Roychowdhury et al. (2025) Web based geospatial artificial intelligence for earth resource management toward climate change adaption

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-28
• Authors: Tuhin Roychowdhury, Arati Paul
• DOI: 10.1016/b978-0-443-29216-3.00003-5

Research Groups

• Amity University, Kolkata, West Bengal, India
• Regional Remote Sensing Centre-East, National Remote Sensing Centre (NRSC), Indian Space Organisation (ISRO), Kolkata, West Bengal, India

Short Summary

This chapter introduces the integration of web-based geospatial artificial intelligence (GeoAI) with remote sensing and GIS for earth resource management, emphasizing its role in climate change adaptation and disaster management through accessible, cost-effective solutions.

Objective

• To explore and demonstrate the application of web-based geospatial artificial intelligence (GeoAI) for effective earth resource management and climate change adaptation, particularly in the context of climate-resilient disaster management.

Study Configuration

• Spatial Scale: Global to local scales
• Temporal Scale: Not explicitly defined in the provided excerpt, but implied long-term for climate change adaptation.

Methodology and Data

• Models used: Artificial intelligence (AI), Geospatial Artificial Intelligence (GeoAI)
• Data sources: Remote sensing, Geographic Information Systems (GIS), open geospatial data, remotely sensed imagery, and other diverse sources

Main Results

• The provided text is an introductory chapter and does not present specific results from a study. It highlights the foundational importance and potential applications of Web-based GeoAI in earth resource management and climate change adaptation.

Contributions

• This chapter contributes by synthesizing the current state and potential of integrating web-based geospatial artificial intelligence (GeoAI) with remote sensing and GIS to provide accessible and cost-effective solutions for earth resource management and climate change adaptation, particularly for a broader user community with limited computing resources.

Funding

• Not mentioned in the provided excerpt.

Citation

@article{Roychowdhury2025Web,
  author = {Roychowdhury, Tuhin and Paul, Arati},
  title = {Web based geospatial artificial intelligence for earth resource management toward climate change adaption},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-29216-3.00003-5},
  url = {https://doi.org/10.1016/b978-0-443-29216-3.00003-5}
}