A software system that uses AI and 3D spatial data to generate unique 3D identification numbers for surface, multi-storey, and underground properties.
Ministry of Rural Development · Dept of land resources (DoLR) · Software
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Decomposed from what the description asks for. Nothing added.
Spatial Data Ingestion Engine
Imports and aligns drone imagery, LiDAR point clouds, GIS layers, building floor plans, and elevation models.
AI Floor & Building Segmenter
Automatically extracts building footprints and separates individual floor levels from 3D data and floor plans.
3D ULPIN Generator
Creates standardized, unique volumetric land identification numbers for surface, apartment, and subsurface property units.
Topology Validation Engine
Checks 3D spatial boundaries to detect overlaps and ensure valid volumetric property topologies.
3D Cadastral Viewer
Visualizes 3D property parcels, floor units, and underground structures in an interactive spatial interface.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
Evaluators will test whether your solution can process complex 3D data like LiDAR and floor plans to automatically segment buildings and generate unique 3D ULPINs. They will also look for AI-driven topology validation that prevents overlapping vertical property boundaries.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
The exact syntax or format required for the 3D ULPIN identifier?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Sample 3D point cloud, LiDAR, or floor plan datasets for training and testing?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Accuracy threshold for AI building extraction and floor segmentation?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
Each one is quoted from a gap in the brief, not a guess about your team.
Needs data you may not get
The problem requires integrating LiDAR point clouds, drone imagery, and building floor plans, but no datasets are provided.
No measurable target
The description demands AI-driven building extraction and topology validation but specifies no accuracy target or success threshold.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for spatial data ingestion engine. How would you build that?
Decoded from SIH26011 itself — Imports and aligns drone imagery, LiDAR point clouds, GIS layers, building floor plans, and elevation models. The brief asks for it by name.
The brief asks for ai floor & building segmenter. How would you build that?
Decoded from SIH26011 itself — Automatically extracts building footprints and separates individual floor levels from 3D data and floor plans. The brief asks for it by name.
The brief asks for 3d ulpin generator. How would you build that?
Decoded from SIH26011 itself — Creates standardized, unique volumetric land identification numbers for surface, apartment, and subsurface property units. The brief asks for it by name.
The brief asks for topology validation engine. How would you build that?
Decoded from SIH26011 itself — Checks 3D spatial boundaries to detect overlaps and ensure valid volumetric property topologies. The brief asks for it by name.
The brief asks for 3d cadastral viewer. How would you build that?
Decoded from SIH26011 itself — Visualizes 3D property parcels, floor units, and underground structures in an interactive spatial interface. The brief asks for it by name.
Where does your data come from — a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: evaluators will test whether your solution can process complex 3D data like LiDAR and floor plans to automatically segment buildings and generate unique 3D ULPINs.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will also look for AI-driven topology validation that prevents overlapping vertical property boundaries.
This is the evaluator read for your problem statement, decoded from the brief's own wording.