Environment and ClimateData & AI

SEEDS: AI for Resilient Cities: Hyperlocal Risk Mapping for Inclusive Climate Action

Strengthening Climate Resilience through Predictive, Community-Led Risk Intelligence

~1,00,000
~1,00,000
families reached across 10 cities
85–88%
85–88%
accuracy in predicting heat, flood, and cyclone risks
1,650+
1,650+
homes insulated, resulting in 8–12°C cooler indoor temperatures
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Watch the story in 60 seconds (AI-generated video; visuals are illustrative)

Case at a Glance

About the Organisation

SEEDS (Sustainable Environment and Ecological Development Society) is a not-for-profit organisation working to protect lives and livelihoods through community-led, resilience-based solutions. Since 1994, SEEDS has supported vulnerable communities across disaster risk reduction, climate adaptation, resilient infrastructure, and nature-based solutions, with operations across India and Asia.

Problem Statement

India faces increasing climate and disaster risks from heatwaves and floods to cyclones and earthquakes disproportionately affecting people living in informal settlements. Disaster planning remains reactive and lacks granular, real-time data, making it difficult to identify the most vulnerable households and take early, targeted action.

Solution

SEEDS developed an AI-driven, hyperlocal risk mapping system that integrates satellite imagery, geospatial layers, environmental indicators, and household data to generate building-level, multi-hazard vulnerability scores. The system enables governments and communities to anticipate risks, prioritise interventions, and implement preventive, community-led resilience measures.

Read the full case study
Challenge
Reactive planning and coarse data limited the ability to protect the most climate-vulnerable households.

As climate risks intensified, SEEDS observed systemic gaps that limited effective disaster preparedness and climate resilience planning:

Limited Granularity of Risk Data
Existing disaster planning relied on city- or ward-level averages, obscuring household-level vulnerability in dense and informal settlements.
Reactive Planning Approaches
Interventions were typically triggered after disasters, resulting in higher loss of life, livelihoods, and assets.
Inefficient Resource Allocation
Without precise risk ranking, governments struggled to prioritise investments for the most at-risk households.
Exclusion of Community Knowledge
Planning processes often failed to integrate local insights and lived experiences into formal risk assessments.
SEEDS Challenge

These challenges underscored the need for an evidence-based, predictive, and inclusive model that could identify climate risks before disasters occurred.

Solution
Predictive, hyperlocal risk intelligence to enable preventive and inclusive climate action.

SEEDS built an AI-powered, multi-hazard risk mapping platform that combines:

  • Satellite imagery and land-use data
  • Meteorological and time-series climate data
  • Building footprints, roof types, and vegetation cover
  • Population density and household surveys
SEEDS AI Risk Mapping Platform

Using spatial analysis, supervised learning, and forecasting models, the system generates hyperlocal vulnerability scores for heat, floods, cyclones, and earthquakes. Interactive GIS dashboards support planners and field teams, while adaptive learning loops integrate sensor data and community feedback to continuously refine risk assessments.

Outcomes & Impact
From prediction to prevention, translating AI insights into measurable resilience outcomes
  • Reached approximately 1,00,000 families across 10 cities including Delhi, Nagpur, Chennai, and Bhubaneswar
  • Achieved 85–88% accuracy in identifying multi-hazard risks
  • Enabled insulation of 1,650+ high-risk homes, reducing indoor temperatures by 8–12°C
  • Improved efficiency of resource targeting by 40%
  • Increased community awareness by 72–75% through AI-driven advisories
  • Supported integration of AI insights into Heat Action Plans and Flood Preparedness Plans

Technology Stack

Tools/ Techniques Used For What It Enabled Category
Satellite Imagery & Geospatial Layers Risk mapping Building-level vulnerability assessment Open Source
Python, TensorFlow, Scikit-learn Model development Spatial analysis and supervised learning Open Source
QGIS Visualisation Interactive GIS dashboards Open Source
GeoJSON Standards Data exchange Interoperable mapping and analysis Open Source
TensorFlow, PyTorch, OpenCV Model development Training and deployment of CV models Open Source
OpenStreetMap (planned) Base imagery Imagery-agnostic scalability Open Source

Key Project Learnings

01
Granular Data Changes Outcomes

Building-level risk insights enabled precise targeting, leading to measurable reductions in heat exposure.

02
Prediction Enables Prevention

Shifting from reactive response to predictive planning improved preparedness and reduced downstream losses.

03
Community Feedback Strengthens Models

Integrating local inputs improved accuracy, trust, and adoption of AI-driven advisories.

Potential for Wider Adoption

Sector Adaptability of the Solution
Government Systems Integration into Heat Action Plans and disaster management systems
Urban Local Bodies Reusable AI backbone for early learning interventions
Civil Society Organisations Community-led climate adaptation and preparedness initiatives

See it in Action

Solution Video
Solution demo

This video features Trevor Noah visiting SEEDS in New Delhi, where AI is used to assess heat risk at the level of individual homes using satellite imagery and real-time data. It demonstrates how predictive models identify vulnerable households, estimate indoor temperatures during heatwaves, and guide simple interventions such as roof insulation to reduce heat exposure and improve community resilience.

Solution Video
User Testimony 1
This video follows a SEEDS project manager working in East Delhi, showcasing how AI is used to identify and prioritize vulnerable communities affected by extreme heat. It demonstrates how data-driven insights enable targeted outreach, while community-driven solutions and volunteer efforts help families adopt simple practices to prepare for and respond to heat-related risks, ultimately strengthening disaster resilience.

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