Mars Crater Database: A participative project for the classification of the morphological characteristics of large Martian craters

The determination of ages of surfaces of rocky and icy bodies of the solar system is one outstanding challenge in planetary science. Applications of radiometric methods, typically used for terrestrial rock samples, to extraterrestrial samples, and the significance of these results for the global picture of planetary evolution are yet limited by several obstacles. Ages from the Apollo samples are—and will remain for the next decades—the major contribution to our understanding of the chronology of the Moon, Mars, and the asteroids of the main belt. Ages of the meteorites from Mars, or the Moon, cannot be assigned with certainty to a particular source on the surface. The recent breakthroughs enabling in situ isotopic analyses on rovers (e.g., first K-Ar ages on Mars; Farley et al., 2014) are extremely valuable, but the numbers of ages that can be obtained by these techniques are necessarily constrained by the limited mobility of the rover. The continuous exposure of a planetary surface to meteoritic bombardment offers a major tool to planetary scientists to map planetary surfaces and determine the chronology of various geological processes, including volcanism, tectonic deformation, erosion and transport, and impact cratering itself (Hartmann and Neukum, 2001). In addition, in the case of Mars, the morphology of impact craters themselves, and their distribution in time and space, may be used to constrain the past presence and spatial extent of subsurface volatiles, and in the end, the climatic evolution of the planet (Barlow and Perez, 2003; Barlow, 2004; Jones and Osinski, 2015; Warren et al., 2019).

Such investigations are facilitated by the availability of reliable catalogs of impact craters. Previous workers took advantage of new imagery to refine and update earlier crater databases down to smaller and smaller diameters (Costard, 1989; Barlow and Bradley, 1990; Barlow and Perez, 2003). As the number of impact structures increases exponentially with smaller sizes, the acquisition of data for objects smaller than 5 km in diameter becomes particularly tedious. Several attempts have been made to automatically identify craters using imagery or elevation data, but these techniques produce false detections, and the identification proves to be incomplete (~70%) and depend on the algorithm architecture and other parameters such as type of terrain, crater morphologies, data set used (digital elevation model [DEM], imagery, etc.), and training data set (Urbach and Stepinski, 2009; Salamunniccar et al., 2012; Lee, 2019). Recent improvement of the technique has nevertheless allowed ~90% true detection to be reached for kilometric craters on Mars (Benedix et al., 2020a), thanks in particular to the quality and the size of the crater catalog used to train the algorithm (~7000 craters among the 385,000 entries of the database compiled by Robbins and Hynek, 2012). However, this technique does not allow a classification of detected impact craters, with multiple entries relevant for future scientific studies, including primary or secondary origin, the morphology of the ejecta blanket, and the degradation state.

The visual identification by impact scientists of impact craters on planetary surfaces is a subjective task (Robbins et al., 2014) but remains the most widely accepted approach. The subjectivity of this technique leads to up to 35% of difference in terms of number of craters reported over the same region between different experts for subkilometric craters on the Moon. The most complete human-made Martian crater database contains 384,399 craters larger than 1 km in diameter (Robbins and Hynek, 2012). For craters ≥ 3 km in diameter, corresponding to 21% of the database (~85,000 entries), elevation, morphologic, and morphometric information is available. For a large proportion of smaller impact craters, only size, location, and primary/secondary categorization are provided, i.e., 79% of the catalog. According to the authors, this database would be amenable to computing surface crater retention ages if partially or totally buried and secondary craters were removed. Furthermore, this catalog was never formally assessed by different experts after the peer-review process and publication. This type of assessment could potentially lead to a wider acceptance in the community of Martian scientists (98 citations of Robbins and Hynek [2012, 2014] in January 2020). These gaps represent one of the motivations of this work.

