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International Conference on Geospatial Data Mining and Machine Learning - (ICGDML-26)

30th - 31st October 2026 , Adelaide - Australia

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Call For Papers

The (ICGDML-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as GIS (Geographic Information Systems) are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Geospatial data mining techniques and applications
  • Machine learning for spatial data analysis
  • Spatial clustering methods in data mining
  • Geospatial big data challenges and solutions
  • GIS applications in predictive modeling
  • Data mining for environmental monitoring
  • Spatial data visualization and interpretation
  • Machine learning algorithms for geospatial data
  • GIS in urban analytics and planning
  • Geospatial data integration and fusion techniques
  • Applications of AI in geospatial analysis
  • Spatial regression models in data mining
  • Geospatial data mining for disaster management
  • Innovative applications of machine learning in GIS
  • Geospatial data mining in agriculture
  • Spatial decision support systems and data mining
  • GIS for social media data analysis
  • Geospatial data mining for transportation studies
  • Machine learning for land use classification
  • Future trends in geospatial data mining

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.