Training Projects
DeepSense has compiled a few data sets for students, and others interested in the ocean and AI, so they can have the opportunity to complete AI projects independently. We hope participants can learn about a specific type of ocean related data, and experience an explicit AI project. It is expected that the participants work on the project alone, but we have provided some guidance that includes notebooks, data, outputs and models to try to improve upon.
We have found that the data cleaning step can take a long time, so our hope is that these datasets will be reasonably clean, allowing the participants to explore ocean AI.
Object Detection
We used the google open images database to obtain approximately 650 images of starfish. The images were already separated into train, test and validation sets. The metadata linked below is only for the starfish images, not for the entire dataset. The metadata includes coordinates for bounding boxes around the starfish.
If you want to download other categories of images from the open images database, you can do so by following the instructions here:
Regression
Predicting the values of one buoy using the parameters of another buoy. In this project, we are using the dataset of Mouth of Placentia Bay Buoy, Pilot Boarding Station / Red Island Shoal Buoy, Placentia Bay: Ragged Islands – KLUMI( Land station) which are located in Newfoundland and Labrador.
Datasets
Pilot Boarding Station / Red Island Shoal Buoy
Placentia Bay: Ragged Islands – KLUMI( Land station)
The dataset available here is till April 19, 2021. You can get the latest dataset from smartatlantic.
REFERENCES
A MACHINE LEARNING REDUNDANCY MODEL FOR THE HERRING COVE SMART BUOY
