A Multi-Annotator Survey of Sub-km Craters on Mars
We present here a dataset of nearly 5000 small craters across roughly 1700 km<sup>2</sup> of the Martian surface, in the MC-11 East quadrangle. The dataset covers twelve 2000-by-2000 pixel Context Camera images, each of which is comprehensively labelled by six annotators, whose results a...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Language: | English |
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MDPI AG
2020-08-01
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| Online Access: | https://www.mdpi.com/2306-5729/5/3/70 |
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| author | Alistair Francis Jonathan Brown Thomas Cameron Reuben Crawford Clarke Romilly Dodd Jennifer Hurdle Matthew Neave Jasmine Nowakowska Viran Patel Arianne Puttock Oliver Redmond Aaron Ruban Damien Ruban Meg Savage Wiggert Vermeer Alice Whelan Panagiotis Sidiropoulos Jan-Peter Muller |
| author_facet | Alistair Francis Jonathan Brown Thomas Cameron Reuben Crawford Clarke Romilly Dodd Jennifer Hurdle Matthew Neave Jasmine Nowakowska Viran Patel Arianne Puttock Oliver Redmond Aaron Ruban Damien Ruban Meg Savage Wiggert Vermeer Alice Whelan Panagiotis Sidiropoulos Jan-Peter Muller |
| author_sort | Alistair Francis |
| collection | DOAJ |
| description | We present here a dataset of nearly 5000 small craters across roughly 1700 km<sup>2</sup> of the Martian surface, in the MC-11 East quadrangle. The dataset covers twelve 2000-by-2000 pixel Context Camera images, each of which is comprehensively labelled by six annotators, whose results are combined using agglomerative clustering. Crater size-frequency distributions are centrally important to the estimation of planetary surface ages, in lieu of in-situ sampling. Older surfaces are exposed to meteoritic impactors for longer and, thus, are more densely cratered. However, whilst populations of larger craters are well understood, the processes governing the production and erosion of small (sub-km) craters are more poorly constrained. We argue that, by surveying larger numbers of small craters, the planetary science community can reduce some of the current uncertainties regarding their production and erosion rates. To this end, many have sought to use state-of-the-art object detection techniques utilising Deep Learning, which—although powerful—require very large amounts of labelled training data to perform optimally. This survey gives researchers a large dataset to analyse small crater statistics over MC-11 East, and allows them to better train and validate their crater detection algorithms. The collection of these data also demonstrates a multi-annotator method for the labelling of many small objects, which produces an estimated confidence score for each annotation and annotator. |
| first_indexed | 2024-03-10T17:59:38Z |
| format | Article |
| id | doaj.art-06ff6c46e4824d59b9ebbc660571ba94 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T17:59:38Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-06ff6c46e4824d59b9ebbc660571ba942023-11-20T08:58:25ZengMDPI AGData2306-57292020-08-01537010.3390/data5030070A Multi-Annotator Survey of Sub-km Craters on MarsAlistair Francis0Jonathan Brown1Thomas Cameron2Reuben Crawford Clarke3Romilly Dodd4Jennifer Hurdle5Matthew Neave6Jasmine Nowakowska7Viran Patel8Arianne Puttock9Oliver Redmond10Aaron Ruban11Damien Ruban12Meg Savage13Wiggert Vermeer14Alice Whelan15Panagiotis Sidiropoulos16Jan-Peter Muller17Mullard Space Science Laboratory, UCL, Holmbury Hill Rd, Dorking RH5 6NP, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKThe College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UKMullard Space Science Laboratory, UCL, Holmbury Hill Rd, Dorking RH5 6NP, UKMullard Space Science Laboratory, UCL, Holmbury Hill Rd, Dorking RH5 6NP, UKWe present here a dataset of nearly 5000 small craters across roughly 1700 km<sup>2</sup> of the Martian surface, in the MC-11 East quadrangle. The dataset covers twelve 2000-by-2000 pixel Context Camera images, each of which is comprehensively labelled by six annotators, whose results are combined using agglomerative clustering. Crater size-frequency distributions are centrally important to the estimation of planetary surface ages, in lieu of in-situ sampling. Older surfaces are exposed to meteoritic impactors for longer and, thus, are more densely cratered. However, whilst populations of larger craters are well understood, the processes governing the production and erosion of small (sub-km) craters are more poorly constrained. We argue that, by surveying larger numbers of small craters, the planetary science community can reduce some of the current uncertainties regarding their production and erosion rates. To this end, many have sought to use state-of-the-art object detection techniques utilising Deep Learning, which—although powerful—require very large amounts of labelled training data to perform optimally. This survey gives researchers a large dataset to analyse small crater statistics over MC-11 East, and allows them to better train and validate their crater detection algorithms. The collection of these data also demonstrates a multi-annotator method for the labelling of many small objects, which produces an estimated confidence score for each annotation and annotator.https://www.mdpi.com/2306-5729/5/3/70Marscratersremote sensingobject detectionplanetary science |
| spellingShingle | Alistair Francis Jonathan Brown Thomas Cameron Reuben Crawford Clarke Romilly Dodd Jennifer Hurdle Matthew Neave Jasmine Nowakowska Viran Patel Arianne Puttock Oliver Redmond Aaron Ruban Damien Ruban Meg Savage Wiggert Vermeer Alice Whelan Panagiotis Sidiropoulos Jan-Peter Muller A Multi-Annotator Survey of Sub-km Craters on Mars Data Mars craters remote sensing object detection planetary science |
| title | A Multi-Annotator Survey of Sub-km Craters on Mars |
| title_full | A Multi-Annotator Survey of Sub-km Craters on Mars |
| title_fullStr | A Multi-Annotator Survey of Sub-km Craters on Mars |
| title_full_unstemmed | A Multi-Annotator Survey of Sub-km Craters on Mars |
| title_short | A Multi-Annotator Survey of Sub-km Craters on Mars |
| title_sort | multi annotator survey of sub km craters on mars |
| topic | Mars craters remote sensing object detection planetary science |
| url | https://www.mdpi.com/2306-5729/5/3/70 |
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