Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Urban Analytics, Smart Cities and Climate-Responsive Planning shows how data and machine learning help cities plan for people and a changing climate. You learn to work with urban and geospatial data and apply AI to mobility and land-use analysis, service and infrastructure planning, and climate-responsive design — anticipating heat, flooding and resource stress. The course connects analytics to real planning decisions and the equity and governance they must serve. You finish able to reason about an AI approach to a climate-responsive urban-planning problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to urban analytics and smart cities — analysing urban data for mobility, land use and climate-responsive, resilient city planning.
📋 Course Objectives
1. Work with urban and geospatial data.
2. Analyse mobility and land use with AI.
3. Support infrastructure and service planning.
4. Plan for climate resilience and stress.
5. Address equity and governance in planning.
👥 Who Should Enroll?
• Urban planners and designers
• Smart-city and GIS professionals
• Sustainability and resilience analysts
• Students of urban planning
🚀 Key Learning Outcomes
• An understanding of AI in urban planning.
• A climate-responsive city perspective.
• An urban-analytics project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Foundations of AI for Urban Analytics & Smart Cities
Introduce urban analytics concepts and data‑driven city planning • Explain AI’s role in heat‑mapping, mobility, and climate adaptation • Map urban datasets and create indicator matrices using QGIS
Day 2 – Urban Heat Mapping & Land‑Use Intelligence
Analyze satellite‑derived temperature and vegetation indices in Google Earth Engine • Identify heat‑prone and climate‑vulnerable zones • Conduct land‑use intelligence workflows with Python and QGIS
Day 3 – Mobility Analytics & Climate‑Responsive Planning Dashboards
Perform mobility pattern analysis for smart‑city decision‑making • Visualize movement and spatial data with Kepler.gl • Build interactive dashboards that integrate heat, land‑use, and mobility indicators for climate‑responsive planning
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | QGIS |
| Covered Tool / Platform | Google Earth Engine |
| Covered Tool / Platform | Kepler.gl |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Google Sheets |
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