NATIX Launches 360Data Subnet on Bittensor for Real-Time Mapping

Coin WorldTuesday, May 27, 2025 9:50 am ET
3min read

NATIX Network has officially launched its 360Data Subnet (Subnet 72) on the Bittensor decentralized AI network, marking a significant advancement in its mission to modernize mapping and autonomous driving through decentralized, real-world data processing. This initiative is powered by a Solana-based decentralized physical infrastructure network of smart cameras, which combines high-volume, street-level data collection with scalable machine learning through Bittensor.

The subnet, incubated by Bittensor infrastructure partner Yuma, processes 360° video feeds captured from NATIX-enabled Tesla vehicles and other mobile devices. These feeds are then converted into AI models that enhance real-time map-making and vehicle autonomy. This approach addresses the limitations of traditional methods, such as those used by companies like Uber, which rely on synthetic simulation and bespoke data collections that are both expensive and lag behind real-world changes.

NATIX aims to leverage a global community of over 250,000 drivers who have collectively logged over 170 million kilometers through smartphone and VX360 device usage. The VX360, developed in collaboration with Grab, utilizes Tesla’s existing camera systems to collect 360° imagery without the need for expensive new hardware. The data is processed both in the cloud and at the edge, with smartphones detecting elements like traffic lights and signs in real time. More intensive classification is handled off-device.

Through Bittensor’s decentralized framework, NATIX miners are rewarded for training and improving AI models on the subnet, which are then redeployed across the NATIX Edge Network. This decentralized approach enables continuous improvement of AI models, significantly enhancing mapping accuracy, autonomous vehicle safety, and real-world responsiveness. The subnet’s initial focus is on roadwork detection, a critical application for both mapping platforms and autonomous vehicle navigation. Over the longer term, NATIX plans to expand its operations to include pothole detection and infrastructure analysis, eventually providing a full scenario classification to support autonomous vehicle training.

In an exclusive interview, NATIX CEO and co-founder Alireza Ghods discussed the company’s approach to scaling decentralized data capture. He emphasized that data collection for mapping and autonomous driving is extremely expensive, and large companies that collect this footage do not share it as they use that data to maintain a competitive edge. NATIX, however, can collect data at an unprecedented scale through crowdsourcing, making the end result as good as data collected by dedicated mapping vehicles. For example, Grab built their mapping solution based on crowdsourced efforts, resulting in maps more accurate than Google Maps in Southeast Asia.

Ghods also addressed the technical challenges of collecting 360° video from thousands of cars and phones in real time. He explained that the compute required for processing the data is heavy and cannot be done at the edge, so it is handled at the cloud level. The data shared by users is only uploaded when they are connected to their home Wi-Fi connection. Additionally, the smartphone network is used for real-time detection of map attributes such as traffic signs and traffic lights.

Regarding the scaling strategy, Ghods highlighted that the VX360 device is cost-effective, tapping into commodity hardware like smartphones and Tesla vehicles’ existing 360° cameras. This approach makes it scalable as a crowd-sourced solution. NATIX is also in discussions with partners who want to operate a fleet of VX360s, including a Tesla fleet owner with over 3,000 Tesla vehicles in their network. These partnerships will help increase the utility of the product beyond crypto rewards.

Ghods sees big players like Waymo, Tesla, and Mobileye as potential customers, noting that even if some tell them they do not need their data today, they will need it soon enough as the roads are constantly changing. He also mentioned that NATIX has Grab as a customer and is in the process of closing deals with some of the largest autonomous driving players, both as direct customers for the data and partners for building various products for autonomous driving.

When asked about the lessons learned from similar projects like Hivemapper and DIMO, Ghods pointed out that successful models like Geodnet focus on protocol revenue and have a proper mechanism for value accrual to the token. NATIX is following a similar strategy, ensuring that the value accrual goes towards the $NATIX token through protocol revenue and buyback and burn mechanisms. He also emphasized that 360° data is the holy grail of street-level visual data, opening the door to countless new mapmaking use cases and Physical AI development.

Regarding the utility of the $NATIX token, Ghods explained that it is used for protocol governance and securing the network. Validators in the StreetVision Subnet are required to stake $NATIX, and the token is also used for data curation. The StreetVision Subnet focuses on insight extraction and AI model creation, generating its own value and having its own value accrual mechanism for its token. The revenue generated will be used for value accrual towards $dTAO and partially towards $NATIX as the data used for such insights and models belongs to NATIX.

Ghods also addressed concerns about centralization, noting that anyone can run a validator for their subnet. NATIX is working with other major Bittensor validator players to ensure an open ecosystem. He also mentioned that miner centralization is a bigger concern than validator centralization, as the output of the network depends on miners. NATIX has removed staking or $dTAO holding requirements for miners to ensure an open ecosystem.

The StreetVision Subnet’s first task will be real-time roadwork detection, which is crucial for mapping updates and AV reliability. More use cases will be added later, including detecting potholes, road signs, littering, and infrastructure monitoring. The subnet will also include analysis and classification of driving videos into scenarios from routine traffic conditions to rare “edge cases,” which is important for scenario generation for autonomous driving and Physical AI. Grab is currently using NATIX’s data to build their pipelines for the US and EU markets and is a paying customer. NATIX is also in commercial negotiations with dozens of autonomous driving companies and working with top autonomous driving research labs to create cutting-edge products for simulation-to-reality and reality-to-simulation.

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