The Earth Science and Remote Sensing Unit at NASA's Johnson Space Center maintains an enormous collection of over four million astronaut photographs of Earth, spanning manned spaceflight from the Mercury missions to today's ISS missions. These photos can be vital to researchers across many fields, but many are inaccessible because they lack labels for common features like islands, volcanoes, and rivers. Feature Hunter is the first step toward the creation of a machine learning model aimed at labeling previously uncategorized Astronaut Photography with geographic features. Machine learning requires large sets of training data to produce accurate results. Feature Hunter users will help develop this training data by viewing images, determining whether or not a feature or features are present, and identifying features by placing bounding boxes around them. Then, experts in the Earth Science and Remote Sensing Unit will use this data to develop and deploy a machine learning model to add feature-level metadata to the entire astronaut photography database.
Geographic Scope: Whole Earth (astronauts collect imagery of all continents but Antarctica)
Project Status: Active - recruiting volunteers
Participation Tasks: Identification,
Start Date: 07/01/2019
Project Contact: email@example.com
Federal Government Sponsor:
Other Federal Government Sponsor:
Fields of Science: Ecology and environment, Geography, Geology and earth science
Intended Outcomes: Feature Hunter is aiming to improve the astronaut photography database by utilizing machine learning to identify of features within images with the help of training data generated by citizen scientists.