Development of a GIS-Based Urban Green Space Monitoring Mobile Application with Machine Learning Integration

The client requires a reliable, scalable digital solution to facilitate community-driven data collection on urban green spaces, specifically trees, to support the creation of an up-to-date green map. The current challenge involves engaging diverse user groups, ensuring ease of use, and managing accurate data input for environmental monitoring and future AI-powered identification.

A non-governmental organization focused on environmental data collection, civic participation, and urban green space monitoring, seeking to engage the community through a user-friendly mobile app.

• Develop a cross-platform mobile application to enable volunteers and citizens to input spatial, descriptive, and visual data about urban trees and green areas.
• Create a cloud-based infrastructure for scalable data storage, processing, and machine learning model training.
• Implement a machine learning component capable of recognizing tree species from images to automate and enhance data accuracy over time.
• Design the app to support future feature expansion, including AR-based gamification and other interactive educational tools.
• Ensure the application is accessible to a broad demographic, including young students and senior citizens, to maximize civic participation and environmental awareness.

• User-friendly interface for inputting location data, descriptions, and images of trees and greenery
• Real-time data submission to a cloud database, supporting both Android and iOS devices from a single codebase
• A scalable cloud infrastructure for data management and processing
• Integration of a Machine Learning model that processes images to identify tree species
• Modular architecture designed for future enhancements such as AR gamification and educational modules
• Secure user authentication and data privacy compliance

• Mobile device sensors and camera APIs for image capture
• Cloud-based database for data storage and retrieval
• Machine Learning API for species recognition processing
• Potential future integration with AR frameworks for gamification

• High system availability and scalability to accommodate increasing user participation
• Data input and processing latency minimized to ensure real-time user feedback
• Strong data security and user privacy protections
• Cross-platform compatibility with synchronized updates for Android and iOS
• Maintainability with modular code architecture for future feature additions

The project aims to facilitate widespread community engagement in urban environmental monitoring, resulting in a comprehensive, up-to-date green map that benefits city planning and conservation efforts. By enabling automated species identification, the system will reduce manual effort and improve data accuracy. Anticipated outcomes include increased volunteer participation, enriched environmental data repository, and scalable platform infrastructure that supports future technological enhancements.