Introduction

Locational and area planning involves selecting suitable sites and allocating land uses by analysing physical and human geography. With the rise of AI and drones, coupled with GIS and Remote Sensing (RS), new possibilities have emerged that are reshaping how such planning can be carried out.

Body

• How AI can be effectively used along with GIS and RS in locational and area planning
• Land Suitability Analysis: AI analyzes GIS–RS layers on soil, slope, rainfall, and vegetation to identify the most suitable land parcels. This ensures optimum site selection for agriculture, industries, or housing projects.
• Predictive Modelling of Hazards: Machine learning applied on RS imagery forecasts floods, droughts, or landslides in vulnerable terrains. Planners can thus avoid high-risk areas when allocating land uses.
  • Eg: LiDAR and GIS are used in Varanasi to model flood-prone zones under the 3D urban twin initiative.
• Urban Growth Forecasting: AI models use past RS data and GIS layers to project urban expansion patterns. This helps in designing transport networks and zoning policies proactively.
• Resource Allocation: By scanning spatial data, AI pinpoints underserved areas lacking basic amenities. GIS-based maps then guide the location of schools, health centres, or water supply.
• Change Detection: AI detects even subtle land-use changes like deforestation or illegal construction in RS images. Such insights help in conservation planning and regulatory action.
• Decision Support Systems: AI integrates large volumes of RS data into easy-to-use GIS dashboards. Planners get ready scenarios for sustainable land-use planning.
  • Eg: Real-Time 3D flood model of Varanasi offers over 75 GIS thematic layers for decision support.

How Drones can be effectively used along with GIS and RS in locational and area planning

• High-Resolution Mapping: Drones capture centimetre-level images of terrain and settlements. When fed into GIS, these maps improve precision in micro–level planning.
• Ground-Truthing of RS Data: Drone surveys validate satellite imagery by offering near-real observations. This enhances the accuracy of GIS-based land-use and resource maps.
• Monitoring of Land Use: Periodic drone flights detect encroachments, land degradation, or urban sprawl. GIS layers are updated in real time for corrective planning.
  • Eg: Maharashtra government launched a pilot project in Pune to conduct drone-based surveys of mining sites
• Infrastructure Layouts: Drone imagery helps trace the exact alignment for roads, canals, and pipelines. Overlays with GIS ensure minimal displacement and optimal land use.
• Disaster Assessment: Drones map flood plains, landslide scars, or cyclone-hit areas within hours. This real-time data feeds into GIS for relief distribution and rehabilitation planning.
• Agricultural Planning: Drones capture crop health and irrigation stress at field level. Combined with RS data, this guides location-specific planning for farm productivity.
  • Eg: Farmer in Odisha was honored for using drones to spray fertiliser across 640+ acres, improving land management efficiency.

Conclusion

The integration of AI, drones, GIS and RS strengthens locational and area planning by improving spatial accuracy and predictive capability. To sustain and expand these gains, India must invest in R&D and skilled human resources so that technology is applied effectively for informed and balanced planning.

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