Jal Drishti

Care Before It Becomes Rare

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Inspiration

India receives over 4,000 billion cubic meters of rainfall annually, yet more than 600 million people face severe water stress. Approximately 85% of rural households rely on groundwater, leading to rising electricity costs for pumping water, while vast amounts of rainwater go unused. The core challenge identified is not water scarcity but efficient water management and accessibility. Jal Drishti was inspired by the need to harness rainwater effectively to address water stress and promote sustainable water usage

What it does

Jal Drishti is a user-friendly web and mobile application that uses satellite imagery, augmented reality (AR) input, AI-powered rooftop detection, and GIS modeling to provide real-time assessments of rooftop rainwater harvesting and artificial recharge potential. It offers immersive virtual reality (VR) visualizations so users can preview the impact and measurement of a rainwater harvesting system before installation. The app also provides alerts, smart cost analysis, policy guidance, and instant PDF reports to encourage sustainable water use and maintenance.

How we built it

The solution uses:

Satellite data from sources like Google Earth Engine, Sentinel-2, Landsat, and Maxar for environmental inputs.

AI and computer vision (U-Net, Mask R-CNN, OpenCV) for automatic rooftop detection.

GIS integration via QGIS and Azure-hosted PostGIS for spatial analysis.

Automated runoff and rainwater calculation algorithms with rainfall data from IMD, NASA, GPM, and hydrological formulas.

AI/ML tools deployed on Azure ML, TensorFlow Lite, and PyTorch for model execution.

Cloud backend comprising Azure Functions, App Service, Cosmos DB/PostgreSQL with REST/GraphQL microservices architecture.

Frontend mobile app built in Kotlin with Jetpack Compose and web app using React and Mapbox/Leaflet for mapping and interactive visualization. Additional features include AR-based measurements, WhatsApp alerts, and connection to local vendors for system installation.

Challenges we ran into

The team faced challenges in accurately detecting rooftops using AI and integrating diverse data sources such as satellite imagery, rainfall APIs, and soil data. Achieving real-time processing with AI and GIS calculations required robust cloud backend design and optimization. Another challenge was ensuring guided, error-free user experience with precise measurements and policy compliance across varying local regulations, as well as blending AR/VR technologies seamlessly.

Accomplishments that we're proud of

Real-time and accurate rooftop rainwater harvesting feasibility assessment using AI and GIS.

Integration of satellite data with AR input for enhanced user engagement and precise measurement.

Implementation of immersive VR visualizations for users to preview rainwater harvesting impact.

Development of an intelligent alert system via WhatsApp for timely maintenance and policy compliance.

Deployment of a scalable cloud backend supporting microservices architecture enabling smooth data processing.

Enabled significant water, energy, and cost savings for users with 20-30% water consumption reduction and up to 40% reduction in water bills.

What we learned

The team gained valuable insights into combining AI, satellite data, and GIS for environmental applications. They learned to optimize machine learning models for edge and cloud deployment, ensuring quick and reliable rooftop detection. The importance of user-centric design emerged, especially in guiding users through complex environmental data and compliance requirements. They also experienced the challenges of integrating emerging technologies like AR/VR with traditional GIS tools in a cohesive system.

What's next for Jal Drishti

Future plans include expanding the freemium model to provide more detailed assessments and reports, extending the platform to work in flood-prone and drought-prone areas, and scaling deployment for government and university partnerships (B2G licensing). Enhancements in AI-driven best-fit design recommendations and flexible real-time cost estimations are planned. The team also aims to deepen vendor collaboration for verified referrals and include blockchain for secure data and transactions. Continued focus on sustainability goals includes reducing CO2 emissions and promoting energy savings by raising groundwater levels

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