To ensure repeatability and ease-of-use, we provide a comprehensive workflow with detailed instructions and open-sourced the programming code to assist others in replicating our methodology. Although SfM has become widely adopted by ecologists, deep learning presents a steep learning curve for many.
Furthermore, because each image only needs to be provided with a single set of dense labels this method scales linearly making it useful for large areas or high resolution-models. When quantitatively validating the classification results we found that this method is capable of accurately projecting semantic labels from image-space to model-space with scores as high as 91% pixel accuracy. Unlike other methods, ours involves creating dense labels for each of the images used in the 3-D reconstruction and then reusing the projection matrices created during the SfM process to project semantic labels onto either the point cloud or mesh to create fully classified versions. We present a novel method that can efficiently provide semantic labels of functional groups to 3-D reconstructed models created from commonly used SfM software (i.e., Agisoft Metashape) using fully convolutional networks (FCNs). This lack of granularity makes it more difficult to identify the class category responsible for changes in the structure of coral reef communities. However, there exist few efficient methods that can classify portions of the 3-D model to specific ecological functional groups. Structure-from-motion (SfM) algorithms that utilize images collected from various viewpoints to form an accurate 3-D model have become more common among ecologists in recent years.
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