AI-driven Approach to City Management

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In the ever-evolving landscape of urban environments, the integration of Artificial Intelligence (AI) has become a powerful catalyst for change. In this article, we delve into three use cases of Geometa platform that showcase how AI is changing urban management. From enhancing safety to fostering sustainability, these applications demonstrate the immense potential of AI in shaping the cities of tomorrow.

Geometa is a web platform that fuses spatial data, engineering documents, and business information to create a digital twin of cities. By leveraging AI insights, it offers an integrated platform for urban management, enabling informed decision-making.

Use Case: AI-powered Satellite Image Analysis Unlocks Hidden Buildings for Revenue Boost

Problem: Unregistered buildings, often constructed without the necessary permits or regulatory oversight, not only compromise safety standards but also hinder effective urban planning and resource allocation.

Solution: Geometa analyzes satellite imagery to identify rooftops of buildings. By comparing these rooftops with existing databases of registered buildings, the system efficiently flags potential unregistered structures for further inspection.


  • Regulatory Compliance: Registering informal buildings ensures they meet safety and building code requirements.
  • Enhanced Safety: Identifying unregistered structures enables safety inspections and necessary improvements, reducing risks to occupants and neighboring properties.
  • Improved Urban Planning: Inclusion of unregistered buildings in urban development strategies optimizes resource allocation and public services.
  • Increased Tax Revenue: Registering unregistered buildings allows collection of property taxes, funding public services and infrastructure projects.

Use Case: Harnessing Citizen-Captured Imagery for Effective Public Space Management

Problem: Assessing the attractiveness and popularity of urban public spaces and understanding their appeal to citizens is a challenging task for city administrations. Traditional methods lack comprehensive data on the preferences and sentiments of people regarding different locations within the city.

Solution: Geometa utilizes a specially developed neural network to classify social media photos taken by individuals in urban environments. By translating each image to a geolocation point and assigning coordinates, Geometa creates a map that accumulates data and visually represents the popularity of urban public spaces. The date of the photo is used to analyze trends and visualize the frequency of photo capture over time.

Benefits: The tool improves real estate valuations, evaluates renovation projects, understands citizen preferences, and provides visual representations of public space popularity.

Use Case: Optimizing Urban Planning with AI-Powered Vehicle Analysis and Density Mapping

Problem: Urban planners face challenges in accurately understanding the utilization of parking spaces, leading to difficulties in planning adequate parking lots and establishing effective urban design standards.

Solution: Geometa utilizes neural network to analyze orthophotomaps or satellite images. The neural network distinguishes vehicles, classifying them as cars or trucks, and assigns a centroid to each vehicle as its representation in the system. This process generates a thematic data layer. The system accumulates historical data and enables dynamic analysis of vehicle distribution over time.

Benefits: Urban planners can make informed decisions related to parking lot design, transportation infrastructure improvements, urban design standards, and future urban development projects. This data-driven approach ensures that urban planning aligns with the actual needs and demands of residents. Also, the detailed vehicle distribution data can be leveraged to regulate parking lot prices effectively.

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