Published May 30, 2025 | Version v1
Book chapter Open

Applications of Artificial Intelligence in Geography Research

Authors/Creators

  • 1. Department of Geography, Dada Patil Mahavidyalaya, Karjat, Dist.- Ahilyanagar-414402, (MH), India.

Description

Artificial Intelligence (AI) is rapidly transforming the landscape of geographical research by enhancing the efficiency, scale, and precision of spatial analysis. From land use classification and climate modeling to urban planning and disaster risk assessment, AI techniques such as machine learning (ML), deep learning (DL), computer vision, and natural language processing (NLP) are revolutionizing the way geographical data is processed and interpreted. The integration of AI with Geographic Information Systems (GIS), Remote Sensing (RS), and Big Data platforms has enabled researchers to extract meaningful patterns from vast, complex, and often heterogeneous datasets.

This chapter provides a comprehensive overview of AI applications in geography, emphasizing both theoretical underpinnings and practical deployments. Key focus areas include land cover classification using convolutional neural networks (CNNs), spatial pattern detection via unsupervised learning, urban sprawl prediction using ensemble models, and integration of AI in climate change analysis and microclimate mapping. Furthermore, we discuss the challenges of interpretability, data bias, model generalization, and ethical considerations. Case studies from different regions illustrate the practical benefits and limitations of these applications.

The chapter concludes by outlining future directions, advocating for hybrid models, real-time AI-GIS integration, and inclusive geospatial data governance to address emerging challenges. The findings aim to bridge the gap between technological advancement and spatial science, contributing significantly to the evolving paradigm of AI-powered geographic research.

Files

8. Agastirishi Bharat Toradmal.pdf

Files (259.8 kB)

Name Size Download all
md5:d4bb779c218f50c355e55009cfa5acea
259.8 kB Preview Download

Additional details

References

  • 1. Abadi, M., et al. (2016). TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (pp. 265–283). 2. Ashraf, M., Anwar, S., & Umer, S. (2022). AI-driven flood prediction and mapping using Sentinel-1 data and deep learning. Remote Sensing Applications: Society and Environment, 25, 100683. https://doi.org/10.1016/j.rsase.2022.100683 3. Chen, X., & Zhang, Y. (2020). Deep learning for remote sensing image classification: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1324. https://doi.org/10.1002/widm.1324 4. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://arxiv.org/abs/1702.08608 5. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 6. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi.org/10.1126/science.aaf7894 7. Li, X., Zhou, Y., Asrar, G. R., Imhoff, M., & Li, X. (2017). The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Science of the Total Environment, 605–606, 426–435. https://doi.org/10.1016/j.scitotenv.2017.06.229 8. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1 9. Robinson, C., Hohenthal, J., & Maron, M. (2022). Citizen science, machine learning and remotely sensed imagery: Opportunities for ecological research and conservation. Biological Conservation, 266, 109422. https://doi.org/10.1016/j.biocon.2022.109422 10. Singh, A., Agarwal, P., & Kumar, A. (2021). Application of UAVs in precision agriculture: A review. Journal of Cleaner Production, 316, 128369. https://doi.org/10.1016/j.jclepro.2021.128369 11. Yuan, Q., Shen, H., Li, T., Li, Z., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. https://doi.org/10.1016/j.rse.2020.111716