Centram Centre for Applied Artificial Intelligence

AI ASSISTED ANALYTICS AND AUTOMATION COURSE FOR LANDS AND HOUSING MANAGEMENT

Course Overview

This course is designed for national land agencies, cadastral offices, housing authorities, urban planners, valuation/tax units, dispute resolution bodies, tenure regularisation teams, environmental & social safeguards units, surveyors, registry IT teams and the data scientists/engineers who support them.

Course title

AI‑Assisted Analytics & Automation for National Lands & Housing Management

Target audience

Audience: Land registries/cadastral offices, housing authorities, urban planners, land valuation and property tax units, surveyors, grievance & dispute resolution officers, tenure regularisation teams, environmental/social safeguards staff, GIS/IT teams, M&E specialists, and data teams supporting land & housing systems.

Course learning outcomes

By course end participants will be able to:

 Design auditable, interoperable data pipelines linking cadastre, land registry, valuation/tax, planning layers, survey data, satellite/UAV imagery, housing inventories and citizen complaints.

  1. Apply computer vision, geospatial time‑series and NLP to detect encroachment, illegal subdivision, informal settlements growth, tenancy changes and non‑compliance; and generate evidence packages for field verification and legal processes.
  2. Build decision‑support tools for parcel valuation, property tax analytics, allocation of housing subsidies, priority setting for tenure regularisation and land acquisition compensation with safeguards.
  3. Operationalise digitised workflows (e‑conveyancing, e‑payments, automated notices, work‑order generation for field surveys) while preserving legal validity, chain‑of‑custody and access controls.
  4. Implement governance: LADM/standards, data sharing agreements, FPIC and safeguards for customary tenure and vulnerable groups, privacy, model‑risk management, auditability and grievance/redress.

Course Duration

The course duration is 2 weeks’ intensive physical boot camp

Course Content

Introduction: sector goals, stakeholders & data landscape

  1. Objectives: Map national land & housing policy goals (tenure security, efficient markets, revenue mobilisation, planning, resettlement) to analytics use cases and stakeholders.
  2. Topics: Institutional actors (land registry, survey, planning, housing, valuation, courts), typical workflows (survey → registration → valuation → taxation → transfer), key datasets and metadata, LADM (ISO 19152) overview.
  3. Lab: Problem scoping exercise — pick a priority (e.g., accelerate formalisation of 50k informal parcels; reduce property tax gap by X%) and map data, decisions and KPIs.

Legal & ethical frameworks, tenure types, safeguards & FPIC

  1.  Objectives: Understand legal limits, tenure diversity (statutory vs customary), land acquisition rules, compensation frameworks, and safeguards for vulnerable groups
  2. Topics: Land law basics, conveyancing rules, registration admissibility, FPIC, eviction protections, gendered tenure issues, privacy and data protection, public access vs confidentiality, dispute resolution processes.
  3. Lab: Create a data‑access & classification matrix distinguishing public metadata vs restricted records (juvenile/compensation cases, sensitive tenure claims) and draft FPIC/consent requirements for field mapping.

Data ingestion, normalization & provenance for cadastral systems

  1. Objectives: Ingest and harmonise vector (parcel fabrics), raster (imagery/DEMs), survey measurements, registry documents and logs with provenance and audit trails.
  2. Topics: Parcel fabric models, parcel identifiers, cadastral survey formats (DXF/GPX/SHAPE), georeferencing and datum transforms, deed/OCR ingestion, versioning, immutable logs and provenance (hashing, append‑only stores), interoperable APIs (WFS/ WMS/ WCS).
  3. Tools/patterns: PostGIS/GeoPackage, GDAL/OGR, GeoServer, ETL with Python, DVC/MLflow for provenance.
  4. Lab: Build an ETL to ingest parcel boundary shapefiles + deeds (PDFs), normalise attributes, link deeds to parcels and create provenance logs.

Remote sensing & parcel change detection

  1. – Objectives: Detect land‑use change, subdivision, encroachment and informal settlement growth using time‑series EO and UAV imagery.
  2. Topics: Sentinel/Planet/High‑res imagery use, change detection algorithms, semantic segmentation for built footprint detection, sub‑parcel boundary probability mapping, uncertainty quantification and sample‑based validation.
  3. Tools: Google Earth Engine, OpenCV, TensorFlow/PyTorch (UNet/Mask R‑CNN), QGIS.
  4. Lab: Implement a time‑series change detection pipeline to flag recent encroachments or new informal settlements against cadastral fabric and produce verification GIS layers.

