GeoAI for Cadastral, Built-Up & Road Feature Extraction

Abstract

This report presents deep learning-powered solutions developed by the AGSRT team to automate the extraction of key geospatial data from imagery. The focus is on three core use cases: road network detection from sub-meter satellite data, built-up area extraction, and cadastral boundary recognition. These high-accuracy, scalable solutions support land management, infrastructure development, and urban planning.

Introduction

Traditional geospatial mapping processes often require intensive manual effort, particularly for digitizing features across large areas. This report introduces three deep learning-driven approaches that significantly accelerate and enhance the accuracy of feature extraction across three essential layers: cadastral boundaries, built-up areas, and road networks.

Client Scope of Work

Automate cadastral boundary extraction from legacy map imagery.
Accurately identify built-up areas from satellite data with high spatial resolution.
Extract road networks from sub-meter satellite imagery to support transport planning and analysis

Solution Stack --

Cadastral Boundary Extraction

Using scanned cadastral maps as input, deep learning models are trained to detect plot boundaries automatically. This approach enables:

Precise boundary extraction from non-standardized, legacy maps.
Significant reduction in processing time compared to manual digitization.
Scalable feature extraction for municipalities, districts, or entire regions.

Built-Up Area Extraction

For urban growth and planning analysis, built-up features are extracted from satellite imagery with a spatial resolution finer than 1 meter. This model:

Identifies buildings and infrastructure footprints with high fidelity.
Differentiates between developed and undeveloped land.
Supports monitoring of urban sprawl and detailed land use analysis.

Road Network Extraction

Road features are extracted from satellite imagery with spatial resolution of less than 80 cm. This solution enables:

Detection of road alignments including major and minor roads.
Vector-ready outputs for integration into transport and planning systems.
Suitable for rural and urban transport network mapping.

Methodology Summary

Data Preparation – Input imagery or scanned maps are preprocessed to enhance feature clarity.
Model Training – Annotated samples are used to train deep learning models specific to each feature type.
Inference & Extraction – Trained models process the full datasets to generate spatial outputs in vector format.
Validation & Refinement – Outputs are validated for accuracy and refined to ensure readiness for operational use

Results

Cadastral Boundaries: Accurate and clean polygon layers representing land parcel divisions.
Built-Up Areas: Detailed building footprints, enabling volumetric and urban density analysis.
Road Networks: Continuous and coherent vector paths of road geometries across diverse landscapes.