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34 results for βimpact cratersβ
Dataset for paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis"
<p>Dataset for the paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis" published in Icarus.</p>
Tycho Region Database of impact craters >=21 meters on the Moon.
<p>This dataset presents the results of an innovative AI-driven lunar crater mapping project, focused exclusively on the Tycho region of the Moon at 21m/px (<strong>see Version V2 for the global catalog at 100m/px</strong>) , marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:</p> <ol> <li>La Grassa, Riccardo, et al. <strong>"YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces."</strong> Remote Sensing 15.5 (2023): 1171, https://doi.org/10.3390/rs15051171.</li> <li>La Grassa, R, et al. <strong>"LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon"</strong>, ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.<strong><br></strong></li> </ol> <p><br>The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.</p> <p>The area analyzed in this dataset is defined by the following coordinates: longitude [-21.0000000001998046, 44.9997999998701630] and latitude [-50.9998000002697722, 39.0000000003997229].</p> <p>This dataset, which includes over 6.8 million craters at a resolution of 21m/px, is a valuable resource for both astrophysicists and AI researchers. It provides precisely labeled crater data, including coordinates, dimensions, and classifications, serving as an essential benchmark for comparative analyses, model validation, and advancements in lunar and planetary science.</p> <p>Notably, the global catalog created using the WAC imagery at 100m/px resolution is available in <strong>version V2</strong> of this repository. This global dataset complements the Tycho region-specific data by offering a broader perspective on lunar crater distribution.</p> <p>*********************************************************************πππππππππ **********************************************************************</p> <p>Release of a Tycho area at 21m/px of craters catalog with more than 6.8 million craters.</p> <ul> <li><em>File source csv</em></li> <li><em>Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.</em></li> <li><em>The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. <br></em></li> <li><em>Diameters (km)</em></li> </ul> <p> </p>
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
Depths of Pluto's and Charon's Impact Craters
<p>Tab-Separated Values (TSV) files of crater depth data for Pluto and Charon, as published in supplemental material for the publication, "Depths of Pluto’s and Charon’s craters, and their simple-to-complex transition," by S.J. Robbins et al. (2020), doi: 10.1016/j.icarus.2020.113902). The first row in both files describes what is contained in that column. Calculations were carried out to machine's precision and are not reliable past about 2–3 significant figures.</p>
Updated database of craters on Mars with pitted impact deposits
<p>This point-based database (provided in two formats: as a ESRI shapefile set and simple .csv) currently contains 309 craters on Mars that possess “crater-related pitted materials” (CRPM), which are consistent with impact deposits described in detail in Tornabene et al. (2007; 2012). The Tornabene et al. (2012) publication is the original source of the initial database of 204 craters, which was based on a survey by the Mars Reconnaissance Orbiter (MRO) over a period of late 2006 to early 2012. MRO continues to image the surface and its craters, as such the database has since grown from 204 to 309 entries to date. Despite this growth, the general characteristics of the crater population remains generally consistent with what is described in Tornabene et al. (2012) (e.g., size range, latitudinal and elevation distribution, etc.).</p> <p>When present, these pitted impact deposits represent the upper most surface of the crater-fill with the pits potentially representing top-down views of so-called degassing pipes observed only in eroded cross-sections at some terrestrial impact structures such as the Ries in Germany (e.g., Caudill et al., 2021). Therefore, the craters that contain pits and preserve them well are themselves amongst the very best-preserved and often youngest craters of their size-class on Mars. Indeed, some of these craters are observed to have far-reaching (10s to 100s of crater radii) thermal / secondary crater rays (e.g., Tornabene et al. 2006), which is considered to be a feature associated with only the best-preserved and youthful craters on planets/moons with solid surfaces.</p> <p>These craters have enabled us to place further constraints on the scaling of crater depth as a function of diameter for complex craters on Mars (Tornabene et al. 2018) and may even help us to ultimately determine where the only samples we have of Mars — the Martain Meteorites — come from.</p> <p>See README rtf file for further details on the database.</p> <p> </p> <p><strong>Versions</strong></p> <p><strong>8.21.2025: </strong>4th version - deleted 3 additional duplicates (Lunae, Oudemans and Toro) total entries is now 309<strong><br></strong></p> <p><strong>8.20.2025b</strong>: 3rd version upload - fixed 1 duplicate (312 entries), caught some additional updates with respect to new official crater names, and CTX image IDs.</p> <p><strong>8.20.2025</strong>: 2nd version with an increase to 313 entries with some updates to preservation ratings, image IDs, etc.