This report addresses the challenge of efficiently processing and interpreting hyperspectral data cubes collected during field operations, where their high spectral dimensionality and large file sizes often hinder immediate analysis. It presents a practical workflow and software tool that enables rapid inspection and decision-making in the field. The methodology combines the collection of drone images and the creation of orthomosaics with in-field gathering of ground-truth data to guide annotation and validate results. Using annotated drone imagery, the workflow trains machine learning algorithms to classify habitats—potentially in a hierarchical structure. As a case study, the report showcases a coastal mapping campaign focused on identifying seagrass, seaweed, kelp, sediment, and rock. This approach supports detailed yet efficient mapping, contributing to the understanding and sustainable management of valuable marine ecosystems.