This study therefore had the objective to provide complementary information and added value to the Robbins and Hynek’s catalog. For this purpose, a large group was formed, composed of planetary scientists and students carefully trained to conduct impact identification and classification. Each member of the group was tasked with the objectives to check the validity of a segment of the catalog and check for missing craters. In addition, the participants had to classify each impact crater according to its morphology by using the classification scheme discussed in the next section. A second important objective was complete the morphological information for craters smaller than 3 km in diameter. The advantages of gathering a large group are to reduce the tedious workload per individual and allow multiple checks, which increase the final robustness of the observations and classifications. The morphological classification was performed using the Thermal Emission Imaging System (THEMIS) mosaic, which provides complete coverage of the surface of Mars at a resolution of 100 m per pixel (Christensen et al., 2004). Mars Obiter Laser Altimeter (MOLA; Smith et al., 2001) data were also used to distinguish buried impact structures. The common guidelines and platform designed for this investigation are described in the “Procedure for Adding Information to the Database” section, along with the necessary correction procedures following the integration of observations from multiple researchers. The analysis of this catalog is discussed in “Results and Discussion” section. Precautions and directions for possible investigations using the Mars Crater Database and other crater catalogs are discussed in concluding section.

Each crater in the database was classified into one of the four classes described below. Here, we bring the reader’s attention to the fact that this classification and its subsequent results do not replace the classification made by Robbins and Hynek (2012); instead, we had the aim to provide future users with an alternative point of view regarding the morphological information contained in the database and complete it for diameter (D) ≤3 km.

(1) Layered ejecta rampart sinuous (LERS) and low-aspect-ratio layered ejecta (LARLE) craters: Primary impact craters for which the ejecta blankets are continuous and exhibit sinuous termini (Barlow et al., 2000; Costard, 1989) were classified as LERS craters. Low-aspect-ratio layered ejecta (LARLE) craters (Barlow et al., 2014; Boyce et al., 2015) were also classified in this category. Some examples are given on Figure 1A. The LERS craters were also subclassified following the following standard classification (Barlow et al., 2000), including single (SLERS), double (DLERS), and multiple ejecta layers (MLERS). Radial (Rd) and circular ejecta morphologies, such as pancake craters (Barlow et al., 2000), were excluded from this category and were classified in the “standard” crater category (see point 4).

(2) Secondary craters: Despite the fact that Robbins and Hynek (2014) already identified ~70,000 secondary craters in their database, a preliminary analysis revealed that some entries were likely inconsistent with a secondary origin (see Supplemental Material Appendix A¹). We classified impact craters as secondary craters following the same criteria used by Robbins and Hynek (2014) and inspired by several studies (Shoemaker, 1962, 1965; Oberbeck and Morrison, 1974; McEwen and Bierhaus, 2006; Robbins and Hynek, 2011a, 2011b; Quantin et al., 2016): “[i] Is the crater entrained within a chain, elongate crater group, and/or has a ‘herringbone’ ejecta pattern? [ii] Is the crater highly elliptical with the long axis radial to a much larger crater? [iii] Does the crater appear to have been ‘scooped’ out at an angle with the shallower part radially away from a larger crater?” (Robbins and Hynek, 2014, p. 67). If a crater fit one of these criteria, it was marked as a secondary crater. Some examples classified in this category are found in Figure 1B. A regional analysis was essential to identify possible relationships with a primary crater, and this was based on clustering and alignments. If the orientations of asymmetric craters in the surveyed population had ejecta pointing away from a potential source primary crater, the CRATER-ID of the plausible primary crater is mentioned in the ORIGIN column of each secondary impact crater entry in the database (see Supplemental Material Appendix B). This association was added if the primary impact crater source could be identified according to the criteria developed by Robbins and Hynek (2011a, 2011b), and if the associated secondary population (secondary impact craters having the same origin) exceeded 50 impact craters (arbitrarily fixed).

(3) Buried/degraded craters: Impact craters exhibiting a high degree of degradation, such as those for which the elevation of their floor was approximately equal to that of the surrounding terrain or those with a flat floor mainly filled up by sedimentary or volcanic material (Barata et al., 2012), implying they are actually partially buried, were classified in this category. Such impact craters must indeed be generally excluded from crater counts because their formation occurred before the dated surface (Craddock et al., 1997; Barlow, 1995). Examples of buried/degraded craters are shown on Figure 1C. Some of them can nevertheless be used for specific purposes (such as dating basement units, or old surfaces covered by volcanic or sedimentary material). This point will be developed and discussed later herein.

