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A4· Architecture and planning

AI for Urban & Regional Planning

You answer real planning questions about an Indian city in QGIS with AI help, from where a dark store should go to which wards lack services, and every layer you use carries its source.

Choose a planRead the syllabus
Who it is for
BPlan and MPlan students, and BArch students interested in urban work. QGIS labs need a Windows, Mac or Linux laptop; research and writing labs run on a phone or browser.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 4.6 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Planning Associate
  • GIS Analyst
  • Urban Research Assistant

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • QGIS 4.2 or LTR 3.44Free tierVerified
  • QuickOSM plug-in for QGISFree tier
  • QGIS AI assistant plug-ins (for example GeoAgent)Free tier
  • Deep-research mode in a free AI chatFree tier
  • Claude, ChatGPT or Gemini (free tier)Free tier
  • Saarthi sandbox (Saarthi coach and datasets)Saarthi sandbox

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · AI for Urban & Regional Planning

01Planning data in India2 lessonsWhere Indian planning data comes from, how old and how good it is, and how to set up QGIS for a city.
  1. Sources, ages and gaps15 min
  2. Set up QGIS for Mangaluru30 min
02AI inside QGIS2 lessonsUsing LLMs to write PyQGIS and build Processing models, and the risks of letting an agent run code.
  1. Expressions, PyQGIS, plug-ins and agents15 min
  2. Build a Processing model with AI help30 min
03Accessibility and catchments2 lessonsNetwork distances, isochrones and what road data quality does to your answer.
  1. From buffers to networks14 min
  2. Walking catchments for schools and clinics30 min
04Siting dark stores and last-mile hubs2 lessonsLocation analysis for quick commerce: demand, coverage, cost and the rules that limit where stores can go.
  1. How siting decisions are made15 min
  2. Score candidate sites with synthetic demand35 min
05Service gaps and equity2 lessonsWard-level indicators against planning norms, and the traps in comparing areas.
  1. Measuring service gaps15 min
  2. A ward-level gap map35 min
06Provenance and the planning report2 lessonsMaking an analysis others can trust, repeat and challenge, and disclosing how AI was used.
  1. Provenance, licences and disclosure14 min
  2. Assemble a reproducible planning brief30 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Dark-store and last-mile siting for Mangaluru from public data

    A quick-commerce operator (fictional) wants to open three dark stores in Mangaluru and asks where. Using OpenStreetMap roads, a gridded population layer and the Saarthi sandbox synthetic order-density layer, identify candidate sites in zones where a warehouse or commercial use appears permitted, compute two-wheeler catchments on the road network with one-way streets respected, and choose three sites with a tested maximum-coverage method. Report the coverage, the marginal value of a fourth site, the zoning status of each site with sources, the areas left uncovered, and the limits of the analysis.

    You hand in

    • QGIS project with road network, candidates, catchments and demand layers, all reprojected to EPSG:32643
    • Candidate-site table with a zoning judgement and source for each (or 'unverified')
    • Tested greedy coverage script with a hand-solvable test case, run for k = 2, 3 and 4
    • Maps: chosen sites with catchments, and uncovered demand
    • Four-page brief with recommendation, limits and AI disclosure
    • Provenance log and README that lets another student reproduce the result

    Data: Public: OpenStreetMap (ODbL) and a public gridded population layer. Synthetic: Saarthi sandbox order-density points for Mangaluru (not real customer data). The operator is fictional.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Ward-level service-gap analysis with a data-provenance log

    Choose one public service (primary schools, primary health facilities or public open space) and analyse its provision across Mangaluru's wards. Combine facility data from OpenStreetMap and at least one official list, ward boundaries from a documented source, population from the census or a gridded layer, and one URDPFI norm quoted from the official guidelines. Measure supply, walking access and norm-based shortfall per ward, show finer-grained access on a grid, flag wards where the data is unreliable, and write a brief that separates what the data shows from your recommendations.

    You hand in

    • Merged and de-duplicated facility layer with a record of duplicates reviewed
    • Ward table: population (with source), facilities, access share, norm requirement, shortfall, flags
    • Ward choropleth with hatched flagged wards, and a 250 m access grid map
    • Four-page brief with findings, limits and separately marked recommendations
    • Complete data-provenance log covering every dataset and derived number
    • Peer-reproduction record and five-minute defence

    Data: Public: OpenStreetMap, census tables or a gridded population layer, a published ward map, official facility lists where available, and the URDPFI Guidelines (MoHUA, 2015). No personal data; any identifiers in official lists are removed before analysis.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Mr D'Souza, a retired town planner who now advises a quick-commerce company on site selection. He wants to know where your population numbers came from, how old they are, and why your catchments ignore the one-way streets in the city centre.

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for AI for Urban & Regional Planning.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy