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GeoAI for Deltas

Remote Sensing, Simulation, and Smart Governance.DE

Language EnglishEnglish
Book Hardback
Book GeoAI for Deltas Shubham Mahajan
Libristo code: 52872865
Publishers Springer, Berlin, January 2027
The book provides a comprehensive guide on applying GeoAI for the sustainable management of vulnerab... Full description
? points 469 b Coming soon Coming soon New New
70 841 Ft
Forthcoming Expected 11. 01. 2027 Expected 11. 01. 2027

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The book provides a comprehensive guide on applying GeoAI for the sustainable management of vulnerable delta regions. It integrates remote sensing, simulation, and smart governance frameworks. Also, it demonstrates how artificial intelligence, when combined with geospatial technologies, can transform monitoring, prediction, and decision-making in environments. Moreover, the book addresses challenges like flooding, coastal erosion, and urbanization by presenting cutting-edge AI-driven methods. It covers advanced remote sensing, machine/deep learning for geospatial analysis, flood risk prediction, coastal erosion modeling, water quality monitoring, and urbanization impact assessment. It also explores the creation of digital twins, AI-enabled early warning systems, and smart governance frameworks that include citizen science.

Additionally, it discusses ethical, legal, and societal considerations for responsible and sustainable GeoAI deployment. The book combines theoretical foundations with practical case studies across all chapters. It offers the audience actionable insights to enhance resilience, improve policy-making, and optimize resource allocation.

The book's beneficiaries include researchers, policymakers, engineers, planners, GIS specialists, and graduate students as an essential reference for leveraging AI and geospatial technologies for sustainable delta management.

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About the book

Full name GeoAI for Deltas
Language English
Binding Book - Hardback
Date of issue 2027
Number of pages 562
EAN 9783032331328
Libristo code 52872865
Publishers Springer, Berlin
Dimensions 168 x 240
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