Categories: Payment system news

Bee Maps Raises $32M to Scale Solana-Powered Decentralized Mapping Network

Bee Maps, the decentralized mapping project powered by Hivemapper, has raised $32 million in fresh funding to expand its global contributor network and scale its infrastructure, it announced Monday.

The round was led by Pantera Capital, LDA Capital, Borderless Capital and Ajna Capital, marking one of the largest financings in the decentralized physical infrastructure (DePin) sector this year.

Bee Maps is an application on the Hivemapper network, which is one of the largest decentralized physical infrastructure networks (DePIN) focused on mapping data on Solana.

Hivemapper enables drivers to contribute data through AI-enabled dash cams, which detect real-time changes on roads (like new signs on the roads, detours, or construction zones), ensuring digital maps can update quickly to stay accurate. Bee Maps leverages this infrastructure by rewarding contributors with its native token, $HONEY, for collecting street-level imagery.

The raise highlights strong investor appetite for Bee Maps’ vision of real-time, AI-powered maps. In recent months, Bee Maps has teamed up with major players including Lyft and Volkswagen’s robotaxi program to bring its mapping-data to their platforms.

The fresh capital will be used to distribute more devices, enhance AI models that process and update map features and boost contributor incentives tied to $HONEY.

“With this funding, we are accelerating global device deployments, expanding coverage, and strengthening our AI pipeline. Demand is not the problem — supply is,” said Ariel Seidman, the co-founder of Hivemapper, in a press release shared with CoinDesk.

Bee Maps is also rolling out a Bee Membership plan, slashing upfront costs from nearly $600 to just $19 a month. The subscription bundles hardware, software, and contributor benefits into one package, lowering the barrier for new participants to join the network.

Read more: Volkswagen ADMT Taps Solana-Based Hivemapper Bee Maps for Driverless Data

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