Detailed explanation of the new concept DeSPIN: 8 projects worth paying attention to

Original author: cookies

Original translation: TechFlow

With the continuous development of Web3 technology, Decentralized Spatial Intelligence Network (DeSPIN) is becoming a field of great concern. By analyzing and utilizing visual data in the real world, DeSPIN not only provides innovative solutions for map construction, urban planning, and robotics, but also opens up a new Contribute-to-Earn economic model. This article will explain in detail the core concepts, main protocols and future development directions of DeSPIN.

What is DeSPIN?

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

Before understanding the specific application of DeSPIN, we need to first understand the basic framework of spatial intelligence. Spatial intelligence consists of four core parts:

• Data collection: Collect data with the help of sensor networks (such as cameras, GPS) and IoT devices (such as mobile phones, 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 visualized geographic information.

• Decision support system: Build spatial prediction models to provide users with application services such as route optimization, obstacle avoidance, etc.

Main protocols in the DeSPIN field

Currently, several 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 problems in real time through mobile applications, and drivers collect data through dashcams installed on their vehicles. These data are processed by AI algorithms to generate maps, and their accuracy is verified through Reinforcement Learning with Human Feedback (RLHF). Hivemapper provides coverage maps, and users can see which areas have been mapped and access data through APIs. Data contributors are rewarded with $HONEY tokens, 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 dashcams, and adopts a drive and earn model. Its core technology VX 360 supports 360-degree panoramic data collection, and the collected data can be used to develop driver assistance functions such as autonomous driving optimization. Currently, NATIX Network has covered 171 countries, with more than 223,000 registered drivers and a cumulative mapped mileage of 131 million kilometers. Data contributors and network nodes can receive $NATIX token rewards to further stimulate ecological development.

Both Hivemapper and NATIX are committed to building better maps through crowdsourced road data. The potential application scenarios of this data are very wide, mainly including the following aspects:

• Optimizing urban traffic: By analyzing road data collected in real time, traffic flow management can be improved, congestion can be reduced, and travel efficiency can be improved.

• Monitor road conditions: Promptly detect and report road damage, obstructions, or other potential problems to help maintain the safety and reliability of infrastructure.

• Detecting crime and violence: Using map data combined with AI algorithms can help identify and locate abnormal behavior and provide support for public safety.

These applications not only improve the functionality of maps, but also bring practical value to urban management and social security.

3. FrodoBots

FrodoBots is a protocol for gamified data collection through robots. Users can remotely control ground robots to collect geographic data, and it supports multiple modes of operation (such as controllers, keyboards, or game steering wheels). In addition, researchers can deploy AI navigation models on the platform for testing. Users earn FrodoBot Points (FBPs) by completing driving tasks. The points are related to the distance and difficulty of the task. The longer the distance and the higher the difficulty, the more points. FrodoBots has been tested in multiple cities and held navigation ability competitions between AI and humans. In addition, FrodoBots has also established a guild-like system Earth Rovers School, which allows 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 3D models. The platform provides high-quality 3D data for creating 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. 聽

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

5. PrismaXAI

PrismaXAI is a protocol for collecting scene-specific 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 improving the accuracy of data annotation through a decentralized verification mechanism. This protocol has great potential in acquiring long-tail data and provides unique advantages for model training.

6. OpenMind AGI

OpenMind AGI focuses on understanding the real world through vision-language-action models (VLAMs). Its core system, OM 1, is a multi-platform operating system that can interact with dynamic real-world environments and is particularly suitable for customized development of robotics. The platform collects data through mobile phones and robots and shares this data with robot developers to improve and innovate robot application scenarios.

7. MeckaAI

MeckaAI is a decentralized robot AI model training protocol. Users help train robot behavior models by uploading video data. The platform provides mobile applications , and users can earn OG Mecka Points by completing tasks to further incentivize data contribution. MeckaAI is committed to promoting the development of robotics technology through crowdsourcing and lowering the threshold for obtaining training data.

8. Xmaquina DAO

Xmaquina DAO is a decentralized autonomous organization (DAO) that supports open source robotics 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 the research and development of robotics technology, while MachineDAO votes on which projects to allocate resources to by staking tokens $DEUS. This model provides financial support for the open source development of robotics technology while ensuring transparency and fairness in resource allocation.

MachineDAO鈥檚 organizational structure

Due to space limitations, there are some application protocols in similar fields that are not elaborated in detail here, such as Alaya_AI , Gata_xyz , KrangHQ , etc., which are also worthy of attention.

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 rise of new concepts such as the Human Data Fleet, DeSPIN is expected to lead a new technological revolution.

One possible trend is the popularization of the Train-to-Earn (T2E) model, where users contribute value through spatial data obtained in daily life and are rewarded based on the quality of the data. For example, the emergence of decentralized eyewear devices can greatly improve the accuracy and diversity of data collection. The data captured by smart glasses can not only reflect the way humans perceive the world in the most realistic way, but also collect a lot of long-tail data such as environmental noise and facial features, bringing wider 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 leakage and abuse?

• Demand-side acceptance: Are traditional institutions willing to adopt decentralized datasets?

The solution to these problems will determine the future direction of DeSPIN and needs to be further studied and solved in the future.

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