Search for: All records

The Backyard Buoys project (https://backyardbuoys.org/) enables Indigenous and coastal communities to gather and use wave data to enhance their blue economies and hazard protections. These communities have been historically underserved, and climate change is making weather and wave predictability even harder. Leveraging low-cost, scalable marine technology in partnership with regional ocean observing networks, Backyard Buoys offers a system for community-managed ocean buoys and data access to complement Indigenous Knowledge. These innovations include a sustainable process for community-led implementation and stewardship of affordable ocean buoys along with co-designed and co-produced mobile and web-based applications (apps) that render data easy to access and understand. 

We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification.