Bharat in Betastartup field guide
← Back to the atlas
No. 1251Problem statementBuilt environment

Intelligent Land Record Digitization and Validation System

Source

Source summary

The challenge asks for AI-assisted digitization of scanned, handwritten and legacy land records across Indian languages. OCR, computer vision and NLP should extract structured parcel and ownership fields, validate them against rules and other databases, detect duplicates and assign confidence scores. Low-confidence fields must enter human verification. LRMS, DILRMP, GIS and cadastral integration, secure repositories and audit trails are required; referenced scope/technology tables are absent from the capture.

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.