(4) All other impact craters: Craters that could not be attributed to one of the three previous categories were classified in a fourth category named “standard” in this study (Fig. 1D). This category may include impact craters superposed onto the surrounding ground, background secondary craters, and craters without ejecta, or with a blanket that was not continuous enough or sinuous enough to be classified as LERS or LARLE craters (such as radial ejecta, pancake or pedestal craters; Barlow et al., 2000; Kadish and Head, 2014).

Visual examination of ~385,000 impact craters is a particularly long task, and it would be difficult to perform by only one person (taking ~133 [at 8 h per day] working days for a single person, assuming 10 s spent on each crater, including the time required to move from one crater to another). A platform allowing collaborative research between planetary scientists was therefore developed at the Geosciences Paris Sud (GEOPS) laboratory. This platform and the distribution of the workload between the different participants made the workload for each participant reasonable and the examination of craters an enjoyable experience. The regional context of some of the craters can potentially provide a clue with which to correctly identify and classify a particular crater. It was therefore essential to not use individual images covering the immediate surroundings of each crater, but instead use a data set allowing the participant to navigate at distance around each crater at different resolutions. The most appropriate controlled imagery mosaic, at the time of writing, covering the entire surface of Mars at a resolution that allowed identification of impact craters larger than 1 km in diameter was the Thermal Emission Imaging System (THEMIS) data mosaic. Therefore, the visual assessment was performed using these images. We hypothesized that some craters may not have been included (missing entries) in the database of Robbins and Hynek (2012). This data set enabled the exploration of the entire surface of Mars in order to identify and to add potential new entries to our database, if necessary. The use of the THEMIS daytime infrared (IR) mosaic nevertheless entailed the visualization of a very small portion of the surface of Mars with a low resolution, since this mosaic was created with both low-quality and high-quality images. This may have led to a misinterpretation of crater morphology, but these misinterpretations should be limited to a low number of cases (a few tens of cases to a few hundred cases) compared to the number of craters contained in the original database (Barlow, 2017).

A Web interface was developed using the open-source JavaScript library Cesium (https://cesiumjs.org/). Cesium is a geographic information system (GIS) library optimized for three-dimensional (3-D) mapping. Cesium was specifically designed for Earth-based applications. We therefore implemented planetary geographic standards for its use. In brief, our interface has some interesting advantages:

1. an effective and easy-to-use data navigation system,

2. a web client that does not require complex setup for local installation of dedicated software,

3. effective simultaneous access by distant users on different operating systems and Web browsers,

4. a precise georeferenced framework for visualization,

5. easy integration of global imagery data of Mars, distributed by the U.S. Geological Survey (USGS), such as MOLA or THEMIS data sets, and

6. the possibility to adapt the interface for assessment of crater catalogs.

This last point is developed in the next section.

The catalog published by Robbins and Hynek (2012) was first divided into 140 quadrangles corresponding to the Mars 2M quad cartographic regions defined by the USGS (Fig. 2). The data divided in this way were stored in 140 geoJSON files. GeoJSON is an open standard format designed for representing simple geographic features and their attributes; it is based on JSON, the JavaScript Object Notation, and hence it is particularly adapted for Web applications (http://geojson.org/). Each of these files was distributed to 56 collaborators (see Supplemental Material Appendices C and D). The selected participants included planetary scientists, astronomers, or geologists that has been previously trained on the recognition of the diversity of impact crater morphologies and the utilization of the Web interface. A tutorial detailing the usage of the interface and containing many examples of craters in each category was distributed to each participant. We also distributed a testing quadrangle located at the north part of Chryse Planitia, where all craters have been classified, allowing each user to visualize the complete diversity of crater morphologies investigated in the classification.

