The rapid growth of earth observation imagery from satellites, drones, aerial platforms, and UAVs has revolutionized the fields of Geographic Information Systems (GIS) and artificial intelligence (AI). Orbica has been at the forefront of research and development, harnessing this imagery to develop advanced AI solutions for detecting diverse vegetations & species using different sources such as satellite, aerial, and high-resolution drone imagery. This cost-effective tool offers extensive applications, including identifying informal settlements, mitigating wildfire fuel risks, supporting urban planning, assessing tree proximity to powerlines, detecting environmental changes, aiding disaster response efforts, and facilitating humanitarian mapping initiatives. Powered by state-of-the-art deep learning techniques such as convolutional neural networks (CNN) and transformers, the tool seamlessly integrates core geographic principles to accurately identify various vegetation types and species, including Native/Exotic forests, Shelterbelts, and Wilding conifers such as Pine radiata, Douglas fir, and Contorta. Geoprocessing techniques utilizing popular open-source libraries like GDAL, Shapley, Opendatacube, Xarray, Postgres, and others are employed to further enhance the AI models' feature classification capabilities. The generated features are conveniently served as an API/Tile service, enabling effortless integration into mapping applications and diverse geospatial workflows. Continuous feedback loops ensure ongoing improvements in dataset accuracy and precision, resulting in a progressively refined dataset over time. This research presents the evolution of AI-based tree/vegetation detection models, showcasing their remarkable ability to leverage diverse imagery sources and open-source technologies to effectively address a wide range of environmental and socio-economic challenges.