Hivemapper Saga: A Closer Look At The Google Maps Killer

The global digital mapping industry is $20-25 billion and is expected to grow with a CAGR of 12-13% from 2023 to 2031 according to various sources. With around 60-70% market share captured, Google Maps is single-handedly leading the industry. 

With the increased usage of location-based services, the demand for high-quality geospatial data skyrocketed. Google began charging for high-volume usage of its Maps APIs. Many organizations are looking for the most cost-effective alternatives to Google’s offerings. And open-source and free competitors like – OpenStreetMap rely heavily on volunteer efforts that corporations are now commercially benefiting from.

Enter Hivemapper. Hivemapper is creating a crowdsourced, blockchain-based global mapping system that compensates participants for capturing large amounts of high-resolution, street-level imagery using specialized dashcams.

In less than 2 years, Hivemapper has:

• Supply: 59,386 on-chain contributors

• Demand: 714 users

As shown in the above illustration, 4k Dash Cams first collect high-quality images at a 10 FPS (frames per second) resolution. 

These images & associated GNSS (Global Navigation Satellite System) and IMU data are then sent to the Hivemapper network using the Hivemapper consumer application. Before that, the Hivemapper application collects images (on average every 8 meters), filters out the unusable images according to network standards, packages 10 to 100 images in a bundle, and then this bundle is sent to the Hivemapper network. 

These bundles then enter the verification queue to be processed. Here multiple quality checks such as correctness of image and data, quality of image, blurriness, glare, etc. are performed. After the system verification, the images pass through a manual quality assurance process. 

For high-quality real-time mapping, it is necessary to perform location verification on gathered information & images. A better way of saying this is – as Hivemapper depends on drivers for mapping the world, it has to make sure that the Hivemapper network can trust data providers. For this, Hivemapper has implemented 3 layers of location verification system which team Hivemapper calls – “Proof of Location”.

1. Hivemapper uses GNSS (Global navigation satellite system) to record the position of the dashcam while mapping. 

2. Hivemapper dashcams connect with Helium hotspots deployed in nearby areas and record the location of helium access points. 

3. Collected images go through human validation by contributors.

Hivemapper is designed with the same philosophy as most public blockchains. A system in which participants are incentivized for their contributions and users on the demand side will need to pay for the services. The protocol also implements multiple levels of performance checks to avoid any kind of bad behaviors from network agents.

1. Contributors collect street-level imagery using Hivemapper dashcams. They are rewarded with HONEY tokens based on factors like the novelty, reputation, clarity, and consumption of their data.

2. AI Trainers participate in labeling map data and training Hivemapper's machine learning models. They are also rewarded with HONEY tokens for their work.

3. 40% of the total 10 billion fixed HONEY token supply is allocated to reward contributors over time as the global map coverage increases.

4. HONEY token rewards are minted weekly based on the Global Map Progress. More mapping progress unlocks more token rewards.

5. Specific regions can have HONEY reward multipliers to incentivize mapping in high-priority areas.

1. Enterprises and developers who want to consume Hivemapper's map data must purchase Map Credits.

2. Map Credits are priced at $0.005 each, with 50 credits providing access to 1 km per week of map data.

3. When Map Credits are purchased, an equal amount of HONEY tokens are burned, creating demand for the token.

4. The burned HONEY tokens are reallocated to a new rewards pool for contributors.

5. Potential map data customers include location-based services, logistics companies, automotive manufacturers, surveying businesses, insurance, and more.

The first principle thinking behind Hivemapper was – crowdsourcing location-based information in a cheap and fast manner and incentivizing the suppliers through on-chain tokens. 

In this industry, the key metric to measure performance is – Mapping Velocity i.e. the speed at which unique KMs of a given area are mapped. In 2 years Hivemapper mapped more than 10 million unique KMs around the world, which is 18% of the total road network. For comparison, it took Google more than 12 years to map 16 million Unique KMs. For the first 5 years after the launch, on average Google Maps mapped 133,000 KMs per month. The current Hivemapper average per month is 3x of Google's first five years. 

Another parameter is cost. Mapping the world requires significant financial investment and human effort. Google's approach of collecting data using company-owned vehicles is expensive, with each one potentially costing around $500,000 to manufacture and put into use. Achieving extensive coverage is challenging without substantial financial resources. 

Whereas with Hivemapper crowdsourcing & cyclic reward incentive mechanism such a barrier doesn’t exist.

The above image shows the Weekly active contributors of Hivemapper.

In March 2023 Hivemapper Contributors started increasing dramatically, specifically QA contributors. The reason behind this was the announcement of the Map AI trainer program. With this program, anybody can earn a HONEY token without being a driver or purchasing a dashcam, just by playing a simple gamified map training task. Due to this, multiple spam bots started targeting AI trainers.

