Another new concept? Learn about DeSPIN and 8 projects worth following.

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> The core of DeSPIN lies in combining geographic information with environmental context to support human decision-making.  **Written by: cookies****Compiled by: Deep Tide TechFlow**  With the continuous development of Web3 technology, the Decentralized Spatial Intelligence Network (DeSPIN) is becoming a highly关注领域. 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, main protocols, and future development directions of DeSPIN.  ![](https://img.gateio.im/social/moments-4fbfa240ad0790350c2a5a6a8702f77f)  ## What is DeSPIN?  Spatial Intelligence is a technology that extracts insights by analyzing visual data from the real world. Its core lies in combining geographical 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, forming an open and shared ecosystem. Imagine being able to earn rewards by sharing photos of roads taken in your daily life or recording environmental data in malls and streets. This model not only lowers the barrier 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: Collecting data using sensor networks (such as cameras, GPS) and Internet of Things devices (such as smartphones, laptops).* Data Processing and Analysis: Use machine learning techniques to process geographic 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: Construct spatial prediction models to provide application services for users, such as route optimization, obstacle avoidance, etc.  ## Main Protocols 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 map-building protocol that uses 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, which are verified for accuracy through human feedback (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 that focuses on collecting road data through mobile devices and dash cams, adopting 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 crowdsourced road data. The potential application scenarios of this data are very broad, mainly including the following aspects:  * Optimize urban traffic: By analyzing real-time collected road data, traffic flow management can be improved, congestion reduced, and travel efficiency increased.* Monitor road conditions: Timely detection and reporting of road damage, obstacles, or other potential issues helps maintain the safety and reliability of infrastructure.* Detecting criminal and violent behavior: Utilizing map data combined with AI algorithms can help identify and locate abnormal behavior, 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 geographic data, supporting multiple operation modes (such as controllers, keyboards, or gaming steering wheels). In addition, researchers can deploy AI navigation models for testing on the platform. 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 earned. FrodoBots has been tested in multiple cities and has held 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 the creation of various digital scenes, suitable for fields such as virtual reality and urban planning. Users can also purchase these 3D data directly 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 Intelligent 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 a unique advantage 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 that interacts with dynamic real-world environments, particularly suitable for customized development in robotics. The platform collects data through mobile phones and robots and shares this data with robot developers to improve and innovate robotic application scenarios.  **7 MeckaAI**  MeckaAI is a decentralized robot AI model training protocol, where users help train robot behavior models by uploading video data. The platform provides a mobile application, allowing users to earn OG Mecka Points by completing tasks, further incentivizing data contributions. MeckaAI is committed to promoting the development of robot 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 robotics field through resource allocation. Its internal innovation center, Deus Lab, focuses on the research and development of robotics technology, while MachineDAO votes on resource allocation to various projects by staking the $DEUS token. This model provides financial support for the open-source development of robotics technology while ensuring transparency and fairness in resource allocation.  ![](https://img.gateio.im/social/moments-e21150175fb8280ec0b27564ab8b7526)*MachineDAO's organizational structure*  Due to space constraints, there are 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 infancy, its potential cannot be ignored. With the development of physical AI and embodied AI, as well as the emergence of new concepts such as 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 earn rewards based on the quality of the data. For example, 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 lot 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 Verification: How to ensure the authenticity and accuracy of crowdsourced data?* Ethical issues: How to regulate the use of data to avoid privacy breaches and abuse?* Acceptance of the demand side: Are traditional institutions willing to adopt decentralized data sets?  The resolution of these issues will determine the future direction of DeSPIN and requires further research and solutions in the future.