NESP TWQ Round 3 - Project 3.2.3 - Monitoring aesthetic value of the GBR by using artificial intelligence to score photos and videos
This project addresses the urgent need to understand and monitor the aesthetic value of the Great Barrier Reef. Focusing on the fast-changing underwater systems of the Reef, this research used advanced technology (including eye tracking and heart rate measurement) to elicit what environmental and experiential attributes contribute to aesthetic value. A Big Data platform using artificial intelligence was created to assess large volumes of visitor supplied imagery and to map aesthetic value across space and time.
More details about this project can be found on the Monitoring the Great Barrier Reef Griffith Inititue for Tourism website.
This project is continuing under NESP TWQ Round 5 - Project 5.5 – Measuring aesthetic and experience values using Big Data approaches
Videos
Datasets
This dataset contains the caffe deep-learning framework along with the setup for image aesthetic train and test code for the Algorithm data.
Organizing focus groups was used as an effective qualitative research method to examine collective opinions of participants on a specific topic.
The second stream within the NESP 5.5 project was conducted using eye-tracking technology to examine possible differences between three participant groups in evaluating the aesthetic beauty of GBR underwater sceneries.
The last stream within the NESP 5.5 project was related to the conduct of an online survey to get aesthetic ratings of additional 3500 images downloaded from Flickr to improve the Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes, 
This dataset resulted from two inter-linked research streams. The first stream was related to the application of eye-tracking technology and an online survey in studying natural beauty.