The Hivemapper team identified this and quickly implemented anti-spam protections. The team also recreated the reputation system & changed parameters for incentives to ensure little to no rewards to Spammers. Due to this, there was a sudden decrease in contributors starting from June 2023.

Almost as soon as AI Trainers went live, spammers started targeting them with bot accounts. We knew it would happen (it’s unavoidable with HONEY rewards on the line) but we were still a bit surprised by the speed, scale and sophistication of these efforts. 

Although we feel good about the job we’ve done of identifying spam and ensuring that spammers receive little or no rewards, this cat-and-mouse game has taken a significant amount of effort. And at times, spikes in spam had a severe negative impact on the quality of data from AI Trainers.

For this reason, we plan to invest heavily in additional anti-spam protections, in our reputation system, and in a leveling system that will create strong incentives for sustained quality over time. 

– The Hivemapper Team

In November 2023 Hivemapper celebrated its first anniversary. Along with this product launches like Scout and community initiatives like Open Road Season 2 contributed to a healthy increase in both mapping & QA contributors. An increase in healthy contributors is a sign of increasing road coverage, higher map quality, an increase in value to users, and ultimately a growing demand from end clients.

From February 26, 2024, to March 25, 2024, a total of 14.3 million $HONEY tokens were distributed to mapping contributors. Hivemapper has 5.02k active mapping contributors.

That means on average (in 30 days) a mapping contributor earned ~2850 $HONEY tokens. With a current price of $0.10, we can safely say that a mapping contributor earns $250-300 per month. This means for mapping contributors it takes less than two months to recover their investments.

1. Decentralized Map AI for ADAS and Autonomous Vehicles

Hivemapper's crowdsourced imagery and Map AI pipeline provide fresh, detailed map data to enhance ADAS safety and enable autonomous driving. Extracted road features like signs, lane markings, and traffic lights support perception model training and navigation in challenging conditions.

2. Smart Cities Infrastructure Monitoring and Optimization

Cities can leverage Hivemapper's regularly refreshed street-level imagery and Map APIs to efficiently monitor road conditions, prioritize repairs, and optimize infrastructure. Insights into traffic patterns, construction, and asset inventory enable data-driven urban planning and maintenance.

3. Real-Time Mapping for Navigation and Logistics

Hivemapper provides navigation apps, rideshare platforms, and logistics companies with highly scalable, dynamic map data. Real-time road information, construction alerts, and fresh imagery enable efficient routing, pickup/dropoff optimization, and supply chain modeling.

4. Property Assessment and Development Analysis

Detailed, up-to-date street-level imagery allows for remote property valuation, insurance underwriting, and site selection. Developers and investors can scout and analyze properties, assess neighborhood conditions, and inform land use decisions.

5. Diverse Imagery Dataset for Computer Vision AI

Hivemapper's imagery spans diverse global environments and conditions, providing a rich dataset to train and enhance computer vision models. The Map Image API enables access to this valuable data to improve model performance and robustness for various applications.

1. Spam and Low-Quality Contributions

   With an open, decentralized model, Hivemapper may face issues with spam imagery or low-quality map data submitted by malicious or careless contributors looking to game the token rewards. Implementing robust data validation, quality control mechanisms, and contributor reputation systems will be critical to maintaining map accuracy and integrity.

2. High Dashcam cost

   The current $469 price point for the Hivemapper dashcam may limit the scalability and global adoption of the network. Many potential contributors, especially in developing regions, may find the hardware cost prohibitive. This challenge may be mitigated as more affordable dashcams become available from third-party providers, potentially increasing network coverage. 

3. Misaligned incentives 

   Ensuring the HONEY token rewards are properly calibrated to incentivize high-quality, needed mapping contributions is complex. If incentives are skewed, it could lead to over-mapping of certain areas while leaving other regions under-covered. Careful economic design and ongoing incentive adjustments will be required.

4. Quality assurance overhead

   Decentralized data collection introduces challenges in standardizing imagery specs, coverage frequency, and annotation quality. Hivemapper will need to invest heavily in both technological and human-driven quality assurance processes to achieve a consistently accurate global map. This overhead could slow scaling compared to centralized mapping operations.

5. Competition with tech giants  

   While Hivemapper may build a technically superior mapping product, it will still need to compete with established players like Google and Apple for enterprise mapping customers. These tech giants have deep pockets, strong brand recognition, and existing sales channels that could be hard for a startup to match. However, Hivemapper's lower cost structure and fresher map data could still prove attractive to customers.

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