Figure 2 gives an overview of the appearance of the interface on the screen of each user. Label 1 on Figure 2 corresponds to the geographic limits of one GeoJSON file corresponding to a Mars 2M quadrangle. The interface provides 3-D and 2-D (simple cylindrical projection) visualization modes (see label 2 on Fig. 2). Each file may be examined and revised independently of the others, and the entire THEMIS imagery set remains accessible when a given GeoJSON file is uploaded on the interface in order to avoid edge effects. Once a part of the catalog is loaded in the interface, the user has the possibility to visualize the surface through different data sets and set up the transparency level of each one (see label 3 on Fig. 2). For the purpose of this study, all participants chose the THEMIS DAY IR mosaic overlaid to the MOLA data set, at 50% of transparency level, throughout the entire revision. The catalog was loaded into the interface by a simple drag and drop of the GeoJSON files from the local computer disk. Each entry of a GeoJSON file corresponds to a crater with its location, its size, and its identification number, information given by Robbins and Hynek (2012). When new craters (not present in the original database) are identified from THEMIS imagery or MOLA data set, the user has the option to add a crater and define its size and position based on two or three points placed manually on the rim using an in-house developed widget. The crater diameter is determined from the circle that is automatically fitted to these points.

Crater classification was performed via a specific flag widget (a simple click on one of the entries of the list of crater classes, corresponding to label 4 on Fig. 2). The class “uncertain” allows users to report that they are unable to apply to a certain crater one of the classes proposed in this study. Furthermore, the class “misidentified entry” gives the users the possibility of flagging an unrecognizable impact structure for the place and diameter described in the database of Robbins and Hynek. The crater then appears with a specific color-code (a different color is assigned to each crater class). The legend panel (label 4 on Fig. 2) can be also saved (with the specific extension “legendjson”) for future use. If such a file is already available, it can be loaded by a simple drag and drop in the legend panel. The result of the classification can be saved on the local computer and reloaded later, allowing the processing of a GeoJSON file in several sessions. A flag counter displays the number of entities already classified (see label 5 on Fig. 2); this is useful to resume ongoing revision of a part of the catalog by the same user, or for the examination and double-checking of the work of a given participant. With our interface, it is possible to classify impact craters at a rate of 200–500 craters per hour (7–18 s for each crater), depending on crater density and morphological complexity of each crater. Examination of the entire catalog of Martian craters would thus represent a total workload of ~800–2000 h, which corresponds to a reasonable average workload of ~14–36 h per participant.

An additional global check was performed by A. Lagain once the work was achieved. This process had the objective to (1) correct the possible mistakes of less experienced participants and provide feedback to improve the processing of the next file assigned, and (2) assign “uncertain” craters into one of the other classes, where appropriate. If needed, this step made use of the higher-resolution CTX data (6 m/pixel), when available. At this step, each layered ejecta crater was also classified into one of the four subclasses of this class: SLE, DLE, MLE, and LARLE craters. Details about the revision team and the contribution of each participant are given in the Supplemental Material Appendices C and D.

The catalog obtained following the assessment of the database compiled by Robbins and Hynek (2012) by the group using the Cesium platform contains 384,582 entries, of which 74.9% (288,155) are classified as “standard,” 2.2% (8445) are LERS craters, 6.4% (24,530) are buried/degraded craters, and 14.4% (55,309) are secondary craters. Finally, 8145 misidentified entries (2.1%) were identified from Robbins and Hynek’s database. Figures 3 and 4 present, respectively, the incremental crater size-frequency distribution of the four categories as well as misidentified entries and their geographic distribution, which are described in detail in the following sections, while Figures 5 and 6 detail and compare the size-frequency and geographic distributions of LERS and LARLE impact craters with other databases compiled in previous studies.

The distribution of “standard impact craters” is closely related to the surface age (Fig. 4A). A high cratering density of this type is observed on highlands areas, whereas a low density is observed on northern lowlands and on the Tharsis bulge. From 45° latitudes, a decreasing cratering density is observed with increasing latitude. This observation is interpreted to be the result of the presence of polar caps and enhanced efficiency of resurfacing processes.

