SIH26012: AI-Based Automated Urban Parcel Mapping and Cadastral Feature Extraction System — SIH 2026 | Wynit

SIH26012: AI-Based Automated Urban Parcel Mapping and Cadastral Feature Extraction System — SIH 2026 | Wynit

What they'll ask you

13

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

What accuracy metric or target percentage counts as successful boundary extraction?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Where students can access standard sample drone imagery and DSM/DTM datasets for training?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
The exact GIS file formats required for output export compatibility?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

10 morethe ones a panel gets to if there is time

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.
The brief asks for ai feature extraction engine. How would you build that?
Decoded from SIH26012 itself: Segments parcel boundaries, building footprints, roads, and land-use types from orthorectified drone imagery. The brief asks for it by name.
The brief asks for topology and polygon generator. How would you build that?
Decoded from SIH26012 itself: Converts predicted raster boundaries into clean vector polygons while detecting overlapping or inconsistent geometries. The brief asks for it by name.
The brief asks for web-gis interactive dashboard. How would you build that?
Decoded from SIH26012 itself: Displays orthorectified imagery alongside extracted vector layers and allows users to manually inspect and edit shapes. The brief asks for it by name.
The brief asks for ground truthing integration module. How would you build that?
Decoded from SIH26012 itself: Imports GNSS survey data and field ground-truthing points to validate and refine generated cadastral maps. 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 check the automated extraction of parcel boundaries and building footprints from aerial imagery.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will also inspect the automated topology validation module for clean polygon generation and test the Web-GIS interface for interactive editing.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (364 words)

Background: Accurate and up-to-date urban land records are essential for effective land governance, urban planning, taxation, infrastructure development, and delivery of citizen-centric services. At present, preparation of cadastral maps and delineation of urban parcel boundaries is largely dependent on manual interpretation of drone imagery and field-based Ground Truthing (GT) activities. The process is time- consuming, resource intensive, and requires extensive human intervention for extraction of parcel boundaries, building footprints, road networks, and other cadastral features.Further, dense urban settlements, irregular parcel geometries, encroachments,overlapping structures, narrow access roads, and mixed land-use patterns create significant challenges in preparation of accurate parcel maps. Manual digitization and validation of parcel boundaries often lead to delays in completion of cadastral surveys and generation of urban land records.With availability of high-resolution orthorectified lmagery (ORl), Digital surface Models (DSM), Digital Terrain Models (DTM), and drone datasets, there exists significant potential for leveraging Artificial lntelligence (Al), computer Vision, and GeoAl technologies for automated extraction of cadastral features and preparation of preliminary urban Parcel maps. Description: The system should be capable of: . Automatic extraction of parcel boundaries . ldentification and delineation of building footprints . Detection of roads, pathways, and access corridors . Classification of land-use features in urban areas The proposed solution should utilize: . High-resolution Drone lmagery . Orthorectified lmagery (ORl) . DSM/DTM datasets . Existing GIS Parcel layers . Ground Truthing (GT) datasets . GNSS/CORS-enabled surveY data The platform should incorporate: 1. Al-based image segmentation models for parcel delineation. 2. Deep learning techniques for feature extraction and object detection. 3. Automated topology generation and parcel polygon creation. 4. Detection of overlapping or inconsistent parcel geometries. 5. Web-GlS visualization and editing interface. Expected Solution: The expected outcome is development of an Al-enabled automated cadastral mapping platform capable of significantly reducing manual efforts involved in urban parcel mapping and cadastral preparation. The final solution should: . Automatically generate preliminary urban parcel maps . lmprove speed and efficiency of cadastral surveys . Reduce manual digitization efforts . Enhance accuracy of parcel boundary extraction . Support Ground Truthing and field verification activities The solution should include: . Al/ML-based parcel extraction engine . GIS-ready cadastral outputs . Web-based visualization dashboard . Automated topology validation module