Revolutionizing Refined Urban Governance with High-Quality Datasets

In intelligent algorithms driven by data, the quality and quantity of data determine the learning efficiency and decision-making precision of AI systems. Different from traditional programming, machine learning and deep learning models rely on massive training data to “self-learn” patterns and rules. Therefore, building and maintain datasets has become the core mission in AI research and development. Through continuously enriching data samples, AI model can handle more complex real world problems, as well as improving the practicality and applicability of technology.

The task of refined urban governance aims to use artificial intelligence (AI) technology to perform intelligent image recognition on problematic events (such as road damage, garbage dumping, road occupation, etc.) provide technical support.

Data labeling method and AI algorithm for refined urban governance

· Data annotation is usually presented in the form of event label annotation + specific object rectangle frame annotation.

Relevant algorithms are essentially target detection + target classification tasks. This type of task is a basic task of artificial intelligence. It is usually developed based on deep learning and is identified by extracting specific target features and background features of different events. Commonly used target detection + target classification algorithms (such as yolo) frameworks and networks (such as resnet) can be used.

The difficulty of fine urban governance tasks mainly lies in the background complexity of real urban scenes and the complexity of specific goals in events.

Real city background complexity

The types, positions, and shapes of objects in images in different locations and different perspectives in the city are very different. For example, alleys, urban main roads, and street merchants all have completely different target distributions, which makes it difficult for the algorithm to extract effective features.

The specific target complexity involved in the event

The appearance of the main target is also very different under a specific event. For example, road damage may have different shapes and degrees of damage. Road operators may be trolleys, tricycles or motor vehicles. Advertisements may be banners, vertical plaques, garbage It may be oversized waste, kitchen waste, etc. The complexity of the specific target also puts forward higher requirements for the accuracy of the algorithm.

Nexdata has designed a series of training data systems to solve refined urban governance tasks. Through higher-quality finished data sets, it can solve the AI task requirements and difficulties in the governance process, and provide technical support for urban governance personnel to carry out their work, thereby improving Improve the level of urban governance and upgrade smart cities.

10 Categories – 8,085 Groups of Urban Refined Management Data

10 Categories – 8,085 Groups of Urban Refined Management Data. The collection scenes include street, snack street, shop entrance, corridor, community entrance, construction site, etc. The data diversity includes multiple scenes, different time periods(day, night), different photographic angles. The urban refined management categories in the images were annotated with rectangular bounding boxesThis data can be used for tasks such as urban refined management.

189 Videos-Electric Bicycle Entering Elevator Data

189 Videos-Electric Bicycle Entering Elevator Data，the total duration is 1 hour 58 minutes 40.72 seconds. The data covers different types of elevators, different types of electric bicycles, different time periods. The data can be used for tasks such as electric bicycle detection, electric bicycle recognition .

In the future data-driven era, the development prospects of artificial intelligence are infinite, and data is still a core factor for AI to unleash its full potential. By building richer datasets and advanced annotation technology, we can certainly promote more breakthroughs in AI in all walks of life. If you have data requirements, please contact Nexdata.ai at [email protected].