StreamSense

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Inspiration

Water connects every community, yet many streams and water bodies are rarely monitored because traditional water testing can be expensive, time-consuming, and difficult to scale. We wanted to make basic environmental monitoring more accessible to everyone.

What it does

StreamSense helps users monitor the health of streams and water bodies. It uses location data, guided visual surveys, photos, and AI-assisted analysis to assess water conditions and generate meaningful environmental insights.

How we built it

We built StreamSense as a web-based platform combining geolocation, guided visual surveys, photographic evidence, and AI-assisted analysis. The interface was designed to make field data collection simple, visual, and accessible. Users can locate a water body, answer guided questions, capture evidence, and receive an AI-assisted assessment based on their observations.

Challenges we ran into

Our biggest challenge was balancing detailed environmental data collection with a simple user experience. We also had to find a reliable way to combine user observations, photographs, and location data without making the survey complicated or time-consuming.

Accomplishments that we're proud of

We are proud of turning a complex water-monitoring process into a simple guided experience. StreamSense enables people to contribute structured environmental observations while AI helps transform those observations into meaningful insights.

What we learned

We learned that building an environmental AI solution requires more than just an AI model. Data quality, user experience, evidence collection, location accuracy, and clear presentation of results are equally important.

What's next for StreamSense

We plan to expand StreamSense with advanced water-quality analysis, improved AI image assessment, historical monitoring, interactive watershed maps, community-driven data, and dashboards for researchers and environmental authorities. Our long-term goal is to create a scalable network of verified water-health observations.

Built With

Submitted to

OneAquaHealth IEEE Global Hackathon

Created by

I contributed to the development of StreamSense by working on the core web application, user experience, and integration of Google Maps, Firebase, and the OpenAI API. I helped design the guided water survey workflow, location-based features, and AI-assisted analysis system. I also worked on integrating the different components into a functional and user-friendly platform for real-world water monitoring.

PRIYAM SARKAR
Moupriya Sil
Anirban Dalui
Moumita Basak