Interpreting the new track of Decentralization space intelligent network: core concepts, main projects, and future development

Author: cookies

Compiled by: Deep Tide TechFlow

With the continuous development of Web3 technology, the Decentralized Spatial Intelligence Network (DeSPIN) is becoming a highly regarded field. By analyzing and utilizing visual data from the real world, DeSPIN not only provides innovative solutions for map construction, urban planning, and robotics but also opens up a brand new “Contribute-to-Earn” economic model. This article will provide a detailed interpretation of the core concepts of DeSPIN, its main protocols, and its future development direction.

What is DeSPIN?

Spatial Intelligence is a technology that extracts insights by analyzing visual data from the real world. Its core lies in combining geographic information with environmental context to support human decision-making. The Decentralized Spatial Intelligence Network (DeSPIN) integrates this technology with the decentralized concepts of blockchain and Web3, creating an open and shared ecosystem. Imagine being able to earn rewards by sharing road photos taken in your daily life or recording environmental data in malls and streets. This model not only lowers the threshold for data collection but also incentivizes ordinary users to contribute to the development of spatial intelligence.

Before understanding the specific applications of DeSPIN, we need to first grasp the basic framework of spatial intelligence. Spatial intelligence consists of four core components:

• Data collection: Collect data using sensor networks (such as cameras, GPS) and Internet of Things devices (such as smartphones, laptops).
• Data processing and analysis: Using machine learning techniques to process geospatial metadata, identify patterns in the data, and build a spatial query database.
• Knowledge representation: Associating data with environmental context through semantic mapping to provide users with visual geographic information.
• Decision Support System: Build spatial prediction models to provide application services for users, such as route optimization and obstacle avoidance.

The main protocol in the DeSPIN field

Currently, multiple innovative protocols have emerged in the DeSPIN field, focusing on different application scenarios. Here are eight projects worth paying attention to:

1.Hivemapper

Hivemapper is a decentralized mapping protocol that adopts a “Drive-2-Earn” model. Users report road issues in real-time through a mobile application, while drivers collect data using dashcams installed in their vehicles. This data is processed by AI algorithms to generate maps, and its accuracy is verified through human feedback reinforcement learning (RLHF). Hivemapper provides coverage maps, allowing users to see which areas have been mapped and access data via API. Data contributors can earn $HONEY token rewards, which can be used to purchase map data or other services.

2.NATIX Network

NATIX Network is a decentralized map economy protocol focused on collecting road data through mobile devices and dash cams, utilizing a “drive-to-earn” model. Its core technology, VX360, supports 360-degree panoramic data collection, and the data collected can be used to develop driving assistance features, such as autonomous driving optimization. Currently, NATIX Network has covered 171 countries, with over 223,000 registered drivers and a cumulative mapped mileage of 131 million kilometers. Data contributors and network nodes can earn $NATIX token rewards, further incentivizing ecological development.

Hivemapper and NATIX are committed to building higher quality maps through crowd-sourced road data. The potential applications of this data are very broad, mainly including the following aspects:

• Optimize urban transportation: By analyzing real-time collected road data, traffic flow management can be improved, congestion can be reduced, and travel efficiency can be enhanced.
• Monitor road conditions: Timely detection and reporting of road damage, obstacles, or other potential issues contribute to the safety and reliability of infrastructure.
• Detecting crime and violent behavior: Utilizing map data combined with AI algorithms can help identify and locate abnormal behaviors, providing support for public safety.

These applications not only enhance the functionality of maps but also bring practical value to urban management and social safety.

3.FrodoBots

FrodoBots is a protocol for gamified data collection through robots, allowing users to remotely control ground robots to collect geographical data, supporting various operation methods (such as controllers, keyboards, or gaming steering wheels). Additionally, researchers can deploy AI navigation models on the platform for testing. Users earn FrodoBot Points (FBPs) by completing driving tasks, with points related to the distance and difficulty of the tasks; the longer the distance and the higher the difficulty, the more points are earned. FrodoBots has been tested in multiple cities and has hosted competitions for navigation capabilities between AI and humans. Furthermore, FrodoBots has established a guild-like system called Earth Rovers School, allowing new users to participate in data collection by renting Earth Rovers.

4.JoJoWorld

JoJoWorld is a protocol focused on 3D spatial data collection, where users contribute data to help train three-dimensional models. The platform provides high-quality 3D data for creating various digital scenes, suitable for virtual reality, urban planning, and other fields. Users can also directly purchase these 3D data for personalized digital model development.

The next four protocols also focus on collecting spatial data from the real world, but their application areas are more segmented, covering specific scenarios such as robot model training. These protocols inject more possibilities into the ecosystem of the Decentralized Spatial Intelligence Network (DeSPIN) by focusing on long-tail data and specific needs.

5.PrismaXAI

PrismaXAI is a protocol that collects specific scene data from a first-person perspective, suitable for complex scenarios such as hand-object interaction, dynamic movement, and social gatherings. Its core technology, Proof-of-View, ensures the authenticity of the data while enhancing the accuracy of data annotations through a decentralized verification mechanism. This protocol has great potential in acquiring long-tail data, providing unique advantages for model training.

6.OpenMind AGI

OpenMind AGI focuses on understanding the real world through Visual-Language-Action Models (VLAMs). Its core system, OM1, is a multi-platform operating system capable of interacting with dynamic real-world environments, particularly suitable for customized development in robotics. The platform collects data through mobile phones and robots, sharing this data with robot developers to improve and innovate robotic application scenarios.

7.MeckaAI

MeckaAI is a decentralized robot AI model training protocol that allows users to help train robot behavior models by uploading video data. The platform offers a mobile application where users can earn OG Mecka Points by completing tasks, further incentivizing data contributions. MeckaAI is committed to advancing robotic technology through a crowdsourcing model, lowering the barriers to obtaining training data.

8.Xmaquina DAO

Xmaquina DAO is a decentralized autonomous organization (DAO) that supports open-source robot projects. Unlike other protocols that directly participate in model training, the core goal of Xmaquina DAO is to support research and innovation in the field of robotics through resource allocation. Its internal innovation center, Deus Lab, focuses on research and development in robotic technology, while MachineDAO votes on resource allocation to various projects by staking the token $DEUS. This model provides financial support for the open-source development of robotic technology while ensuring transparency and fairness in resource allocation.

The organizational structure of MachineDAO

Due to space limitations, there are also some application protocols in similar fields that are not detailed here, such as Alaya_AI, Gata_xyz, KrangHQ, etc., which are also worth paying attention to.

The Future of DeSPIN: From Contribution to Value

Although DeSPIN is still in its early stages, its potential cannot be ignored. With the development of physical AI and embodied AI, as well as the emergence of new concepts like the Human Data Fleet, DeSPIN is expected to lead a new technological revolution.

A possible trend is the popularity of the “Train-to-Earn” (T2E) model, where users contribute value through spatial data obtained in their daily lives and receive rewards based on data quality. For instance, the emergence of decentralized eyewear devices can greatly enhance the accuracy and diversity of data collection. The data captured by smart glasses not only reflects the way humans perceive the world most authentically but also collects a variety of long-tail data such as environmental noise and facial features, bringing broader possibilities to the field of spatial intelligence.

However, the development of DeSPIN also faces some challenges, such as:

• Data validation: How to ensure the authenticity and accuracy of crowdsourced data?
• Ethical issues: How to regulate the use of data to prevent privacy breaches and abuse?
• Acceptance by the demand side: Are traditional institutions willing to adopt decentralized datasets?

The resolution of these issues will determine the future direction of DeSPIN and needs further research and solutions in the future.