Researchers at the University of Southern California have introduced a cost-effective artificial intelligence platform designed to map urban tree canopy coverage using freely available aerial imagery. Developed by a team led by John Wilson, founding director of the Spatial Sciences Institute at USC Dornsife, the tool addresses a critical need for municipalities seeking to mitigate extreme urban heat through targeted green infrastructure investments. Traditional canopy mapping relies on expensive light detection and ranging surveys or costly commercial satellite data. The USC system bypasses these financial barriers by processing publicly accessible photographs from the U.S. Department of Agriculture National Agriculture Imagery Program, which are refreshed every two to three years. By training machine learning models on this open data, researchers generate high-resolution canopy maps that enable cities to evaluate existing tree cover and prioritize planting zones without incurring specialized survey costs. The platform was initially developed and validated in Boyle Heights and City Terrace, historically underserved Los Angeles neighborhoods with limited green cover. The canopy mapping model demonstrated high accuracy, while the individual tree detection algorithm successfully identified overlapping tree crowns, performing competitively against traditional lidar-based methods. To verify broader applicability, the team deployed the unaltered models across neighborhoods in San Francisco and Phoenix. Despite varying climates and urban layouts, the system maintained consistent performance, indicating strong cross-regional generalization and allowing other municipalities to adopt the framework without extensive retraining. Public adoption has already outpaced expectations. The ArcGIS deep learning package, distributed through the Esri Living Atlas platform, has surpassed 12,900 downloads in the past six months. By providing open-source code and a ready-to-use model, the researchers have lowered the technical barrier for local governments lacking in-house data science capabilities. The initiative directly supports the USC Urban Trees Initiative, a multi-year partnership guiding tree planting across Los Angeles neighborhoods based on heat vulnerability and canopy deficits. Published recently in the journal Remote Sensing, the project underscores USC strategic focus on leveraging artificial intelligence for geospatial problem-solving. Looking ahead, the research team plans to integrate the current canopy extent model with freely available lidar datasets to capture tree height and three-dimensional structure. This enhancement will enable cities to calculate existing shade coverage and simulate the cooling impact of future plantings at precise micro-geographies, including school playgrounds, parks, and individual street blocks. The tool deployment marks a significant step toward scalable, data-driven urban forestry. By democratizing access to high-quality canopy mapping, the platform empowers communities worldwide to construct more resilient, heat-adaptive environments as extreme weather events intensify.