Locational and aerial planning involves selecting optimal sites for infrastructure and managing land use through spatial analysis. The integration of AI, drones, GIS, and Remote Sensing makes planning more efficient, accurate, and sustainable.
Technological Synergy
Drones & RS- Satellites provide the macro-view (regional scale), while Drones provide the micro-view (site scale) with high-resolution imagery and LiDAR.
GIS acts as the central “brain” where all spatial data is layered, stored, and visualized.
AI processes the massive data from drones/RS to automatically detect patterns, classify land, and predict future trends.
Role of AI with GIS and RS in Planning
Automated Land Use Classification- Eg- ISRO’s Bhuvan portal uses AI to automate the Land Use Land Cover (LULC) mapping across India.
Infrastructure Corridor Optimization- Eg- The PM Gati Shakti platform integrates 200+ GIS layers to plan multi-modal connectivity projects across India.
Predictive Urban Growth- AI analyzes historical RS data to predict future urban sprawl, helping in proactive zoning.
Optimal Site Selection for Renewables- AI evaluates GIS layers like slope, solar radiation, and grid proximity to identify high-yield locations.
Traffic and Mobility Planning- AI analyzes real-time GIS traffic data to optimize the location of new flyovers or metro stations.
Environmental Risk Assessment- AI simulates flood or landslide scenarios based on RS topographical data to designate “no-build” zones.
Precision Agriculture Planning- AI analyzes multispectral RS data to determine the best locations for warehouses based on crop yield forecasts. Eg- FASAL project uses AI to forecast district-level yields.
Illegal Construction Detection- AI compares time-series satellite images to automatically flag unauthorized changes in land use.
Retail and Logistics Locational Planning- Eg- Amazon and Flipkart use spatial AI to decide the location of “Dark Stores” for 10-minute deliveries.
Role of Drones with GIS and RS in Planning
High-Resolution Cadastral Mapping- Drones create centimeter-level accurate maps for property titling.
3D Digital Twins of Cities- Drones use LiDAR to create 3D replicas of urban areas for detailed architectural planning.
Real-Time Construction Monitoring- Eg- NHAI has mandated drone surveys for all highway projects to monitor progress.
Disaster Damage Assessment- In areas inaccessible to RS due to cloud cover, drones provide immediate imagery for relief planning.
Mining Area Surveillance- Wg- using drones to prevent illegal iron ore mining.
Coastal Zone Management- Drones map shoreline erosion and mangrove health with high precision for environmental planning.
Transmission Line Planning- Eg- PowerGrid Corporation of India uses drones for the inspection and locational planning of pylons in hilly terrains.
Hydrological Planning- Eg- Under the Jal Shakti Abhiyan, drones map micro-watersheds for water conservation planning.
Challenges
High initial cost of technology and data processing infrastructure
Shortage of skilled geospatial and AI professionals
Data integration issues between multiple agencies due to different formats and standards delay implementation.
Regulatory restrictions on drone operations
Data privacy – High-resolution mapping of urban areas raises privacy issues.
Inadequate real-time data sharing due to low inter-agency coordination
Lack of decentralised planning capacity at local level – ULBs and PRIs lack funds and functionaries.
Way Forward
Implement National Geospatial Policy 2022 for open access and standardised datasets
Capacity building at state and local levels – Establish district-level geospatial planning units
Promote public-private partnerships for geospatial infrastructure
Integrate Bhuvan, Digital India Land Records, and urban GIS databases
Simplify drone regulations under Drone Rules 2021 for planning use
These measures can improve evidence-based spatial planning and resource optimisation in India.