Challenge 01
Social Impact & Sustainability
Early Disaster Awareness
See the risk before it hits. Turn scattered data into early action.
Build an AI-powered geospatial intelligence platform that brings together public data to detect emerging disaster risks, starting with flash floods. Map the communities and critical infrastructure at risk, and turn it into early, evidence-backed action.
Who should apply
ClimateTech, GeoAI, AI/NLP, data-platform, civic-tech, drone and weather or water-IoT startups.
What teams can build
- Risk intelligence dashboard
- AI risk-signal detection
- Geospatial maps
- Source-linked alerts
- Vulnerability indicators
- Response recommendations
Problem and Expected Outcomes+
The problem
Information on flash floods, extreme rainfall, heatwaves and other disasters is scattered across weather bulletins, government advisories, news reports, maps and community updates. NGOs, volunteers and local responders often see risks too late and struggle to identify who needs help first.
Why it matters
Flash floods and extreme weather are becoming more frequent. A single, clear view of emerging risks and vulnerable communities helps responders act hours earlier, and creates a platform any region can adopt.
What to build
- Aggregate public data and use AI to detect emerging risk signals, with flash floods as the lead use case.
- Map risk hotspots, vulnerable communities and critical infrastructure such as hospitals, schools and roads, including cascading impacts on essential services.
- Help community responders prioritise preparedness and relief with transparent, source-linked insights.
Expected outcomes
- Working prototype: a dashboard aggregating 3+ public data sources with AI risk-signal detection.
- Measured result: warning lead time and accuracy on a past flash-flood event.
- Interactive risk map with vulnerable communities, critical infrastructure and cascading impacts.
- Prioritised, source-linked action list for NGOs and responders.
Challenge 02
Sustainable Agriculture & Food Systems
Precision Crop Health
See crop trouble before it spreads. Treat only where it matters.
Use AI with drone, satellite and smartphone images and field sensors to detect crop stress, pests and disease early and guide precise action, so small farmers lose less yield and use less water and chemicals.
Who should apply
AgriTech, agri-drone, GeoAI and remote-sensing, computer-vision and farm-IoT startups.
What teams can build
- Crop-health detection models
- Plot-level risk maps
- Farmer-facing apps
- Precision-treatment advice
- Drone-operator interfaces
Problem and Expected Outcomes+
The problem
Small farmers usually notice pests, disease and water stress only after damage is visible, which leads to yield loss and blanket spraying of chemicals and water. Drone, satellite and phone imaging combined with AI can spot problems days earlier, plot by plot.
Why it matters
Smallholders grow most of our food but face rising pest and climate pressure. Early, plot-level crop intelligence raises yields, cuts chemical and water use, and makes precision farming affordable for millions of farmers.
What to build
- Detect crop stress, pests or disease early from drone, satellite, multispectral or smartphone images plus sensor and weather data.
- Pinpoint exactly where action is needed.
- Guide farmers or spray-drone operators to treat only those spots, in Telugu, at a cost small farmers can afford.
Expected outcomes
- Working prototype: AI crop stress, pest or disease detection on real images with an action map or spray guidance.
- Measured result: detection accuracy, area flagged early, estimated chemical and water saved.
- Farmer-friendly delivery (Telugu, voice or WhatsApp) or a drone-operator interface.
- Cost per acre and per farmer.
Challenge 03
Clean Energy & Circular Economy
Zero-Waste Communities
Waste is a resource in the wrong place. Build the tech that puts it back to work.
Build AI and IoT solutions that segregate waste at source and connect households, waste workers and recyclers, turning everyday household waste and e-waste into value.
Who should apply
WasteTech, recycling and e-waste, computer-vision, IoT and marketplace startups.
What teams can build
- Waste-identification models
- Smart bins
- Tracking apps
- Recycling dashboards
- Traceability systems
- Incentive mechanisms
Problem and Expected Outcomes+
The problem
Mixed household waste ends up in landfills and open burning, while waste pickers sort it by hand and valuable plastics, metals and electronics are lost. Segregation at source is the single biggest lever for a circular city, yet it remains manual, untracked and unrewarded.
Why it matters
Waste volumes are rising fast in Indian cities. Segregation at source unlocks recycling, cuts landfill and emissions, and gives waste workers dignified livelihoods. It is a fast-growing market for WasteTech and circular startups.
What to build
- Identify and segregate waste at source using computer vision and/or sensors, including e-waste items.
- Track waste from homes to collectors and recyclers.
- Reward households and give waste workers better income and safer work.
Expected outcomes
- Working prototype: computer-vision or IoT waste identification (wet, dry, plastic, metal, e-waste) with a tracking app or dashboard.
- Measured result: classification accuracy, % waste segregated, kg diverted from landfill.
- Incentive model for households and income model for waste workers.
- Unit cost and business model.