Geoai-Driven Earth Observation for Sustainable Development

Contemporary Advances and Emerging Opportunities Across Scales
Erscheinungsjahr 2026
ISBN: 978-0-443-44428-9
Verlag: Elsevier Science

Buch, Englisch, 350 Seiten, Format (B × H): 152 mm x 229 mm

GeoAI-Driven Earth Observation for Sustainable Development: Contemporary Advances and Emerging Opportunities Across Scales presents a comprehensive overview of how Geographic Artificial Intelligence (GeoAI) is transforming Earth observation technologies to support the achievement of the United Nations Sustainable Development Goals (SDGs).

The book begins by outlining the evolution of Earth observation and the integration of machine learning and deep learning techniques that enhance data analysis capabilities. It addresses the challenges and ethical considerations involved in deploying GeoAI, emphasizing the importance of open-source tools, cloud-edge intelligence, and reproducibility in advancing the field. The text explores a wide range of GeoAI applications tailored to specific SDGs, including precision agriculture for food security, urban resilience, disaster risk reduction, forest and biodiversity conservation, and water resource management. It also highlights innovations in air quality mapping, clean energy monitoring, health and well-being, inclusive education, gender equality, sustainable economic growth, and infrastructure development. Additionally, the book discusses policy and governance frameworks that enable the effective use of GeoAI for sustainable development, alongside integrated scenario modeling to bridge science and technology in land-use and cover change. Further, it examines emerging GeoAI applications in groundwater quality, marine and coastal ecosystems, and regional security in the context of climate change.

This multidisciplinary volume equips researchers and practitioners with the knowledge and tools to harness GeoAI for addressing complex environmental and societal challenges.

Autoren/Hrsg.

Tripathi, Gaurav

Upadhyay, Ritambhara K.

Singh, Dhanendra K.

Pandey, Manish K.

Pal, Mahesh

Pandey, Arvind Chandra

Fachgebiete

• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
• Geowissenschaften Geologie GIS, Geoinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft
• Geowissenschaften Geologie Meteorologie, Klimatologie
• Geowissenschaften Geologie Geologie
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

Section 1: The Evolution of Earth Observation Technologies: Foundation, Innovation, and the Future Directions
1. State-of-the-Art of GeoAI for SDGs: Current Trends and Future Prospects Across Space and Time Scales
2. Introduction to GeoAI and Earth Observation
3. Machine Learning and Deep Learning Techniques for Achieving the Sustainable Development Goals
4. GeoAI: Revolutionizing Data Analysis in Earth Observation
5. Challenges and Ethical Considerations
6. The Future of GeoAI in Earth Observation: Anticipating Global Trends
7. Open-Source Tools, Cloud-Edge Intelligence, and Reproducible GeoAI for Earth Observation

Section 2: GeoAI Applications for Achieving Sustainable Development Goals
8. Precision Agriculture and Food Security for SDG 2 (Zero Hunger)
9. Urban Resilience for SDG 11 (Building Sustainable Cities and Communities)
10. Disaster Risk Reduction for SDG 13 (Climate Action)
11. Forest and Biodiversity Conservation: Achieving SDG 15 with GeoAI (Life on Land)
12. Water Resource Management for SDG 6: GeoAI Innovations GeoAI for Sustainable Air Quality Mapping, Modeling, and Planning in Support of SDG 3
14. GeoAI in Monitoring and Achieving Clean Energy for SDG 7
15. Advancing Health and Well-being: GeoAI Applications for SDG 3
Social Dimensions of GeoAI: Advancing Inclusive Education (SDG 4) and Gender Equality (SDG 5)
17. Sustainable Economic Growth and Infrastructure: GeoAI for SDG 8 and SDG 9
18. Policy and Governance: Enabling GeoAI for SDGs
19. Integrated Scenario Modeling for Land-Use/Cover Change: Bridging Science and Technology
20. AI Applications in Groundwater Quality and Resource Management
21. GeoAI in the marine and coastal ecosystem
22. GeoAI in the Regional security scenario: Emerging dynamics in the context of climate change

Über Autor(innen)

Pal, Mahesh
Prof. Mahesh Pal is currently working as a Professor in the Department of Civil Engineering, National Institute of Technology, Kurukshetra, India. He joined NIT Kurukshetra in 1994 and proceeded to doctoral studies under the Commonwealth scholarship scheme in 1999. He was awarded a Ph.D. from the University of Nottingham, Nottingham, U.K., in 2002. He has published extensively on land-cover classification, feature selection, and application of artificial intelligence techniques in various civil engineering applications. He was also awarded Commonwealth and Fulbright fellowships in 2008 and 2012, respectively, for further studies on hyperspectral remote sensing in the UK and the US. He is the editor of the International Journal of Remote Sensing and on the editorial board of the Remote Sensing Letters, an international journal, and a reviewer of several international journals.

