Q.15 How can Artificial Intelligence (AI) and drones be effectively used along with GIS and RS techniques in locational and areal planning? (UPSC CSE 2025, GS PAPER-1)(Answer in 250 words,15 marks)

THE INTRODUCTION:

Artificial Intelligence (AI) and drones, integrated with Geographic Information Systems (GIS) and Remote Sensing (RS), serve as transformative tools for locational and areal planning, enabling data-driven, precise, and dynamic decision-making for urban, rural, and disaster management.

THE BODY:

Components and integration:

• • Data acquisition & fusion: Satellite RS provides macro-level, temporal data for entire regions, while drones supply high-resolution, real-time imagery of local areas. AI algorithms process and integrate these diverse datasets, denoising, stitching images, and extracting meaningful spatial features.
  • GIS as central platform: GIS organizes, analyzes, and visualizes layered geospatial data of population density, infrastructure, terrain, and land-use (serving as the nervous system for planning and management).

Applications in locational and areal planning:

Locational planning:

• • Site selection models: AI-driven analytics and GIS-based location-allocation models determine optimal sites for public healthcare, schools, transport, and disaster shelters using multi-variable analysis (e.g., travel time, population needs, hazard zones).
  • Urban infrastructure: Drones facilitate surveys for electricity grids, bus depots, and EV charging stations, with AI optimizing their placement based on traffic flows, land prices, accessibility, and proximity to amenities (e.g., PM Gati Shakti planning framework).
  • Example: Jaipur used drone surveys in urban planning to map development zones and identify areas needing infrastructure, reducing survey costs and improving accuracy.

Areal planning:

• • Land management and zoning: AI automates land-use and land-cover classification from drone and RS imagery, detects encroachments, maps floodplains/wetlands, and assists in slum upgradation.
  • Environmental design: Drones and RS combined with AI support watershed mapping, buffer creation for ecological zones, and micro-irrigation planning using geospatial analysis of soil, slope, vegetation, and hydrology layers.
  • Precision agriculture: Crop-stress mapping and input optimization are achieved through drone-based RS data and machine learning for targeted intervention.

Monitoring, compliance, and disaster management:

• • Real-time monitoring: Drones and AI enable continuous auditing of infrastructure projects, property titling, and asset mapping, enhancing transparency (e.g., SVAMITVA for Indian land records).
  • Disaster response: AI-driven drones rapidly map areas affected by floods, earthquakes, or landslides; GIS integrates these maps for resource allocation and post-disaster recovery.
  • Change detection: Automated detection of illegal activities (mining, deforestation) and assessment of environmental changes reduce manual effort and speed up enforcement.

THE CONCLUSION:

AI-driven drones integrated with GIS and RS revolutionize locational and areal planning, making spatial governance more continuous, accurate, and inclusive. Their synergistic use reduces costs, enhances resilience, and ensures optimal management of resources for sustainable development and disaster preparedness.