How can Artificial Intelligence (AI) and drones be effectively used along with GIS and RS techniques in locational and areal…

The convergence of Artificial Intelligence (AI) and drones with Geographic Information Systems (GIS) and Remote Sensing (RS) creates a robust spatial intelligence framework, transforming locational and areal planning through real-time data acquisition and predictive modelling.

Urban Sprawl and Environmental Zoning AI-powered processing of drone imagery allows automated land-use/land-cover (LULC) classification and change detection, enabling real-time monitoring of urban sprawl and encroachment. Concurrently, integrating multi-spectral RS satellite data with GIS layers supports digital elevation modelling, slope stability analysis, and disaster-prone zone identification (e.g., floodplains and seismic belts), ensuring climate-resilient master planning.

Infrastructure Siting and Smart Governance Machine learning algorithms deployed on GIS platforms perform predictive multi-criteria decision analysis to determine optimal sites for linear and areal infrastructure, including highways, reservoirs, and industrial corridors. In urban governance, AI-driven spatial analytics support MoHUA’s Smart Cities Mission and AMRUT scheme through real-time utility mapping, traffic corridor optimisation, and automated property-tax geo-tagging.

Rural and Natural Resource Management Drone-based LiDAR and high-resolution photogrammetry generate precise micro-topographical data essential for watershed planning, drainage network design, and precision agriculture zoning. This integration facilitates accurate cadastral mapping and land-titling under initiatives like the SVAMITVA scheme.

Challenges and Way Forward Key bottlenecks include spatial data security, platform interoperability issues, the digital divide across local bodies, and regulatory constraints under DGCA drone rules. Streamlining DGCA airspace approvals, enforcing National Geospatial Policy (2022) data-sharing standards, and building institutional technical capacity will ensure seamless spatial analytics for balanced regional development.