Land-use planning is the systematic assessment of land and water potential, alternatives for land use, and socio-economic conditions to select and adopt the best land-use options. The integration of technology makes land-use and areal planning more accurate, dynamic, and evidence-based.

Use of AI along with GIS & RS

1. Automated Land-Use Classification – AI processes satellite imagery and GIS layers for faster, accurate land-use/land-cover mapping. Eg– ISRO’s FASAL project for crop classification.
2. Predictive Urban Growth Modelling – AI analyses GIS–RS datasets to forecast urban sprawl, housing demand, and transport pressure. Eg– Bengaluru Smart City uses AI–GIS for traffic simulation.
3. Disaster Risk Assessment – AI integrates RS hazard maps with GIS to predict floods, landslides, droughts. Eg– Uttarakhand flood risk mapping with AI-enabled RS data.
4. Resource Optimization – AI processes GIS–RS data to identify optimal sites for renewable energy and infrastructure. Eg– MNRE Solar Atlas combined with AI for solar farm planning.
5. Climate Change & Environmental Monitoring – AI detects deforestation, wetland shrinkage, and land degradation from RS imagery layered on GIS. Eg– AI models tracking Amazon deforestation using RS data (Brazil).
6. Agriculture & Food Security – AI with GIS–RS predicts yields, crop stress, and pest infestations.

 Use of Drones along with GIS & RS

1. High-Resolution Mapping – Drones add centimeter-level accuracy to RS–GIS maps for micro-planning. Eg– Swamitva Yojana drone surveys for rural land records.
2. 3D City & Infrastructure Models – Drone imagery combined with GIS builds “digital twins” of cities. Eg– Amaravati 3D city model for planning.
3. Real-Time Disaster Monitoring – Drones provide live data layered on GIS hazard maps. Eg– Drone deployment in Assam floods for evacuation mapping
4. Precision Agriculture – Drones complement RS crop data with farm-level imagery for soil health, irrigation, and spraying. Eg– Drone spraying under PM-KUSUM scheme.
5. Asset & Environmental Monitoring – Drone imagery integrated with GIS tracks forests, coasts, pipelines, and solar parks. Eg– NTPC using drones for solar park inspections.

Challenges in Application of AI and Drones in Land-Use Planning

1. High Cost of Technology – Import dependence for drones and AI hardware raises expenses; unaffordable for local bodies and farmers.
2. Limited Skilled Workforce – Shortage of trained drone pilots, GIS analysts, and AI specialists in government and rural institutions.
3. Regulatory Hurdles – DGCA’s strict norms on drone flying (permissions, no-fly zones) restrict large-scale adoption.
4. Data Quality & Integration – Inconsistent cadastral maps, poor satellite resolution in some areas, and fragmented databases hamper AI accuracy.
5. Privacy & Security Concerns – Risks of surveillance misuse, sensitive land data leaks, and cyberattacks on AI systems.

Way Forward

1. Policy Support – Simplify drone regulations under DGCA; promote indigenous drone and AI manufacturing through PLI schemes.
2. Capacity Building – Establish training centres for drone pilots, GIS analysts, and AI specialists in planning departments.
3. Affordable Access – Subsidize drones for agriculture and rural land-mapping; encourage PPP models for cost-sharing. Eg- Drone Didi Initiative
   

India must move from manual surveys to intelligent mapping, ensuring technology becomes a people-centric enabler of sustainable land management.

Functional and sectional approach

AI

1. Functions

• Data-driven decision making
• Automation of tasks
• Predictive analytics and forecasting
• Personalisation
• Accuracy and precision
• Enhanced efficiency and productivity

2. Sections

• Agriculture
• Urbanisation
• Industries

Drones

1. Functions

• Capability for real-time surveillance and imaging
• High-resolution mapping and spatial data collection
• Access to difficult or hazardous terrains
• Rapid deployment and flexibility in operations

2. Sections

• Disasters