Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystemstructure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Netmodel adapted for regression to map the canopy height of all trees in the state of California with very highresolution aerial imagery 0.6 m from the USDA-NAIP program. The U-Net model was trained using canopyheight models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP imagescollected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km2 areasacross various forest types and landscape variations in California. Our predictions of tree heights exhibited amean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present inCalifornia. In 2020, trees taller than 5 m covered ∼ 19.3% of California. Our model successfully estimatedcanopy heights up to 50 m without saturation, outperforming existing canopy height products from globalmodels. The approach we used allowed for the reconstruction of the three-dimensional structure of individualtrees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation andmapping capability, even in the presence of image distortion. These findings demonstrate the potential of largescale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.