Drones have become an important tool in monitoring coastal and marine ecosystems, for researchers, managers and local communities. Currently there is a lack of standardization and automation in the workflows. Building workflows based on open-source tools to deliver standard products that are ready for use in machine learning can enable researchers and communities to deliver FAIR datasets. We present a workflow that has been developed using open-source Python tools that accepts images from a range of drones. The workflow applies corrections to the image position and stores them with meaning full names in a regular directory structure while providing quality data management metrics and feedback. For habitat assessment the system builds a series of machine learning ready tiles using direct geo referencing. This approach is fast, efficient and allows for multiple looks at the same area. For object identification we have adapted a popular opensource tool labelImg to allow for efficient human classification of marine objects such as turtles and sharks. The workflow then produces a training set ready to be shared for use in machine learning.