Among 185 added entries, half of them are less than 2 km in diameter. It is possible that these small craters were identified by Robbins and Hynek (2012), but that their diameter was underestimated (<1 km in diameter), and the crater was removed from the published database. The low number of new entries identified in our survey is not astonishing since the influence of erosion processes, and more generally all surface processes, on large crater morphologies is limited compared to subkilometric craters, leading thus to a lower degree of subjectivity in the identification of kilometric craters than those smaller craters (Riedel et al., 2020). This result also confirms the completeness of the Robbins and Hynek (2012) database for craters ≥1 km. Despite the direction for participants to pinpoint craters absent from the database during the review process, finding new craters close to the threshold diameter, i.e., 1 km, was challenging. Representing only 10 pixels in diameter on THEMIS imagery, the resolution of the data set and the precision of the manual estimation of their diameter did not allow us to add with confidence an impact crater for which the size was close to this threshold. However, the original catalog was also based on the identification of smaller craters (>500 m, >600,000 in total), which allowed the completeness of the final catalog for D (diameter) > 1 km, thus explaining the low number of new entries found by our team.

Figure 5 shows the global distribution of SLERS, DLERS, and MLERS craters. As other researchers have previously noted (Barlow and Perez, 2003; Costard, 1989; Costard and Kargel, 1995), SLERS craters dominate the population of layered ejecta craters and are present in all Martian regions, mainly north of Alba Patera, at Lunae Planum and Tempe Terra, in Arabia Terra, and in Syrtis Major Planum.

DLERS morphologies are preferentially concentrated at midlatitudes and high latitudes, between 30° and 70°, in both hemispheres, and they are particularly concentrated on the northern plains. MLERS craters are primarily concentrated in cratered highlands, especially in the area covering the northern part of Hellas Planitia, Terra Simeria, Sinai Planum, and Terra Sirenum. The distribution of these three categories is similar to the one found by Barlow and Perez (2003). Our study confirms this distribution, with now the inclusion of 4751 additional SLERS, DLERS, or MLERS craters, particularly for small diameters (≤3 km in diameter) compared to the database of Robbins and Hynek. This inclusion is of particular interest because it offers the possibility to extend our comprehension of the processes responsible for such morphologies in smaller craters as well as their temporal and spatial evolution. More particularly, the dating of a large portion of the impact craters classified in all of these categories would allow the tracking of the presence and depth of the volatile-rich layer in the subsurface at the moment of the impact, which is suspected to have played a major role in their formation (Costard, 1989; Barlow et al., 2000), and this will complete the study performed by Jones and Osinski. (2015) in the mapping of this layer. Such investigation is currently ongoing (Benedix et al., 2020b) and made possible by the semi-automatic dating technique developed by Benedix et al. (2020a) and Lagain et al. (2021).

In a general point of view, LERS craters are widely represented around 10 km of diameter (see Figs. 3 and 6). Their size distribution decreases gradually from this size. The resolution of THEMIS data could nonetheless lead to an underestimation of the smallest LERS craters, for D < 2 km. Some craters were classified here differently from the Robbins and Hynek’s database due to a different morphological interpretation; several examples of different classifications are given in Supplemental Material Appendix E. The size-frequency distribution (SFD) for DLERS craters obtained from the present survey (Fig. 6) is very close to that observed by Robbins and Hynek (2012), while that of MLERS craters follows the same trend as Robbins and Hynek’s database for D > 20 km and that of Barlow et al. (2000) for D < 10 km. Our database presents a continuum linking both distributions between 10 and 20 km concerning the MLERS morphology. Our classification of the SLERS category presents the largest SFD difference compared to other databases (Fig. 6). The SFD of the SLERS category is similar to the database of Robbins and Hynek between 3 and 10 km in diameter and closer to Barlow et al.’s database for D > 10 km. Finally, SLERS craters smaller than 3 km follow the same trend as the craters with D = 3–8 km. The inclusion of 32 LARLE impact craters compared to the previous survey performed by Barlow et al. (2014) confirms the general size-frequency and geographic distribution observed in the past (Fig. 6). The SFD shows a peak around 3–4 km. The lower number of craters for a particular bin size in our database compared that of Barlow et al.’s database is due to a different diameter measurement used in both databases.

