Description: Trees with a minimum height of 6ft were detected and delineated using a deep learning approach. For dispersed trees, canopy segments correspond to an individual tree crown, however within dense forests they will generally capture the dominant and co-dominant tree crowns. Each detected tree is characterized by a polygon representing the crown extent and a point representing the tree top (or center in the case of complex deciduous canopies). Approximately 1.4 million trees were identified in the project area. Tree segment polygons are primarily derived from lidar information, but due to the dual vintages of the source data used in this analysis (2021 and 2025) and the large amount of change between the two time periods, additional tree segments were delineated using imagery information. Tree species, condition, size, and land use location information were attributed to each object. Lidar-derived segments contain the full suite of attributes because both spectral and structural information was available for these trees. Imagery derived segments contain only species, health, and 2D size information because temporally appropriate lidar information was not available to support the calculation of height, leaf area index (LAI), presence of branches beneath 6ft, or diameter at breast height (DBH).