Oxia Planum, Mars, classified using the Noah-H deep-learning terrain classification system
Bibliographic Data
| ID | 16841014 |
|---|---|
| Authors | Alexander M Barrett (0000-0003-3441-9216, The Open University, corresponding author), Jack Wright (0000-0003-0481-0863, European Space Astronomy Centre), Edmundo Favaro (0000-0001-6126-5465, The Open University), Peter Fawdon (0000-0003-1900-8347, The Open University), Matthew R Balme (0000-0001-5871-7475, The Open University), M J Woods (0000-0003-3554-7631, Scisys (United Kingdom)), Spyros Karachalios (Scisys (United Kingdom)), Eleni Bohacek (0000-0002-9901-2751, European Space Research and Technology Centre), Elliot Sefton-Nash (0000-0002-5583-4438, European Space Research and Technology Centre), Luc Joudrier (European Space Research and Technology Centre) |
| Year | 2023 |
| Volume | 19 |
| Issue | 1 |
| Publication date | 2023-12-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Maps (JOURNAL) |
| Journal identifiers | ISSN: 1744-5647 • E-ISSN: 1744-5647 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/17445647.2022.2112777 |
| OpenAlex | W4292227720 |
| Language | EN |
| Citations received | 2 |
| References cited | 15 |
We present a map of Oxia Planum, Mars, the landing site for the ExoMars Rover. This shows surface texture and aeolian bedform distribution, classified using a deep learning (DL) system. A hierarchical classification scheme was developed, categorising the surface textures observed at the site. This was then used to train a DL network, the ‘Novelty or Anomaly Hunter – HiRISE’ (NOAH-H). The DL applied the classification scheme across a wider area than could have been mapped manually. The result showed strong agreement with human-mapped areas reserved for validation. The resulting product is presented in two ways, representing the two principle levels of the classification scheme. ‘Descriptive classes’ are purely textural in nature, making them compatible with a machine learning approach. These are then combined into ‘interpretive groups’, broader thematic classes, which provide an interpretation of the landscape. This step allows for a more intuitive analysis of the results by human users
Astrobiology · Cartography · Exploration of Mars · Geography · Mars Exploration Program · Planum temporale · Remote sensing · Terrain · Astro and Planetary Science · Computer Science · Planetary Science and Exploration · Psychology · Space Science and Extraterrestrial Life · Artificial Intelligence · Geology
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2024 (2) |
| Highly cited | No |