The population of buried/degraded impact craters represents 24,530 entries (6.4%) of the database. This type of impact crater is obviously primarily concentrated on highlands (Fig. 4B), where the surface is more than 3.5 b.y. old. This is consistent with the fact that these craters are also generally old. Unlike other types of craters, an increasing fraction of buried/degraded craters is observed at high latitudes. This observation is consistent with the increased efficiency of resurfacing processes in these areas. Their SFD exhibits an approximately flat distribution between 1 and 10 km (Fig. 3). For impact craters larger than 20 km, those belonging to the “buried/degraded” category are as common as those belonging to the “standard” category. Above 50 km diameter, buried craters dominate the whole population. This information could be used in future studies to examine degradation rates versus cratering rates on Mars. Even if this type of impact crater is generally removed from crater counts, they can be taken into account for dating underlying/buried geological units (Greeley and Fagents, 2001; Frey et al., 1979). The distinction between buried and not superposed impact craters in this category is impossible to make in a catalog because it depends on what part of the structure one is studying. We therefore invite users of our database to examine if they need to include or exclude these craters from the database depending on the focus of their investigation. This can be easily done from the interface we developed in this study. A possible outcome of the classification performed here is the improvement of the local erosion rate estimation on Mars through a careful analysis of the local spatial distribution of these craters and their relationships with datable surrounding terrains. This can be performed using automatic extraction of the elevation of each crater floor and rim and that of the surrounding ground using methods developed by Breton et al. (2019).

Among the 384,399 entries contained in the original database, 55,309 impact craters larger than 1 km in diameter were identified as secondary craters. This is 16,700 fewer than the survey performed by Robbins and Hynek (2014). Some differences in secondary crater identification between both databases are presented in Supplemental Material Appendix A. The main differences are found for isolated craters, for which the morphologic characteristics were insufficient to classify them in this category. For secondary crater clusters, we estimated their potential link to a large primary crater by visually analyzing the direction of their asymmetric shape, thus indicating the direction of their ejection source. For the first time, 38,760 of these secondary craters (70%) were connected to 108 primary impact craters. The entire population of craters identified as secondaries in our survey is distributed on the cratered highlands and around Lyot, Lomonosov, and Mie craters north of the hemispheric dichotomy (Figs. 4C and 7A). The area in the north of Isidis Planitia concentrates a large number of secondaries associated with the Lyot crater, located northwest of this area, as already shown by Robbins and Hynek (2011a). Their size distribution shown, as on Figure 3, exhibits a steeper slope than for “standard” craters, confirming the observations of Robbins and Hynek (2014). The established association between secondary craters and their primary crater can potentially complete conclusions of Robbins and Hynek (2011b) and those of Watters et al. (2017), where the authors investigated the relationship between the secondary morphometry and the characteristics of the primary impact crater. Figure 8 presents the secondary SFD based on the impacted terrain type according to the Geological Map of Mars (Tanaka et al., 2014) and based on the size of the associated primary crater. If any correlation can be established between the terrain type chosen here and the size-distribution of secondary craters, the size of the primary crater is naturally linked to the production of secondaries, thus extending results from Robbins et al. (2014) to a wider crater population. Large primary craters produce large secondary craters, with an SFD flatter than that produced by a smaller impact. The lack of small secondaries produced by large craters is due to the older age of large primary craters and therefore the erosion of their associated small secondaries. Further investigations on the physical properties of the impacted material would potentially allow us to better constrain our knowledge of the factors controlling the secondary crater production, and reciprocally infer the structural properties of the ground at the moment of the impact, but also the local erosion rate evolution (Breton et al., 2018). One of the most unexpected secondary crater SFDs observed in this survey is that of Lomonosov crater, where its associated small secondary crater population (1–3 km) is underrepresented (Figs. 7B and 8B) compared to other craters of the same size and age range (~100 km, >1 Ga) located on the northern lowlands such as Mie or Milankovic. The hypothesis according to which the impact that formed this crater occurred in a shallow ocean (Costard et al., 2019) and generated the tsunami deposits observed in northwestern Arabia Terra (Rodriguez et al., 2016; Costard et al., 2017) could potentially explain the observed SFD of secondaries. Indeed, the debris ejection following the impact into a shallow aqueous layer could exhibit a limit in terms of quantity of small fragments forming secondary craters around the primary impact structure compared to an impact into a dry and strong geological layer due to water resurge (Ormö et al., 2010; Watters et al., 2017). The relationships established in the present study open new perspectives for understanding the emplacement dynamics of this type of crater and the characteristics of the impacted material.

