Build Various AI Models and Build Technology with Web Overhead Interface with Reports Including Mobile App (MoRD)

Context & Background

Road infrastructure planning at the Ministry of Rural Development (MoRD) requires regular overhead visual audits. AI-powered models can automate the parsing of satellite or drone overhead video feeds to identify road quality and plan rural connectivity.

Problems to be Addressed

Overhead imagery suffers from varying resolutions, shadows, and vegetation cover, making automated road tracking difficult.

Aims and Objectives

1. Build deep learning models for overhead road connectivity mapping.
2. Design web dashboards to visualize road maintenance scores.
3. Deploy mobile applications to report localized connectivity gaps.

Methodology

Semantic segmentation models (U-Net, SegNet) parse overhead visual assets. The pipeline identifies road segments, scores pavement quality based on surface texture features, and aggregates reports into a centralized MoRD dashboard.

Expected Outcomes

An overhead road inspection web dashboard, geo-spatial connectivity models, and field testing reports with rural development agencies.