Prof. P K Garg

Role of Geospatial Technology in Intelligent Vision and Computing

Abstract:

Geospatial technology involves the integration of photogrammetry, remote sensing, GPS, LiDAR and GIS, which provides a strong foundation for intelligent vision and computing. By providing real-world images and coordinates, it allows us to map, understand, and interact with the physical world in three dimensions. Geospatial Artificial Intelligence (GeoAI) based computer vision systems automatically extract objects, perform land-use classification, and detect changes over time using high-resolution satellite images, drone, and video images. Remote sensing data processed by GeoAI algorithms facilitates risk modeling, helps map hazard-prone areas, and accelerates post-disaster damage assessments. Spatial computing, when merged with GIS data, can be used to create virtual models of cities. Real-time locational data combined with IoT sensors helps cities manage traffic, optimise transport, and address emergency needs. This intelligent computing allows architects, planners and decision-makers to simulate changes, like urban heat islands or flooding in an area, to authenticate the safety measures before actual implementation on the ground. Satellite and drone images combined with intelligent vision allow farmers to map crop health, analyse soil moisture, and optimise irrigation and fertilisers. Autonomous vehicles and drones require precise spatial data and real-time computing. Geospatial techniques provide accurate routing data and real-time awareness about the obstacles that enables AI-driven intelligent vision to safely navigate in physical environment. Geospatial technology provides the foundation for digital twins by integrating satellite imagery, GIS and GPS data. These can be used to digitizing land records and mapping property ownership to reduce fraud, managing resources, waste collection, and monitoring urban growth patterns.