Drone and ground-truth data collection, image annotation and machine learning: A protocol for coastal habitat mapping and classification

Abstract

Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:

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  Collection of drone images and creation of orthomosaics.

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  Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.

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  Annotation of drone images into – potentially hierarchical – habitat classes and training of machine learning algorithms for habitat classification.

As a case study, we present a field campaign that employed these methods to map a coastal site dominated by seagrass, seaweed and kelp, in addition to sediments and rock. Such detailed but efficient mapping and classification can aid to understand and sustainably manage ecologically and valuable marine ecosystems.