Forested areas play an integral role in the maintenance of both local and global environments. They are the bulk of Earth’s carbon sequestration for mitigating anthropogenic processes, provide natural erosion and runoff control for flooding events which have been growing in frequency because of climate change, and can offer respite for urban heat islands. The effective creation of canopy data is of utmost importance to analyze the aforementioned processes in addition to forest patterns such as disturbance, mortality, and the societal and economic effects forests can provide. Because of the importance of forests and the cycles they are apart of, it is imperative that systems are created that enable the effective monitoring of forest canopy. In particular, canopy classification using remotely sensed data plays an essential role in monitoring tree canopy on a large scale. As remote sensing technologies advance, the quality and resolution of satellite imagery have significantly improved. Oftentimes, leveraging high-resolution imagery such as the National Agriculture Imagery Program (NAIP) imagery requires proprietary software. However, the lack of insight into the inner workings of such software and the inability of modifying its code lead many researchers towards open-source solutions. In this research, we introduce CanoClass, an open-source cross-platform canopy classification system written in Python. CanoClass utilizes machine-learning techniques including the Random Forest and Extra Trees algorithms provided by scikit-learn to classify canopy using remote sensing imagery. One such similar Python module that is based on scikit-learn is DetecTree, but it does not utilize near-infrared (NIR) band imagery. Subsequently, to the best of the authors' knowledge, there are no dedicated tree canopy classification libraries that use scikit-learn in conjunction with infrared data.