Parcel linking, entity resolution & beneficial ownership

  1.  Objectives: Resolve identities across registries (owners, companies, titles) and detect complex ownership/linkage patterns that affect valuation and compliance.
  2. Topics: Record linkage methods, fuzzy matching, corporate ownership chains, PEP/sanctions lists integration, beneficial ownership tracing, maintaining privacy for legitimate owners.
  3. Tools: Dedupe/RapidFuzz, Neo4j/NetworkX for link analysis, Elasticsearch for fuzzy search.
  4. Lab: Link registry title records to company registries and payment records, build a simple ownership graph and flag unusual ownership patterns (frequent transfers, nominee structures

Valuation, property tax analytics & revenue optimisation

  1. Objectives: Build valuation models, estimate tax gaps, and propose data‑driven collection/targeting strategies while ensuring equity.
  2. Topics: Mass appraisal approaches (hedonic, ML‑based), spatial hedonic models, bias/ fairness in valuation, valuation appeals, segmentation (residential/commercial), property tax compliance analytics, leak detection.
  3. Tools: scikit‑learn, XGBoost/LightGBM, spatial regression, GeoPandas.
  4. Lab: Build a mass appraisal model for a sample municipality, estimate under‑taxed parcels and propose an evidence‑backed revenue recovery plan with safeguards for vulnerable households.

Housing inventory, allocation & subsidy targeting

  1. – Objectives: Use administrative and survey data to create housing inventories, design targeting rules for subsidies, and simulate allocation scenarios.
  2. Topics: Housing stock mapping, eligibility rules, proxy targeting vs means testing, avoiding exclusion errors, geographic prioritisation, simulation of allocation outcomes, grievance/appeals mechanisms.
  3. Tools: Databases and dashboards, targeting algorithms, small‑area estimation for under‑surveyed locales.
  4. Lab: Create a housing inventory from registry + household survey samples and prototype a targeting algorithm for a housing subsidy with built‑in appeals flow.

Automated workflows: e‑conveyancing, notifications & evidence packets

  1. Objectives: Automate parts of registration and transaction workflows: e‑filing, document verification (OCR/NLP), automated notices, and evidence package assembly for QCs or field teams.
  2. Topics: e‑signatures & legal validity, OCR/NER for deeds and plans, automated checks (mortgages, encumbrances), payment reconciliation, secure delivery and audit logs, chain‑of‑custody for digital evidence.
  3. Tools: Tesseract/OCR, spaCy, digital signature platforms, secure document stores.
  4. Lab: Build a prototype e‑conveyancing workflow that ingests deed PDFs, extracts metadata, runs rule checks and generates an evidence packet for registrar review.

Dispute detection, grievance triage & participatory mapping

  1.  Objectives: Integrate citizen complaints, participatory mapping and grievance mechanisms with analytics to prioritise field verification and dispute resolution.
  2. Topics: Hotspot detection from complaints, triage scoring, participatory mapping tools (OpenStreetMap, community GPS), confidentiality for claimants, escalation pathways, mediation supports.
  3. Tools: RapidPro/FrontlineSMS for citizen input, QGIS, dashboards for triage.
  4.  Lab: Ingest citizen complaints and participatory map submissions, run triage to prioritise disputed parcels for field verification and produce a case file template for mediators.

 Risk & resilience analytics: flood, landslip, climate & spatial planning

  1.  Objectives: Integrate environmental risk layers into land use planning, compensation planning, resettlement and building permits.
  2. Topics: Floodplain mapping, slope stability, zoning compliance, climate impact scenarios, relocation planning, compensation estimation under uncertainty.
  3. Tools: DEM processing, hydrological models, Google Earth Engine, spatial multi‑criteria analysis.
  4. Lab: Produce risk overlays for a housing estate (flood/landslide) and propose risk‑informed zoning/relocation options with cost estimates and stakeholder mapping.

Operationalisation, standards, MLOps & governance

  1. Objectives: Deploy production systems, ensure interoperability and establish governance: LADM adoption, APIs, MLOps, monitoring, model cards and audit trails.
  2. Topics: LADM implementation patterns, WFS/WMS/CSW APIs, model versioning, drift detection, tamper‑evident logging for legal processes, data sharing agreements, procurement clauses and vendor controls.
  3. Tools: PostGIS, GeoServer, Docker/Kubernetes, MLflow, Evidently/WhyLabs.
  4. Lab: Deploy a parcel verification pipeline (change detection → flag → evidence packet → verifier dashboard) with versioned models and logging for audits.

