Bharat in Betastartup field guide
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No. 1250Problem statementBuilt environment

Predictive Analytics System for Early Detection of Land Acquisition Delays

Source

Source summary

The proposed AI/ML system should flag land-acquisition projects at risk of delay using historical and current approval, compensation, dispute, documentation, possession and rehabilitation data. Explainable project-level scores should identify contributing factors and support corrective recommendations, GIS views and district/state comparisons. Alerts, updated models, integration APIs and role-based audit trails are requested. Scope and technology tables remain placeholders in the saved source.

No. 564Problem statementBuilt environment

Al based system to develop a Digital Elevation Model of the areas to work out various tunnel alignments.

The challenge asks for an AI-assisted digital elevation model that compares possible tunnel alignments using terrain gradient, approach roads to tunnel portals, construction cost, time and other factors. Users should be able to toggle the factors that apply to a particular comparison.

No. 1170Problem statementBuilt environment

Platform for Inter-departmental cooperation (at city level) in Indian Cities, for sharing data & resources, unified phasing, planning and implementation of projects.

Create a city-level interdepartmental coordination platform registering agencies and personnel, sharing geolocated projects and available expertise/equipment, and managing tasks, schedules, reports and responsibilities. Identify projects sharing a site so agencies can phase work together, avoiding repeated disruption such as road reconstruction followed by utility installation. Include meeting/capacity-building support and separate internal, interagency and public discussion areas with appropriate access.

No. 1171Problem statementBuilt environment

Utilization of images for monitoring of progress of construction activities for building construction projects.

Develop ML-based progress monitoring specifically for building construction. Users upload site images and identify building count and activity, such as foundations, superstructure, façades or interiors. Identify the stage, compare previous images and flag mismatches between selected activities and imagery for correction. The source envisages different trained models for components, supporting government agencies that cannot visit every site frequently.