CSI Ocean is a free mobile app for reporting microplastic observations in water, air, and snow. Snap a photo, capture the science, and contribute to a global, open dataset that researchers and communities can act on.
CSI Ocean turns your phone into a microplastic field kit. Open the app, photograph a sample of water, air, or snow, and the app's machine-learning model marks suspected microplastic particles with bounding boxes and confidence scores.
You add the local conditions, depth, sample volume, sampling method, location, and submit. The observation lands on a global map that anyone can browse. Researchers get a structured, reproducible dataset. Educators get a real-world experiment their classes can run anywhere. Citizens get a way to see, document, and discuss what their environment is carrying.
Free. No ads. No tracking. No premium tier.
Microplastic pollution is one of the most pervasive environmental challenges of this century, and one of the hardest to measure. Particles smaller than five millimetres travel in ocean currents, blow on the wind, and fall with the snow. They have been found from the Mariana Trench to the summit of Mount Everest.
But the data on where, how, and how much is fragmented. Field samples sit in spreadsheets that never reach the public. Citizen-led efforts use inconsistent methods. Volunteers want to help, but don't know what counts as a useful observation, what to record, or where their photos should go.
CSI Ocean closes that gap. It encodes the methodology of trained microplastics researchers (sample type, environment, depth, volume, equipment, contamination protocols, filtering method) into a guided wizard that anyone can follow. It uses machine-learning detection to flag candidate particles in seconds. It stores everything in one open, geo-located dataset that researchers can query and the public can explore.
A clipboard, a microscope, a lab notebook, and a desktop database used to be the cost of entry. Now a curious person with a phone can do it on a beach, in a park, or in their own backyard.
Standardised capture, structured metadata, and detection results stored alongside every image. Export observations as PDFs for fieldwork records, or use the dataset as a feed into your own analysis pipeline.
See the data modelarrow_forwardA real-world citizen-science project that runs on phones students already own. Use it for a single field trip or a semester-long monitoring programme. The taxonomies read like a textbook chapter your class can fill in.
Run with your classarrow_forwardRun a project, invite your community to follow it, and watch contributions roll in. Each observation is geo-pinned, timestamped, and tied to a sampling protocol your team can vouch for. A built-in leaderboard recognises your most active contributors.
Find or start a projectarrow_forwardYou don't need a lab coat. You need a phone, some daylight, and twenty minutes. Onboarding walks you through your first observation. From there, every walk along the shore, every snowfall, every stream becomes a small contribution to a global picture.
See how it worksarrow_forwardBrowse the public map. Filter by sample type, region, or time window. See where contributions are concentrated, what is being detected, and where the gaps are. Every observation is sourced and dated.
Browse the global maparrow_forwardPick water, air, or snow. The capture wizard adapts to the sample type, asking only for the fields that make sense for what you are documenting.
Take up to 30 photos with the camera, or pick existing ones from your library. CSI Ocean sends them to its detection model and marks every suspected microplastic particle with a numbered bounding box and a confidence band: green for high, amber for medium, red for low.
Depth, water type, environment, sample volume, equipment, filter pore size, contamination protocol, weather, time of day. Only the fields relevant to your sample type. A skip-advanced option keeps it light when you are starting out, or fill it all in for research-grade entries.
Tap your location, drop a pin, or enter coordinates manually. The app reverse-geocodes a human-readable address. Offline? No problem. The observation queues locally and syncs the moment you reconnect.
Your observation lands in the global feed and on the explore map. Browse what others are reporting. Filter by sample type. Switch from clustered pins to a heatmap. Tap any observation to see the full breakdown, including detections drawn directly on the photos. Export a polished PDF for your field journal.
A glimpse of how the app is actually built. Four headlines below; the full eight feature groups, with every bullet, live on the features page.
A multi-step wizard adapts to the sample type. Machine-learning detection on every photo with confidence bands you can trust.
Clustered pins, animated heatmap, posts feed, global search. Filter by sample type, time window, or observer.
Three-tab detail view, pinch-zoom on bounding boxes, inline location card, polished PDF export with annotations.
Offline-first capture with status badges, cached scientific vocabularies, and resilient sync when connectivity returns.
Switch from clustered pins to a heatmap. Filter by sample type. Tap a pin to read the full observation, including detections drawn on the photos. The cinematic below is rendered programmatically with Remotion to give you a feel for the live screen.
5 sample observations · powered by Google Maps in-app
Sample data shown. The live feed connects when the public dataset ships.
Four of the choices we made before we wrote a line of product code. The full set, plus the data we do hold and the controls you have over it, is on the trust page.
Read the human-language version
How we earn your trustarrow_forwardYes. There is no paid tier, no premium dataset, and no advertising. The project is funded by Nat Geo, so the app can stay free for the people doing the work.
No. The capture wizard is designed for first-time observers. You'll be guided through every step, with optional advanced fields you can skip until you want them. If you have scientific training, the metadata is rich enough for research-grade observations.
The detection model flags suspected microplastic particles and assigns a confidence band: high, medium, or low. It is intended as a screening aid, not a substitute for laboratory confirmation. Researchers using observations for analysis can filter by confidence and by data-quality tag (Casual, Needs ID, Research).
9+ more answers on the trust page
More questionsarrow_forwardFree on iOS and Android. Twenty minutes from a curious walk to a peer-reviewable record. Every observation contributes to a shared scientific dataset.
No ads · No tracking · No premium tier