Upadhyay, Ritambhara K.
Dr. Ritambhara K. Upadhyay is an Assistant Professor at the Department of Geology, School of Earth and Environmental Sciences, Central University of Himachal Pradesh, Dharmshala. She holds a Ph.D. in Geology with a specialization in Hydrogeology and Geoinformatics from Panjab University. Her expertise covers geology, hydrology, remote sensing and GIS, isotope geochemistry, and disaster management. Dr. Upadhyay has diverse professional experience of more than 20 years that includes international teaching at Jimma University, Ethiopia, and corporate experience as General Manager in Environmental Management Services, where she handled projects related to EIA, groundwater approvals, and mining. She is an active researcher with numerous publications in national and international journals and book chapters.

Tripathi, Gaurav
Dr. Gaurav Tripathi is a Post Doctoral Fellow at the Department of Civil Engineering, Indian Institute of Technology, Bombay. He earned his PhD in Geoinformatics from the Central University of Jharkhand. He studied Geo-Information Science and Earth Observation with specialization in Geo-Informatics from the International Institute for Geo-Information Science and Earth Observation (ITC), Netherlands, University of TWENTE, Indian Institute of Remote Sensing, Indian Space Research Organization (ISRO), Dehradun. He served as an Assistant Professor at the School of Agricultural and Environmental Sciences, Shobhit Institute of Engineering and Technology, Meerut, India, and the Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, India. His research interests include GeoAI, hydro geoinformatics, computational hydrology, disaster management, and microwave remote sensing.

Pandey, Arvind Chandra
Prof. Arvind Chandra Pandey received the Ph.D. degree in geology from the Department of Geology, University of Delhi, New Delhi, India, in 2001. He is currently a Professor with the Department of Geoinformatics, Central University of Jharkhand, Ranchi, India. He has been working in diverse application areas of Geoinformatics, viz, water resources, glaciology, natural hazards, urban environment, and forestry. He has completed several projects as a PI and a co-PI from the Indian Space Research Organization, Central Ground Water Board, Department of Science and Technology, and Ministry of Environment, Forest and Climate Change on aspects of Himalayan glacier study, Kosi flood hazards, groundwater targeting, national drinking water mission, arsenic contamination, national urban information system, wasteland mapping, forest fire forecasting, and desertification. Prof. Pandey was a recipient of the National Aeronautics and Space Administration-SERVIR Fellowship in 2013 to work on Himalayan Glaciers in Zanskar Valley, Jammu and Kashmir, India.

Pandey, Manish K.
Dr. Manish Pandey, a Research Associate Professor at MURC, Marwadi University, Rajkot, Gujarat, India, is passionate about blending geomorphology with the latest advances in artificial intelligence, remote sensing, and geographic information systems. He completed his undergraduate and postgraduate studies in Geography with specialisation in remote sensing and GIS at the University of Allahabad and earned his Ph.D. from Banaras Hindu University in fluvial geomorphology with support from prestigious CSIR research grants. With more than a decade of research experience, Dr. Pandey investigates earth surface processes, such as flooding, landslides, and groundwater dynamics, using cutting-edge AI techniques. Skilled in cartography and GIS software like ArcGIS and QGIS, and MATLAB- and Python-based GeoAI, he has also broadened his expertise into glaciology through training with the Geological Survey of India and ISRO. Above all, Dr. Pandey is dedicated to advancing GeoAI as a powerful tool for modeling and understanding the natural world.

Singh, Dhanendra K.
Dr. Dhanendra K Singh is currently working as an Assistant Professor at Sharda University, Greater Noida (U.P.), India. Prior to this, he worked as an Assistant Professor at the Suresh Gyan Vihar University, Jaipur, and Nirwan University, Jaipur. He earned his doctoral degree from the Indian Institute of Remote Sensing, Indian Space Research Organization, Dehradun, and HNBU, Garhwal. He also contributed to various projects at the Remote Sensing Applications Centre, Lucknow, GB Pant NIHE, Almora, and IISC Bengaluru. His areas of expertise are in Geoinformatics in snow/glacier studies, climate change, wetland dynamics, disaster management, mountain geography, and SDGs. He does have vast experience in glacier monitoring techniques on and off the field. He is associated with the Association of Polar Early Career Scientists (APECS) and the Indian Polar Research Network (IPRN) as a council member.