About this Research Topic
Geostatistical Learning (GL) is an emerging field within geostatistics that integrates statistical (machine) learning with geospatial data (Hoffimann et al. 2021). Despite its recent introduction, GL has rapidly garnered attention from geoscientists who recognize the limitations of traditional non-geospatial statistical learning theories, particularly their inability to explicitly represent geospatial associations. Advances in GL are often driven by practical applications in various geosciences, where unique challenges arise that are not addressed by conventional machine learning methods. These challenges necessitate further investigation to enhance the efficacy of geostatistical modeling in contemporary industries. Recent studies have highlighted both the potential and the gaps in current GL methodologies, underscoring the need for a deeper theoretical and practical understanding of learning models in geospatial contexts.
This Research Topic aims to collect and disseminate applications of geostatistical learning across diverse scientific fields. The primary objectives include showcasing the successes and limitations of non-geospatial machine learning methodologies when applied to geospatial data, and contributing to the theoretical framework of learning models in geospatial settings. Specific questions to be addressed include: How do geospatial associations influence the performance of machine learning models? What are the best practices for integrating geospatial data into statistical learning frameworks? By answering these questions, the research aims to bridge the gap between theory and application in GL.
To gather further insights in the realm of geostatistical learning, we welcome articles addressing, but not limited to, the following themes:
- Mining and geometallurgical modeling for energy transition
- Agriculture and crop yield modeling for sustainable food production
- Subsurface modeling for carbon capture and sequestration
- Climate, glaciological, hydrological, and environmental modeling
- Disease modeling and public health
- Water-energy-food nexus
Hoffimann J., Zortea M., de Carvalho B., Zadrozny B. Geostatistical Learning: Challenges and Opportunities. Frontiers in Applied Mathematics and Statistics (2021) DOI=10.3389/fams.2021.689393
Topic Editor Júlio Hoffimann is the founder and CEO of Arpeggeo® Technologies. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
This Research Topic aims to collect and disseminate applications of geostatistical learning across diverse scientific fields. The primary objectives include showcasing the successes and limitations of non-geospatial machine learning methodologies when applied to geospatial data, and contributing to the theoretical framework of learning models in geospatial settings. Specific questions to be addressed include: How do geospatial associations influence the performance of machine learning models? What are the best practices for integrating geospatial data into statistical learning frameworks? By answering these questions, the research aims to bridge the gap between theory and application in GL.
To gather further insights in the realm of geostatistical learning, we welcome articles addressing, but not limited to, the following themes:
- Mining and geometallurgical modeling for energy transition
- Agriculture and crop yield modeling for sustainable food production
- Subsurface modeling for carbon capture and sequestration
- Climate, glaciological, hydrological, and environmental modeling
- Disease modeling and public health
- Water-energy-food nexus
Hoffimann J., Zortea M., de Carvalho B., Zadrozny B. Geostatistical Learning: Challenges and Opportunities. Frontiers in Applied Mathematics and Statistics (2021) DOI=10.3389/fams.2021.689393
Topic Editor Júlio Hoffimann is the founder and CEO of Arpeggeo® Technologies. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: geostatistical learning, geospatial data, applications, mining, agriculture, energy, climate, water, environment
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
