Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park
Bibliographic record
Abstract
Abstract Marine habitat maps are essential tools for marine spatial planning, providing information for decision‐making in conservation and resource management. Accurate classification of benthic habitats supports their sustainable use and identifies key areas for protection. Convolutional neural networks (CNNs) are powerful deep learning algorithms that have shown promise for advancing habitat classification tasks and mapping complex marine environments. This study compares the performance of a CNN and a Random Forest (RF) model in classifying benthic habitats within Apollo Marine Park, Victoria, Australia. Models were trained to classify three distinct habitat types using bathymetry, multibeam backscatter, wave height and positioning data; however, the RF model had access to 100 additional bathymetric derivatives, of which 10 were selected as predictors. The CNN achieved an overall accuracy of 67.32%, while the RF model achieved 62.57%. For individual habitats, the CNN obtained F1‐scores of 0.664 for high energy circalittoral rock with seabed‐covering sponges , 0.538 for low complexity circalittoral rock with non‐crowded erect sponges and 0.774 for infralittoral sand and shell mixes . The corresponding RF scores were 0.598, 0.506 and 0.739. Both models encountered challenges in classifying transitional habitat zones, where diffuse boundaries between habitat types led to overlaps and shared acoustic properties. However, the CNN demonstrated an advantage due to its ability to automatically analyse spatial patterns across multiple scales. In contrast, while the RF model incorporated terrain attributes that capture local variation, its ability to utilize spatial context was constrained to predefined scales of the derived features. The CNN's ability to leverage spatial relationships resulted in clearer and more coherent habitat maps, reducing the salt‐and‐pepper effect commonly observed in pixel‐based classifications. This study highlights the potential of CNNs for marine habitat mapping through their ability to classify data derived from multibeam bathymetry, while also identifying avenues for further refinement to enhance their utility in marine spatial planning tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".