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: 10 May 2026 | Viewed by 26
Special Issue Editors
Interests: Coronavirinae; air pollution; COVID-19; air quality; nitrogen dioxide; atmospheric aerosol; engineering education; computer aided design; cloud computing
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
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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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