Name
Abstract | ||
|---|---|---|
Earth science data archives have significantly increased in size due to the number of advanced sensors and science missions. In the meantime, Earth science data systems have not taken advantage of data driven technologies to provide advanced search capabilities. This paper discusses a machine learning based approach, an enabling data driven technology, to detect Earth science events from image archives. The automated event detection is cataloged in an event database that provides a novel way to explore large archives of data. In addition, a phenomena portal to visually explore events and contextual information is discussed. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323549 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Machine learning, neural network, Earth science, labeled data, training data, portal, event detection | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manil Maskey | 1 | 0 | 0.34 |
| Rahul Ramachandran | 2 | 0 | 0.34 |
| Iksha Gurung | 3 | 0 | 2.37 |
| Muthukumaran Ramasubramanian | 4 | 0 | 0.34 |
| Brian Freitag | 5 | 0 | 0.34 |
| Aaron Kaulfus | 6 | 0 | 0.68 |
| Georgios Priftis | 7 | 0 | 0.34 |
| Drew Bollinger | 8 | 0 | 0.34 |
| Ricardo Mestre | 9 | 0 | 0.34 |
| Daniel da Silva | 10 | 0 | 0.34 |