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Harnessing AI and Machine Learning for Enhanced Geospatial Analysis
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Author(s): Rachna Rana (Ludhiana Group of Colleges, Ludhiana, India)and Pankaj Bhambri (Guru Nanak Dev Engineering College, Ludhiana, India)
Copyright: 2025
Pages: 46
Source title:
Recent Trends in Geospatial AI
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)and Houssem Chemingui (Brest Business School, France & Centre de Recherche en Informatique, Panthéon Sorbonne University, France
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DOI: 10.4018/979-8-3693-8054-3.ch002
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Abstract
In recent years, artificial intelligence (AI) and machine learning (ML) have changed geospatial analysis, allowing for more accurate, efficient, and scalable processing of massive volumes of geographical data. Traditionally, geospatial analysis depended on human-driven approaches and rule-based systems, which were frequently time-consuming and restricted in their capacity to handle large datasets. The combination of AI and ML has resulted in the development of revolutionary approaches like as deep learning, neural networks, and automated feature extraction, which have transformed the use of geographic information systems (GIS). This chapter investigates the critical role of artificial intelligence and machine learning in developing geospatial analysis in a variety of disciplines, including environmental monitoring, urban planning, disaster management, and agriculture. AI-powered models can now do predictive analytics, real-time data processing, and pattern identification in satellite images, LiDAR, and sensor networks.
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