SeaBee documentation

This website provides technical documentation for various aspects of the SeaBee workflow.

To learn more about the SeaBee project itself, please visit

1 Overview

A simplified version of the SeaBee data flow is illustrated in Figure 1. Geotagged images from aerial drones are combined with ground control points using orthorectification software to produce georeferenced image mosaics.

Ground-truth data collected in the field are used to aid annotation of the mosaics to create training datasets for machine learning algorithms. These algorithms are applied to generate data products, which are made available via the SeaBee GeoNode.

Figure 1: Simplified SeaBee workflow.

The full SeaBee workflow is more complex than illustrated above. In particular:

  • Standard aerial drones are flown with both RGB and multispectral cameras. In addition, the project includes hyperspectral data as well as imagery from other types of drone, such as the Otter (an unmanned surface vehicle).

  • Typical data products from the machine learning include both image segmentation (e.g. habitat mapping) and object identification (e.g. seabird or mammal counts).

2 Sigma2

As far as possible, data handling for the SeaBee project is hosted by Sigma2 on NIRD, the National Infrastructure for Research Data. The SeaBee project has its own Kubernetes namespace on Sigma2. A high-level schematic of the current configuration is shown in Figure 2.

Figure 2: Simplified Sigma2 architecture.