NSF Award
2335921
The broader impact/commercial potential of this I-Corps project is the development of an automatic training dataset labelling tool and service. Currently, labelling is accomplished manually, which is time-consuming, and there is a high demand for high-quality training datasets. While popular spoken language applications can leverage numerous teams of dedicated training dataset general labelers to help create training datasets, it poses a significant challenge for scientific and professional domains to produce high-quality training datasets to feed into Artificial Intelligence/Machine Learning (AI/ML) models and algorithms. The proposed technology uses a semi-automatic training dataset labelling tool and online management service to facilitate automatic cropping, segmenting, and labelling of image datasets. Images are a popular data type that is widely used in climate change, smart cities, medical and health domains. The proposed technology may reduce the time and resources spent on obtaining training datasets by millions of data science professionals, researchers, and students, as well as companies and other organizations.<br/><br/>This I-Corps project is based on the development of a software training dataset labelling tool and online service that works automatically to fill the gap of missing high-quality training image datasets. Spatiotemporal Artificial Intelligence/Machine Learning (AI/ML)-based capabilities are under development to automatically classify, label, store, and share training datasets among a group of users. The proposed technology is designed to provide an automated data labeling software tool to automatically tag data and help to identify and classify the data. This process may be used to create training datasets for machine learning models. Specifically, this is an image labelling tool that may be used to classify and label digital images. The proposed technology was developed initially for sea ice research and results confirmed that sea ice images can be automatically labelled. This technology potentially may be used in many areas including climate change and biomedical research.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
