3D ULPIN Generation and vertical Property Mapping SYstem
Ministry of Rural Development · Space Technology · Software
One of the few land-records statements not already answered by a deployed government system, and the standards work exists to ground it — just settle your 3D ULPIN encoding scheme in week one, because that decision is the actual deliverable.
What it actually is
India's land records identify flat pieces of ground, which breaks down in cities where the same footprint holds twenty apartments, a basement car park and a metro tunnel underneath. The ask is a system that gives every one of those volumes its own unique identity code and maps who owns what at which height. It should build those volumes automatically from drone and LiDAR data rather than by hand.
What to build
A 3D cadastre pipeline that ingests LiDAR point clouds and drone-derived DSM/DEM with GIS parcel polygons, automatically extracts building envelopes from the point cloud, segments each envelope into floors by detecting horizontal planar breaks, delineates each floor into volumetric sub-parcels using imported floor plans, generates a structured 3D ULPIN string per volume that encodes its parent surface parcel plus its vertical position including negative levels for basements and subsurface utilities, runs topology validation for overlapping or floating volumes, and presents the result in a 3D web viewer where clicking any floor or basement volume returns its identifier and ownership attributes.
Smallest thing that wins the room
Load a point cloud of a real multi-storey building, watch it split into floors automatically, then click the fourth floor in the 3D viewer and show a generated 3D ULPIN that traces cleanly back to the surface parcel ID.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 68% of the 226 · #74 of 226 by expected field
A normal-sized field. Your idea has to be good, not miraculous.
Why: central ministry statements sat below the average.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
4/5This is a genuinely unsolved problem in Indian land administration rather than a rebuild of something deployed, the international standards work exists to anchor it, the demo is distinctive, and the Space Technology mislabel keeps land-records teams from finding it.
Feasibility
3/5Open point cloud data, DSMs and mature libraries like PDAL and Open3D make automated building extraction and floor segmentation genuinely achievable, and the LADM 3D cadastre standard gives you a model to conform to, but Indian building floor plans are not available as data anywhere, so the sub-parcel delineation inside each floor has to be drawn by hand or synthesised.
Innovation scope
4/5The description names inputs and desired capabilities but never specifies the ULPIN encoding scheme, the segmentation method or the data model, and 3D cadastre in India is genuinely unsettled ground, so the core design decision — what a 3D ULPIN actually is — is entirely yours.
Clarity
3/5The inputs and AI capabilities are listed cleanly, but the central deliverable is left undefined: it never says what a 3D ULPIN looks like, how it relates to the existing 14-character ULPIN, or how ownership rights attach to a volume, which is the whole substance of the problem.
Effort
HeavyPoint cloud processing, automated building extraction, floor segmentation, volumetric parcel construction, an identifier scheme, topology validation and a 3D web viewer is seven pieces, and the 3D viewer alone consumes far more time than teams expect.
Demo-ability
MediumA rotating 3D building where clicking a floor returns an ownership record is visually memorable and instantly legible to a non-technical judge, though the segmentation quality only holds up on the building you tuned it against.
In its favour
- Green flag: 3D cadastre is genuinely unsolved in India — you are not competing against an existing national system the way most land-record statements are
- Green flag: The ISO 19152 LADM and CityGML standards already define volumetric parcel modelling, so you can conform to published work instead of defending an invented data model
- Green flag: The theme is filed under Space Technology, so teams browsing Smart Cities or land governance will not surface it, and the title's typography suggests casual browsers will skip past it too
- Green flag: A 3D building you can rotate and click through is a far more memorable demo than the flat dashboards that dominate this ministry's block
Against it
- Red flag: Indian apartment floor plans do not exist as machine-readable data, so the step from a segmented floor to individual flat boundaries — the actual ownership unit — has to be hand-digitised and that limitation must be stated rather than hidden
- Red flag: The description never defines what a 3D ULPIN is, so you are inventing the central artifact and must justify your encoding against the existing ULPIN scheme when a DoLR judge asks
- Red flag: High-density Indian LiDAR coverage is scarce; teams often end up using a foreign city's open point cloud, which weakens the India-specific claim considerably
- Red flag: 3D web visualisation is a genuine time sink and a team that underestimates CesiumJS ends up with working segmentation and nothing to show it in
What you will be writing
- PDAL / Open3D point cloud segmentation
- PointNet++ or RANSAC plane fitting for floor detection
- CityGML / LADM 3D cadastre data model
- PostGIS 3D geometry with SFCGAL
- CesiumJS or deck.gl 3D web viewer
- Open3D building envelope extraction
- Land administration
- 3D geospatial modelling
- Urban property governance
Prior art to read before you start
3D cadastre and volumetric parcels · LiDAR building extraction · vertical property rights mapping
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.