Ethics, tenure equity, safeguards & capstone

  1. Objectives: Address tenure equity (gender, customary rights), safeguards for dispossessed communities, procurement/contract clauses and present capstones.
  2. Topics: Gendered and customary tenure safeguards, FPIC, grievance redress, transparency vs secrecy (security of vulnerable holdings), procurement for vendor risk mitigation, public communication and trust building.
  3. Capstone: Teams deliver a reproducible pipeline (e.g., encroachment detection + verification workflow; mass appraisal + property tax recovery plan; housing subsidy targeting + appeals workflow; e‑conveyancing prototype + legal SOP) plus governance, safeguards and a demo.

 Capstone project structure

  1. Problem selection, stakeholder mapping, data assembly & baseline KPIs
    – Week 2: Pipeline & prototype implementation (ingest → detection/analysis → UI/workflow)
  2. Evaluation, legal/safeguards statement, SOPs and presentation
  3. Deliverables: reproducible code repo + Dockerfile, provenance logs, evaluation report, model card/AIA, SOPs for field/verifier/registry use and a short policy/communications brief.

Operational KPIs & evaluation metrics

  1. Tenure outcomes: # of parcels regularised, reduction in pending transfers, gender parity in registered ownership.
  2. Revenue & valuation: increase in assessed base, property tax gap reduction, accuracy of mass appraisal (MAE).
  3. Efficiency: time‑to‑register, time‑to‑issue title, % e‑conveyanced vs manual, time from complaint to field verification.
  4. Spatial accuracy & detection: precision/recall for encroachment detection, change detection timeliness, percentage of verified flags.
  5.  Safeguards: FPIC compliance rates, grievance resolution time, number of adverse outcomes linked to automation.

 Recommended tools, libraries & datasets

  1. Geospatial infra: PostGIS, GeoServer, GeoPackage, QGIS, OpenLayers/Leaflet
  2. Remote sensing & imagery: Google Earth Engine, Sentinel/Landsat, Planet (where available), UAV/orthomosaic tools, PDAL, GDAL
  3. CV & ML: OpenCV, TensorFlow/PyTorch (UNet/Mask R‑CNN), scikit‑learn, XGBoost/LightGBM
  4. NLP & OCR: Tesseract/PaddleOCR, spaCy, Hugging Face transformers
  5. Standards & models: LADM (ISO 19152), OGC services (WFS/WMS/CSW), INSPIRE concepts where relevant
  6.  Graph & linking: Neo4j, NetworkX, Elasticsearch, RapidFuzz/dedupe
  7. MLOps & monitoring: Docker/Kubernetes, MLflow, Evidently/WhyLabs, Grafana
  8. Citizen engagement & data collection: ODK/KoBo, RapidPro/FrontlineSMS, OpenStreetMap tools
  9.  Synthetic/sample data: generators for parcels/transactions and anonymised deed corpora for labs

Key risks, safeguards & mitigation

  1. Tenure vulnerability & dispossession risk: strong FPIC, legal review before enforcement, human verification before adverse actions, safe handling of claimant identities.
  2. Privacy & sensitive location data: limit public dissemination of ownership of vulnerable households, geomasking for public outputs, strict RBAC and secure enclaves.
  3.  Gender & customary tenure biases: explicitly track and protect female and customary claims, ensure participatory mapping and remedial measures for exclusion errors.
  4. Legal admissibility & chain of custody: immutable logs, hashed evidence packets, documented workflows for digital evidence and field verification to support adjudication.
  5. False positives & social harm: conservative thresholds for automated flags, mandatory field verification, transparent appeals and grievance mechanisms.
  6.  Political/land conflict risks: multi‑stakeholder oversight, transparent criteria for targeting and acquisition, third‑party mediation options.
  7. Vendor/procurement risk: require reproducibility, training‑data disclosure, source‑code escrow, indemnities and clear data‑use limits.
  8. Data quality & coverage gaps: invest in sample validation, transparency about uncertainty, conservative decision policies where data sparse

Practical lab/project ideas

  1. Encroachment detection pipeline: satellite time‑series → change detection → parcel overlay → evidence packet for verifier.
  2. Mass appraisal demo: build hedonic/ML valuation model for a municipality and use it to estimate property tax potential & protection pathways for vulnerable households.
  3. E‑conveyancing prototype: OCR deed ingestion, automated checks and registrar dashboard with audit logs.
  4.  Housing subsidy targeting: integrate registry + household survey + spatial poverty proxies and build a targeting+appeals workflow.
  5. Participatory mapping & grievance triage: ingest community maps and complaints, prioritise field checks and produce mediation packets.