This research proposes a novel design approach for the creation of open-source software ecosystems (SECO) specifically for territorial monitoring. The primary aim is to integrate a wide array of data sources, including satellite imagery and ground-based sensors, to build a cohesive and adaptable system for monitoring various environmental factors. The core of this approach lies in the semantic integration of these diverse data streams to create a unified model that can be used for real-time territorial analysis. A use case is explored where air quality, temperature, and walkability indices are evaluated as key indicators of Quality of Life (QoL) in urban areas. The research leverages multiple machine learning algorithms, including Random Forest, Adaboost, GradientBoosting, and XGBoost, to process and analyze the collected data. The datasets used for this study include air quality data from India, weather data, and urban sustainability metrics. This integrated approach aims to provide valuable insights into urban planning, sustainability, and environmental monitoring, offering a scalable framework for future territorial monitoring systems.

Keywords: Open-Source Software Ecosystems (SECO), Territorial Monitoring, Semantic Integration, Satellite Imagery, Ground-Based Sensors, Air Quality, Temperature Monitoring, Walkability, Quality of Life (QoL), Machine Learning Algorithms, Random Forest, Adaboost, GradientBoosting, XGBoost, Urban Sustainability, Environmental Data Analysis, Urban Planning, Data Integration.