Bharat in Betastartup field guide
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No. 1247Problem statementBuilt environment

An lntegrated GIS-based Digital Public lnfrastructure for Land Governance

Source

Source summary

The Land Stack prototype should organize rural and urban records around geo-referenced cadastral parcels and identifiers such as ULPIN. Ownership, registration, planning permissions, encumbrances and zoning form core layers; utilities, taxation, valuation and restrictions are additional layers. State-specific formats, languages, units and workflows must remain configurable. The submission should demonstrate sample-data integration, citizen services, open APIs, access controls and audit trails, plus a standards and architecture document. AI/ML and change detection are encouraged extensions.

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.