In the database of Robbins and Hynek, 8145 entries were flagged in our survey as not being impact craters. Some examples are given in Supplemental Material Appendix F. As shown on Figure 4D, misidentified entries are essentially concentrated on rough terrains, near the dichotomy, and on volcano-tectonic provinces where pseudocircular geological shapes are frequent. As already pointed in Robbins et al. (2014), identification of impact craters in heavily modified terrains is subject to a high variability rate in the distinction between impact craters and other geological structures. In this, the observed spatial distribution is not astonishing. The database of Robbins and Hynek includes a confidence index describing a subjective certainty that a crater is an impact crater. However, only 1294 entries have an index suggesting that they may not be impact craters. Some of them, recognized as misidentified entries in our database, do correspond to geological structures that have a shape evoking an impact crater, but that likely correspond to skylights on a tube-fed lava flow or collapsing or thermokarstic structures. Some entries do not even correspond to any visible structures at the surface, whether with THEMIS or MOLA data (some examples are shown in Supplemental Material Appendix F). These misidentified entries could have arisen from geographic coordinate errors. An error in geographic coordinates may lead in this work to the removal of one entry that is compensated by a new entry elsewhere, but this guess is impossible to verify. Nevertheless, no correspondence could be established, for instance, between added entries in our database or impact craters located at opposite coordinates (longitudes and/or latitudes, due to a sign error, for instance) and those originally mentioned in the database.

Figure 3 shows an SFD trend for this category that is quite different from the most common category of craters, i.e., standard craters. The mean slope of the distribution of misidentified entries is lower than the mean slope for other craters. This observation consolidates our interpretation about the source of misidentified entries in the original database. Entries in the original database that are not impact craters should produce an SFD that is notably different from impact craters, and this is what we observe in this study. The confusion between volcanic or tectonic features and impact craters and entries that do not correspond to any circular structure can give rise to a size distribution essentially controlled by the size of pseudocircular shapes. A blind inclusion of these entries to date some parts of the Martian surface can lead to an overestimation of its age, especially for volcanic and tectonic regions. Nevertheless, the fraction of misidentified entries is only 2% of the entire database. This very low fraction, compared to the identification variability determined by Robbins et al. (2014) (up to 30% for subkilometric craters on heavily modified terrains), can be explained in the same way as the low number of new entries (see section on “Standard Impact Craters and Added Entries”): The identification of large craters is easier than identification of smaller ones due to the underrepresentation of pseudocircular structures other than impact craters at this size scale. We expect other misidentified entries that could be spotted by the users of the database. We therefore strongly encourage the users of the database to create a pull request on the Github repository containing the crater database with their own modification of the catalog.

