About Workshop
AI for Urban Analytics, Smart Cities and Climate-Responsive Planning is a 3-day hands-on workshop focused on applying AI and geospatial tools to urban analytics. Participants will explore QGIS, Google Earth Engine, Kepler.gl, Python, and Google Colab to analyze urban heat islands, land use patterns, mobility flows, and climate vulnerabilities.
Aim
The aim of this workshop is to equip participants with the skills to use AI, geospatial analysis, and urban data for smart city planning, climate adaptation, and sustainable urban development. Participants will learn to analyze urban heat, land use, mobility patterns, and environmental indicators to create actionable insights for city-scale decision-making.
What Participants Will Learn
- Introduce urban analytics concepts and data-driven city planning
- Explain AI applications in smart cities, climate adaptation, and urban resilience
- Teach participants how to work with satellite imagery, urban datasets, and geospatial data
- Understand urban heat islands, land-use intelligence, and climate-vulnerable zones
- Introduce Python-based geospatial and urban data analysis
- Guide participants in using QGIS, Google Earth Engine, and Kepler.gl for visualization
- Demonstrate mobility analytics and spatial pattern interpretation for urban planning
- Support scenario-based climate adaptation decision-making
Structure
📅 Day 1: Foundations of AI for Urban Analytics and Smart Cities
- Introduction to urban analytics and data-driven city planning
- Understanding smart cities and climate-responsive urban development
- Role of AI in urban heat mapping, mobility analytics, land-use intelligence, and climate adaptation
- Understanding urban datasets: satellite imagery, land use, road networks, population, mobility, and environmental indicators
- Basics of geospatial thinking for city-scale analysis
- Introduction to urban heat island effects and climate vulnerability in cities
- Developing a basic urban analytics framework for planning and sustainability decisions
🛠️ Hands-on:
- Hands-on 1: Urban Data Mapping and Indicator Matrix Preparation
- Hands-on 2: Basic City Map Visualization using QGIS
🧰 Tools Covered: QGIS, Python, Google Earth Engine, Google Sheets
📅 Day 2: Urban Heat Mapping and Land-Use Intelligence
- Understanding urban heat islands and their impact on climate resilience
- Introduction to satellite-based urban analysis using Google Earth Engine
- Working with land surface temperature, vegetation cover, built-up area, and land-use indicators
- Using Python for basic geospatial and urban data analysis
- Identifying heat-prone zones and climate-vulnerable urban areas
- Understanding land-use intelligence for sustainable planning
- Interpreting urban heat and land-use patterns for climate adaptation strategies
🛠️ Hands-on:
- Hands-on 1: Urban Heat Mapping using Google Earth Engine
- Hands-on 2: Land-Use and Climate Indicator Analysis using Python / QGIS
🧰 Tools Covered: Google Earth Engine, Python, QGIS, Google Colab
📅 Day 3: Mobility Analytics and Climate-Responsive Planning Dashboards
- Introduction to mobility analytics for smart city planning
- Understanding movement patterns, accessibility, congestion, and urban service reach
- Using Kepler.gl for interactive mobility and spatial data visualization
- Connecting heat, land use, and mobility indicators for climate-responsive planning
- Designing urban planning dashboards for decision-making
- Scenario thinking for climate adaptation: green cover, cool roofs, transit access, and walkability
- Preparing a final urban analytics workflow for research, policy, or professional planning use
🛠️ Hands-on:
- Hands-on 1: Mobility and Urban Pattern Visualization using Kepler.gl
- Hands-on 2: Mini Project: Climate-Responsive Urban Analytics Dashboard
🧰 Tools Covered: Kepler.gl, QGIS, Python, Google Earth Engine
Important Dates
Registration Ends
4:30 PM IST
Workshop Dates
2026-06-22
5:30 IST
5:30 IST
What You Will Gain
Outcomes
- Understand AI applications in urban analytics and smart city planning
- Analyze urban heat, land use, and climate vulnerability indicators
- Work with satellite imagery and geospatial datasets for city-scale analysis
- Identify heat-prone and climate-sensitive urban zones
- Use Python, Google Earth Engine, and QGIS for geospatial data processing and visualization
- Conduct mobility analytics using Kepler.gl to understand urban movement patterns
- Connect heat, land use, and mobility data for climate-responsive planning
Who Should Attend
- Students and postgraduate learners in urban planning, civil engineering, environmental science, geography, AI, and data science
- PhD scholars and researchers working in smart cities, urban resilience, and climate adaptation
- Academicians and faculty interested in urban analytics and AI applications in planning
- City planners, municipal engineers, and urban sustainability professionals
- Data science and AI learners interested in real-world geospatial and urban applications
- Industry professionals working in urban infrastructure, mobility planning, and smart city projects