For Science
Modern climate science and ecology suffer from a deficit of hyper-local data. Satellites see the big picture but miss the details. Scientific expeditions are expensive and time-constrained.
Envorum offers a solution: a global network of verified Citizen Scientists equipped with smartphones and motivation. We transform users into a distributed sensor network for structured data collection.
1. Concept: Human-as-a-Sensor
We provide research institutes access to a "Living Sensor Network." You can assign a Research Task in a specific region, and local users will execute it.
Scalability: Simultaneous sampling in 50 countries.
Speed: Receiving data in real-time, rather than months after an expedition.
Cost: Reducing field research costs by up to 90%.
2. Data Integrity
The main issue with traditional citizen science is low data quality ("noise"). Envorum solves this at the protocol level. Scientists receive a cleaned dataset, not a "raw stream."
Geo-tagging: All data is hard-linked to coordinates and H3 cells.
Timestamps: Blockchain guarantees timestamp immutability (Proof-of-Timestamp). This is critical for studying process dynamics.
AI Filtering: Our algorithm (Truth Pipeline) automatically rejects blurry, dark, or irrelevant images before they enter the database.
3. Use Cases
📡 Ground Truthing
Calibration of satellite data. Remote Sensing satellites often misinterpret pixels.
Task: "Photograph this 10x10m square."
Result: A scientist sees a "thermal anomaly" on the satellite, while a user on the ground confirms: "This is smoldering peat" or "This is just a heated factory roof."
⏳ Phenology & Chronicle
Tracking seasonal changes.
Mechanics: Users photograph the same tree or waterline from a fixed angle every week.
Scientific Value: Ideal data for studying the impact of climate change on vegetation and water levels.
🧬 Biodiversity Monitoring
Invasive Species: Mapping the spread of Hogweed or other dangerous plants.
Migrations: Recording the appearance of birds or animals in unusual ranges.
4. Datasets for ML (Machine Learning)
Envorum generates unique labeled datasets for training third-party neural networks.
Waste Recognition: Millions of photos of various waste types in natural environments (not in a studio). Ideal for training sorting robots.
Urban Decay: Datasets of asphalt cracks, metal corrosion, and concrete destruction for training computer vision systems in construction.
5. Data Access
We support Open Science principles for non-profit research while maintaining a commercial model for business.
API Access: Data streaming in JSON/GeoJSON format for integration with scientific software (ArcGIS, QGIS).
Raw Export: Exporting "raw" photos with metadata for proprietary analysis.
Anonymization: All data is transferred in an anonymized form, protecting volunteer privacy.
Grant Program
Accredited universities and ecological laboratories can apply for a grant from Envorum to conduct free research via our platform.
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