reconstructed by SOIT inevitably contain prediction errors, an additional error refinement procedure is required to achieve reliable spectral reflectance reconstruction. In this [...] Read more.
Generative deep learning-based synthetic aperture radar (SAR)-to-optical image translation (SOIT) has been widely employed for cloud removal. However, since cloud-contaminated regions reconstructed by SOIT inevitably contain prediction errors, an additional error refinement procedure is required to achieve reliable spectral reflectance reconstruction. In this study, three machine learning-based regression models, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Natural Gradient Boosting (NGB), are comprehensively evaluated for the error refinement of optical imagery initially reconstructed by SOIT. The factors influencing refinement performance are categorized into four components: (1) the sampling strategy of training pixels from cloud-free regions (random vs. quantile-based sampling); (2) the refinement target (actual spectral reflectance vs. residual between actual and initially reconstructed reflectance); (3) SAR features (pixel-level raw SAR features vs. local spatial SAR features); and (4) the cloud fraction in the scene of interest. A systematic sensitivity analysis of their effects on error refinement performance was conducted over cropland using PlanetScope optical imagery and COSMO-SkyMed SAR imagery. The results showed that cloud fraction had the greatest impact on refinement performance. Regarding SAR features for regression, the use of local spatial SAR features improved spectral similarity by up to approximately 4.6%p compared to raw SAR features. In terms of sampling strategy, quantile-based sampling yielded better refinement performance, whereas the effect of the refinement target was less pronounced. These results suggest that local spatial SAR features and quantile-based sampling strategies are the key determinants of regression-based refinement performance in SOIT-based cloud removal. Full article
(This article belongs to the Special Issue Application of Machine Learning in Geoinformatics)
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15 pages, 26011 KB  
Article
Intelligent Detection of Lunar Impact Craters Using DEM and Gravity Data Based on ResNet and Vision Transformer
by Meng Ding, Zhili Du, Yu Bai, Shuai Wang and Xinyi Zhou
Appl. Sci. 2026, 16(8), 4035; https://doi.org/10.3390/app16084035 - 21 Apr 2026
Viewed by 479
Abstract
The craters on the moon hold important clues about the history of impacts in our solar system. To address the limitation of traditional intelligent methods in detecting buried craters, this study proposes a novel intelligent detection approach based on DEM and gravity data. [...] Read more.
The craters on the moon hold important clues about the history of impacts in our solar system. To address the limitation of traditional intelligent methods in detecting buried craters, this study proposes a novel intelligent detection approach based on DEM and gravity data. We designed a hybrid network architecture (ResNet + ViT) that combines the local feature extraction strengths of Convolutional Neural Networks with the global context modeling capabilities of Vision Transformer. By combining the complementary information from DEM and gravity anomaly data, it achieves comprehensive detection of lunar craters—from those visible on the surface to buried subsurface structures. To mitigate the inherent sample imbalance in both gravity anomaly and DEM training data, we employ a U-Net architecture augmented with residual blocks and train it using a Focal Loss function with dynamic focusing parameters. Experimental results show that: (1) The proposed method attains high segmentation accuracy, achieving a mean Intersection over Union of 81.3% on the DEM test set and 82.6% on the gravity anomaly test set, respectively. (2) Our method outperforms U-Net and its mainstream variants, achieving a precision of 89.48% and superior detection completeness. (3) Application to representative geological units, including the Wugang Basin, Archimedes Crater, and Mare Moscoviense, validates the robustness and practical utility of our method. This study, thus, provides a novel technical framework for global-scale mapping of lunar impact craters and yields new insights into the evolutionary history of the lunar surface. Full article
(This article belongs to the Special Issue Application of Machine Learning in Geoinformatics)
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