Industry Pack · Land & Cadastral
Land & Cadastral
From complex historical records to trustworthy current-state data.
Land and cadastral digitization is about understanding the history of each plot: removing duplicates, setting aside expired records, reconciling conflicting sources, and resolving the current state the data must represent. The output is not a scanned page. It is a verifiable record set.
VALIDATED INTERNALLYThe most mature Industry Pack today, built and measured on real data under internal validation. Customer and project names stay private until publication is approved.
Inputs
Typical input data
Certificates and issuance files
Land-use right certificates and the issuance files behind them.
Change and transfer records
Transfers, mortgages, splits, and mergers recorded across decades.
Free-form documents
Petitions, submissions, notes, and records that do not follow a fixed form.
Merged PDFs and scans
Bundled PDFs, scanned images, and field photographs.
Legacy attribute and reference data
Old attribute tables, code lists, and reference data.
Challenges
Hard problems we solve
Many histories for one plot
A single current plot can carry multiple overlapping historical file sets.
Duplicates and expired records
Duplicate files, superseded records, and changes of owner or attributes.
Mixed-era documents
Documents from multiple periods, forms, and handwriting styles.
Source reconciliation
Sources must be compared to determine the current state.
Project-specific output
Output must match the project schema and regulations.
Outputs
Target outputs
Plot and entity records
Records built to the target schema.
Evidence links
Links from each value back to its source evidence.
Status classification
PASS / REVIEW / SOURCE ISSUE status for each record.
Error and exception reports
Reports for errors, exceptions, and reconciliation.
Integration-ready package
A data package ready for the land database.
Value
Where the value lives
The value is not in reading characters. It is in turning many layers of history into one current state of data that carries evidence and can be accepted.
How to start
Start with one representative dataset
Select a representative dataset, then lock the schema, business rules, critical fields, missing and conflicting-source rules, and acceptance criteria. Run a benchmark before scaling production.
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