Next Article in Journal
Displacements of an Active Moderately Rapid Landslide—A Dataset Retrieved by Continuous GNSS Arrays
Next Article in Special Issue
Forty Years of the Applications of Stark Broadening Data Determined with the Modified Semiempirical Method
Previous Article in Journal
Towing Test Data Set of the Kyushu University Kite System
Data Descriptor

A Multi-Annotator Survey of Sub-km Craters on Mars

Mullard Space Science Laboratory, UCL, Holmbury Hill Rd, Dorking RH5 6NP, UK
The College of Richard Collyer, 82 Hurst Rd, Horsham RH12 2EJ, UK
Hummingbird Technologies Ltd., 51 Hoxton Square, Hackney, London N1 6PB, UK
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 30 June 2020 / Revised: 28 July 2020 / Accepted: 1 August 2020 / Published: 3 August 2020
(This article belongs to the Special Issue Astronomy in the Big Data Era: Perspectives)
We present here a dataset of nearly 5000 small craters across roughly 1700 km2 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. View Full-Text
Keywords: Mars; craters; remote sensing; object detection; planetary science Mars; craters; remote sensing; object detection; planetary science
Show Figures

Figure 1

MDPI and ACS Style

Francis, A.; Brown, J.; Cameron, T.; Crawford Clarke, R.; Dodd, R.; Hurdle, J.; Neave, M.; Nowakowska, J.; Patel, V.; Puttock, A.; Redmond, O.; Ruban, A.; Ruban, D.; Savage, M.; Vermeer, W.; Whelan, A.; Sidiropoulos, P.; Muller, J.-P. A Multi-Annotator Survey of Sub-km Craters on Mars. Data 2020, 5, 70.

AMA Style

Francis A, Brown J, Cameron T, Crawford Clarke R, Dodd R, Hurdle J, Neave M, Nowakowska J, Patel V, Puttock A, Redmond O, Ruban A, Ruban D, Savage M, Vermeer W, Whelan A, Sidiropoulos P, Muller J-P. A Multi-Annotator Survey of Sub-km Craters on Mars. Data. 2020; 5(3):70.

Chicago/Turabian Style

Francis, Alistair, 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, and Jan-Peter Muller. 2020. "A Multi-Annotator Survey of Sub-km Craters on Mars" Data 5, no. 3: 70.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop