A purpose-built AI fuses satellite imagery and on-the-ground submissions into a single classification pipeline. The result: verifiable waste detection at a resolution and reach no single-source method can deliver.
Two signals in. Structured intelligence out. Verified by a distributed ground-truth network.
The model ingests satellite imagery and geotagged ground submissions in parallel. Satellite gives reach. Ground gives precision. Both feed the same pipeline.
A convolutional neural network classifies waste by material type, estimates accumulation density, and assigns a confidence score to every detection. Every output is structured and auditable.
Every detection is geocoded and clustered into zone-level hotspots. The output is a queryable spatial dataset, not just images: intelligence enterprise systems can consume directly.
Every verified ground submission feeds back into the training loop. Our proprietary dataset grows with each cycle, a compounding accuracy advantage new entrants cannot easily replicate.
A real-world AI pipeline fusing satellite imagery and ground-truth submissions. Tuned on the environments where waste accumulates fastest: dense urban areas, coastal zones, the routes between.
We have reports across multiple Nigerian cities, with expansion across Africa underway. Every new region adds to a dataset that compounds in value for the enterprises, governments, and partners building on top of it.
Researchers, enterprise customers, and partners shaping the waste intelligence layer for the planet. Reach out for dataset access, the API, or to talk to the team.
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