Data for figures.

Non-image data that were used to generate figures shown in the paper, if not already contained in S1–S3 Data. (XZ)</p

Result of polyp activity prediction for the red <i>Paragorgia C</i>_<i>r</i> using our LSTM approach.

Plots A) and B) show the ground truth and the results for and , respectively. For better visibility, the original results are smoothed using a Gaussian filter with σ = 5. If no value is available for the next or previous hour, values in the smoothed curve are marked with an “x”. For details on the handling of missing data before smoothing see S7 Text.</p

Features used as input for LSTM-based activity prediction.

Features used as input for LSTM-based activity prediction.</p

Region size development of coral <i>C</i>_<i>b</i>.

The plot shows the region size development of Cb during time period Γ2 in the images recorded by stereo camera sensor K0. Coral sizes in images recorded from different camera angles are marked by different colors. Region Segmentation was done using U-Net f*. The lower threshold applied to remove false positive Cb regions is shown as a red line.</p

Example patch classification result for <i>C</i>_<i>r</i> and <i>C</i>_<i>b</i>.

Frames of extracted patches are shown in dark yellow if the patch was classified as showing extended polyps, while edges of patches classified as showing retracted polyps are colored blue. Gamma correction with γ = 0.3 was applied to improve visibility of the corals. Patches were generated based on masks segmented by model f1. Patch classification was done using models g1,r and g1,b for Cr and Cb, respectively.</p

Plots of polyp activity and sensor data.

The polyp activity time series a(t) is shown together with the non-image sensor data used as LSTM input features (see Section 3.6). (PDF)</p

Jaccard scores per segmentation model and test dataset.

Jaccard scores per segmentation model and test dataset.</p

Annotations for the segmentation experiments.

Pixel-wise annotations used to generate ground truth segmentation masks for segmentation model training and evaluation. (XZ)</p

Macro-averaged <i>F</i>₁ scores () per test set, classification model, and coral.

Macro-averaged F1 scores () per test set, classification model, and coral.</p

Example image with (A)) and without (B)) gamma correction with <i>γ</i> = 0.3.

The background of image B) is very dark, such that the corals, in particular Cb, cannot be visually assessed well. (PDF)</p