Objectives of the service
GeoSpaceAI empowers key stakeholders in the micro-mobility ecosystem, including micro-mobility operators, urban transport authorities, and urban planners. By leveraging insights derived from Earth Observation (EO) satellites, AI and geospatial technologies, these stakeholders can optimize micro-mobility deployments, manage fleets more effectively, and contribute to the creation of a sustainable and efficient urban transportation system.
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Engaged stakeholders: The expanded potential customer base will open communication with key stakeholders (e-mobility operators, city authorities and utilities). This feedback will guide service development and market focus.
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Market analysis: Conducted research in Germany and Europe to understand diverse regional needs and ensure solutions are effectively targeted.
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Refined services: Based on user and customer analysis, we have updated our service offerings and are exploring the integration of valuable data such as charging point information.
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Data Analysis: Complete initial data analysis using existing satellite imagery.
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Data Processing: Develop a core data processing engine to handle Daily and historical data streams.
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Partnerships: Establish partnerships with EO data providers to expand coverage and improve data quality.
Users and their needs
Customers require efficient and sustainable solutions to optimize routes, identify high-demand areas, and proactively address fleet vehicle issues. EO satellites and geospatial technologies provide valuable data and insights to fulfill these needs.
Key Users and Needs:
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Micro-Mobility Operators: Optimize deployments, reduce clutter, improve accessibility, enhance fleet tracking, predictive maintenance, and improved response times.
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Urban Transport Authorities and City Planners: Promote green transportation options, reduce traffic congestion, contribute to sustainable urban systems.
Service/ system concept
Capabilities:
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Daily Data Dashboards: Visualize key metrics such as vehicle location, battery levels, and usage patterns.
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Demand Forecasting: Identify high and low demand areas to optimize vehicle deployment.
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Route Optimization: Suggest efficient routes to rebalance fleets and reduce travel time.
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Maintenance Alerts: Receive notifications of possible vehicle issues based on sensor data.
How It Works:
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Data Collection: Satellite and ground IoT sensors collect data on traffic, weather, and EV locations.
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Data Processing: Advanced algorithms and machine learning analyze the data.
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User Interface: Insights and recommendations are delivered via a user-friendly web platform and or mobile app.
System Architecture:
User Benefits:
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Improved decision making: Data-driven insights enable informed decisions on fleet management and deployment strategies.
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Increased efficiency: Optimized routes and proactive maintenance reduce operating costs and downtime.
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Sustainable cities: Encouraging micro-mobility helps reduce congestion and improve air quality.
Space Added Value
Space Assets: Earth observation (EO) satellites provide:
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Wide-area coverage: Monitor entire cities at once, unlike ground sensors with limited reach.
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Daily and historical data: Track and analyse changes in demand and usage patterns over time.
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Weather insights: Optimize operations based on weather conditions, improving safety and efficiency.
Added Value:
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Superior Data Scope: M3S provides a more comprehensive view of micro-mobility activity compared to ground sensor-only solutions.
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Actionable Insights: EO data combined with other sources offers deeper insights for strategic decision making.
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Future-Proof Approach: Leverages the evolving capabilities of EO satellites and AI, ensuring continuous improvement and adaptation.
Current Status
Current Work:
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Integrate Daily data feeds from selected EO satellites and ground sensors.
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Refine machine learning algorithms for demand forecasting and route optimization.
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Design the user interface for the web platform and mobile application.
Next Steps:
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Customer Needs Analysis: Analyze collected e-mobility data to identify specific needs and preferences.
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Customer Engagement: Use data insights to refine M3S and gain deeper understanding through targeted interactions (meetings, interviews, surveys).
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Continuous Improvement: Evaluate and improve based on user feedback and real-world data.
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Pilot Testing: Test the M3S platform with a micro-mobility operator in a major city.
Prime Contractor(s)
