Digitising Farmlands: Building a Unified Geo-spatial Repository for Policy-Driven Agriculture

Modernising agriculture starts with digitising farmlands. Integrating satellite imagery and on-

ground measurements enables governments to make data-driven policies, optimize crop yields, and support landowners more effectively. Satellite data provides macro-level insights such as district-wide crop patterns, while on-field data offers granular details about individual land parcels and their owners.

Client Background

Our client is a leader in GIS data analytics and government policy consulting, working to create a centralised repository of farmland details. Their expertise bridges advanced geospatial technology with practical policy implementation.

Challenge / Problem Statement

• Satellite-based farmland boundaries, extracted via image processing, are approximate and often misaligned with ground realities.

• On-field measurements are more precise but collected for individual plots, lacking context about neighbouring farmlands.

• The two datasets differ in structure and granularity, making direct integration complex.

• Accurate, unified data is essential for policy formulation, subsidy allocation, and rural development.

  
  

Objectives

• Merge satellite and on-field farmland data into a single, accurate geospatial repository.

• Automate the matching process to reduce manual effort and ensure consistency.

• Enable hierarchical mapping from district to individual farmland level.

• Provide actionable insights for government policy and resource allocation.

Solution Overview

The solution converts each farmland’s geometry into a polyline, then transforms these into embedding vectors by considering attributes like edge angles, area, and other shape factors. A similarity search algorithm matches on-field and satellite data, enabling robust, automated alignment of farmland boundaries.

Key features:

• Automated shape matching: Reduces manual annotation and subjective errors.

• Embedding-based similarity: Captures nuanced geometric differences for accurate matching.

• Consistent labelling: Ensures uniform results across annotators and datasets.

  
  

Technical Challenges

• Massive data scale: With hundreds of thousands of farmlands per state, processing and storage requirements are significant.

• Hierarchical mapping: Data must be organized and matched at district, block, village, and farmland levels to ensure scalability and manageability.

• Shape complexity: Satellite and on-field polylines often have differing numbers of edges and geometric distortions, requiring sophisticated preprocessing

  
  

Key features:

• Edge simplification: Merges nearly collinear edges to harmonize shapes.

• Efficient algorithms: Utilizes optimized shape matching and embedding techniques to handle large datasets without compromising accuracy.

Architecture

The system is built on a robust geospatial technology stack:

• QGIS for visualization and manual corrections.

• GeoPandas for spatial data manipulation and analysis.

• Custom shape matching and embedding algorithms for automated data integration.

• Hierarchical data organization ensures efficient navigation from macro to micro levels.

Implementation Process

1. Data Collection & Preprocessing

   • Gather satellite imagery and on-field measurements.

   • Simplify polylines by merging nearly collinear edges.

2. Feature Extraction

   • Convert farmland boundaries into embedding vectors using geometric attributes.

3. Similarity Search & Matching

   • Run automated similarity search between datasets to identify corresponding farmlands.

4. Hierarchical Mapping

   • Organize matched data at district, block, village, and plot levels.

5. Validation & Integration

   • Validate matches, perform spot checks, and integrate into the centralised repository

     
     

Results & Impact

• Significant manual effort reduction: Automated matching cuts annotation time by over 70%, enabling teams to process thousands of farmlands daily.

• Consistent data quality: Standardized algorithms eliminate inter-annotator variability.

• Scalable solution: Hierarchical mapping supports seamless expansion across states and districts.

Insights

• Key learning: Preprocessing polylines to merge small-angle edges is critical for accurate matching, as satellite and on-field data often differ in edge count.

• Obstacle: Handling the sheer volume of data required robust, scalable algorithms and hierarchical processing.

• Best practice: Embedding-based similarity search proved more effective than traditional shape-matching for complex, noisy boundaries.

Conclusion & Next Steps

This project demonstrates how advanced geospatial analytics can unify disparate farmland datasets, driving efficiency and accuracy for government policy-making. The centralized repository lays the groundwork for smarter subsidies, targeted interventions, and comprehensive rural development. Next, the focus will be on integrating real-time crop monitoring and expanding coverage to new regions.