Individual Reward Factors
More HONEY will be rewarded for higher-quality contributions.
Last updated
More HONEY will be rewarded for higher-quality contributions.
Last updated
is designed to incentivize the creation of a global map by focusing contributors on coverage, freshness, and quality. These factors make the map more useful for customers, which in return generates more rewards for contributors through the burn and mint system.
Imagery for a given week (Mon-Sun) must be uploaded by 4:59pm Pacific Time on the Tuesday following the week’s completion in order to generate rewards.
are rewarded for helping to achieve complete coverage. A complete map is valuable to the Hivemapper Network because it helps address the greatest number of potential use cases for customers.
The Hivemapper Network incentivizes increased coverage by encouraging drivers to vary their routes and mount positions to . Additionally, map data consumers can create bounty rewards for completion of certain areas. All contributors helping to map those areas will receive extra rewards.
Contributors are rewarded for helping the map stay fresh. A fresh map is valuable to the Hivemapper Network because customers want maps that reflect the current reality of the world.
The network incentivizes regularly refreshing with dynamic rewards related to . There is less utility in mapping saturated areas than there is in mapping unsaturated areas; therefore, contributions to less saturated areas are more heavily rewarded.
Mapping data from contributors must be high quality in order to be useful to customers.
Broadly speaking, is collected with a proper , has a , is free of glare and obstructions, and has correct lighting. The Hivemapper Network has multiple mechanisms to assess the quality of data.
Each map contributor has a reputation score that starts out neutral and rises or falls based on the quality of their mount position. Reputation scores are not yet publicly viewable.
Hivemapper’s Map AI processes street-level imagery and extracts map features: traffic lights, speed limits, turn restrictions, highway exits, etc. As contributors play Map AI Trainer games, they earn HONEY rewards for training the Map AI to detect and properly classify map features. This process requires many contributors to review before reaching consensus on any given object.
Each AI Trainer contributor has a reputation score that starts out neutral and rises or falls based on the quality of their data validations. Reputation scores are fragile; it’s hard to build a good one and easy to negatively impact it with poor-quality contributions to the network. Reputation scores are not yet publicly viewable.
A high reputation score leads to benefits and a low reputation score has consequences. In cases of clear fraud attempts, there will be severe and immediate consequences to an AI Trainer contributor’s reputation and quality score, ultimately impacting the amount of HONEY that the contributor can earn.