The identification of misidentified entries in the previous version of the largest Martian crater database and the morphological classification of buried/degraded and secondary craters provide to planetary geologists a complementary tool with which to quickly date geological units on the Martian surface. The identification of the primary crater source of secondary craters opens perspectives for new studies on the spatial distribution and characteristics of secondary craters and also the study of the rheology of the impacted material. Layered ejecta crater identification smaller than 3 km diameter will complete our understanding of the factors affecting the distribution of this type of ejecta and could help us to understand their formation processes. This database could also be used to improve automatic detection of crater algorithms to complete this catalog with smaller craters, with the aim to improve the temporal and spatial resolution of studies involving age derivation of Martian surfaces (Benedix et al., 2020a; Lagain et al., 2020b).

A perfect database of Martian craters will probably never exist, unless each structure could be verified in the field, as done for terrestrial impact structures (not even mentioning that some terrestrial structures are currently debatable and debated). Therefore, different views and opinions are needed to progress and warn the new users that none of the available Martian crater database can be considered to be 100% correct.

The interface developed in this study offers the possibility to the community to complete and revise our classification as well as other crater databases developed in the past, on Mars or on other planetary bodies such as the Moon, as different global higher-resolution data sets are produced, such as the recent CTX global mosaic (Dickson et al., 2018). Consequently, we strongly encourage any completion or revision of our classification by using the interface and the database we present in this study. Any improvement can be implemented to the existing database on a simple request through the Github open repository created for this project (https://github.com/alagain/martian_crater_database). Widely used computer tools for crater counting and dating planetary surfaces include CraterTools (Kneissl et al., 2011) and CraterStats II (Michael and Neukum, 2010). We therefore distributed our database by respecting the architecture of inputs readable by these software packages in order to facilitate their integration in future studies. To this purpose, secondary, degraded craters and misidentified entries are flagged as “marked,” and craters classified in the “standard” category and LERS craters are flagged as “standard,” in agreement with the architecture of these tools.

The catalog is also available as an interoperable data service compliant with the Virtual Observatory (VO) environment. More specifically, all parameters are described using standard metadata from the Europlanet Core (EPNCore) Data Model, and the service is responsive to the EPN-TAP access protocol (Erard et al., 2018, 2020). It can therefore be queried by several VO tools, in particular, by the VESPA portal (http://vespa.obspm.fr), which queries all such services together and can pass selections of data to VO tools with graphical capabilities, such as TOPCAT or Aladin, which in turn make it very easy to compare answers from related data services. Obvious applications include comparisons between several crater catalogs, but also more complex applications such as integration of craters of a given size range inside spatial units (with direct applications to unit dating) or, conversely, extraction of features located inside craters (e.g., gullies, flows, etc.).

Finally, a complete documentation of the interface is available at https://github.com/epn-vespa/cesium/wiki, and a demo instance is available at http://134.158.75.177/viewer/Apps/PlanetaryCesiumViewer/index.html. This offers a flexible way to work on other data sets and create a multiclassification of other planetary features usable within the research communities and adapted to citizen research projects (Jones, 2019; Keshavan et al., 2019), and it can even lead to a training data set for feature recognition using deep learning techniques (Benedix et al., 2020a).

We declare no conflict of interest. We thank Joan Florsheim for the editorial handling of this manuscript, as well as C. Koerbel for helpful comments that improved this manuscript. We also appreciate the valued reviews from G. Michael, which benefited this presentation. We are also grateful to C. Fassett, N. Barlow, S. Robbins, and two anonymous reviewers for reviews of an earlier version of this manuscript. This research was funded by the GEOPS laboratory and the Domaine d’Intérêt Majeur pour l’Astrophysique et les Conditions d’Apparition de la Vie (DIM ACAV), the Australian Research Council (FT170100024), and Curtin University (Perth, Western Australia, Australia). The Europlanet-2020 and Europlanet-2024 Research Infrastructure projects have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements 654208 and 871149. This work made use of the U.S. Geological Survey Web Map Service, http://planetarymaps.usgs.gov. The catalog used for analyses in this paper is available at https://doi.org/10.5281/zenodo.3633594. The EPN-TAP data service is installed and maintained at Jacobs University, Bremen, Germany. The interface used for the crater classification, for which the link is mentioned in the main text, is usable on the latest version of Google Chrome or Mozilla Firefox.