Methods for monitoring the Earth’s tree cover using satellite imagery are often limited; existing approaches may involve labor-intensive visual interpretation of the imagery, or simplified pixel-based techniques. The objective of this project is to improve environmental monitoring by creating an automated tree detection system, which uses the cutting-edge machine learning algorithm YOLOv8. Our system will do better tree detection since it will isolate and outline trees in different terrains, and it will scale better than traditional methods. It will analyze open spaces to estimate potential for new tree planting in addition to counting existing trees, which can also provide valuable insights into reforestation. This model will help support environmental and urban planning initiatives by filling the gap in real time tree cover monitoring. An approach to developing an integrated system that can learn a quicker and more effective understanding of quantifying green spaces is to use real-time satellite data and image-processing workflow to process the satellites with spontaneous moral committees and to deliver a proactive system for each development of green-space management. One way is to introduce an integrated framework that has learned a smarter and more accurate way to quantify green spaces through real-time satellite data and image-processing workflow (e.g., processing the satellites using spontaneous moral committees) and to prevent imbalance development in each evolution of green-space maintenance.