</p> <p><strong>5.3.2023</strong>: 1st version uploaded with 300 entries</p> <p> </p> <p><strong>Main references (*original/source database):</strong></p> <p>*Tornabene, L.L., Osinski, G.R., McEwen, A.S., Boyce, J.M., Bray, V.J., Caudill, C.M., Grant, J.A., Hamilton, C.W., Mattson, S. and Mouginis-Mark, P.J., 2012. Widespread crater-related pitted materials on Mars: Further evidence for the role of target volatiles during the impact process. Icarus, 220(2), pp.348-368. https://doi.org/10.1016/j.icarus.2012.05.022</p> <p>Tornabene, L.L., McEwen, A.S., Osinski, G.R., Mouginis-Mark, P.J., Boyce, J.M., Williams, R.M.E., Wray, J.J. and Grant, J.A., 2007. Impact melting and the role of subsurface volatiles: Implications for the formation of valley networks and phyllosilicate-rich lithologies on early Mars. In International Conf. on Mars VII. Lunar Planet. Sci. Inst. Contri (Vol. 1353), Abstract# 3288.</p> <p><strong>Other references:</strong></p> <p>Tornabene, L.L., Moersch, J.E., McSween Jr, H.Y., McEwen, A.S., Piatek, J.L., Milam, K.A. and Christensen, P.R., 2006. Identification of large (2–10 km) rayed craters on Mars in THEMIS thermal infrared images: Implications for possible Martian meteorite source regions. Journal of Geophysical Research: Planets, 111(E10).</p> <p>Boyce, J.M., Wilson, L., Mouginis-Mark, P.J., Hamilton, C.W. and Tornabene, L.L., 2012. Origin of small pits in martian impact craters. Icarus, 221(1), pp.262-275.</p> <p>Denevi, B.W., Blewett, D.T., Buczkowski, D.L., Capaccioni, F., Capria, M.T., De Sanctis, M.C., Garry, W.B., Gaskell, R.W., Le Corre, L., Li, J.Y. and Marchi, S., 2012. Pitted terrain on Vesta and implications for the presence of volatiles. Science, 338(6104), pp.246-249.</p> <p>Sizemore, H.G., Platz, T., Schorghofer, N., Prettyman, T.H., De Sanctis, M.C., Crown, D.A., Schmedemann, N., Neesemann, A., Kneissl, T., Marchi, S. and Schenk, P.M., 2017. Pitted terrains on (1) Ceres and implications for shallow subsurface volatile distribution. Geophysical Research Letters, 44(13), pp.6570-6578.</p> <p>Tornabene, L.L., Watters, W.A., Osinski, G.R., Boyce, J.M., Harrison, T.N., Ling, V. and McEwen, A.S., 2018. A depth versus diameter scaling relationship for the best-preserved melt-bearing complex craters on Mars. Icarus, 299, pp.68-83.</p> <p>Caudill, C., Osinski, G.R., Greenberger, R.N., Tornabene, L.L., Longstaffe, F.J., Flemming, R.L. and Ehlmann, B.L., 2021. Origin of the degassing pipes at the Ries impact structure and implications for impactβinduced alteration on Mars and other planetary bodies. Meteoritics & Planetary Science, 56(2), pp.404-422.</p> <p>Michalik, T., Matz, K.D., Schröder, S.E., Jaumann, R., Stephan, K., Krohn, K., Preusker, F., Raymond, C.A., Russell, C.T. and Otto, K.A., 2021. The unique spectral and geomorphological characteristics of pitted impact deposits associated with Marcia crater on Vesta. Icarus, 369, p.114633.</p>
Supporting Material for "Automatic Mapping of Small Lunar Impact Craters Using LROC NAC Images"
<p>The supporting material for <em>'Automatic Mapping of Small Lunar Impact Craters Using LROC NAC'.</em></p> <p>This File contains:</p> <ul> <li>Supporting Material (.pdf);</li> <li>List of True Positive detections (.csv);</li> <li>List of all ground truth and CDA detections (.csv);</li> <li>Folder (.zip) with images of the evaluation sites (.pdf); and</li> <li>Folder (.zip) with training image tiles (.png and .txt).</li> </ul> <p>Refer to Supporting Material (.pdf) for file name and header information.</p>
Data accompanying: Impacts into a porous graphite: an investigation on crater formation and ejecta distribution
<p>Reconstructed X-ray tomographies and python analysis scripts for the publication "Impacts into a porous graphite: an investigation on crater formation and ejecta distribution"</p> <p> </p> <p>Uses the spam python toolkit, which can probably be replaced by scipy.ndimage.center_of_mass if needed.</p>
Pluto and Charon Impact Crater Databases, Version 2
<p>Comma-Separated Values (CSV) files of basic impact crater data of Pluto and Charon, including latitude and longitude, diameter, and subjective confidence the features are impact craters. In the "Region Guide" files, one can find the region of each body on which the crater data reside, corresponding to a "Region" column in the database files. The Region Guide files also contain other information about the region, including surface area and the estimated completeness diameters of craters in that region. Additional metadata TXT files briefly describe the image data used to map the craters and very briefly describe the crater datasets.</p>
A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning: Supplemental data
<p>This dataset includes the crater map and equatorial crater depths and diameters presented in the paper: A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning.</p>
Impact Craters on Mimas, Rhea, and Iapetus (incomplete surface coverage)
<p>Comma-separated values (CSV) files of impact crater data from Iapetus, Rhea, and Mimas, based on identification of features on individual images tied to Schenk (circa 2012β2014) basemaps. Data include latitude, longitude, and diameter in units of decimal degrees (location) and kilometers (size). These data cover approximately 27%, 11%, and 73% of the surface area of each body, respectively.</p>
Database of geochemical analyses of soils and rocks related to the Bosumtwi impact crater
<p>This excel file contains geochemical analyses of soils and rocks related to the Bosumtwi impact crater and its surroudings geological units - all data included in this database have been extracted from peer-reviewed literature.</p>
Geologic Map of Tyre Impact Crater on the Galilean Moon Europa - Digitized and Modified Versions (2024)
<p><strong>Geologic Map of Tyre Impact Crater on the Galilean Moon Europa - Digitized and Modified Versions (2024)</strong></p> <p>Files and deliverable documentation of the process of digitizing the geologic map of Tyre, Kadel et al. 2000. This may be useful if you are looking to learn how to digitize a geologic map using some form of mapping software, or to learn about the surface geology of Jupiter's icy moon Europa. </p> <p>Includes:</p> <ul> <li>read.me with supporting information</li> <li>map package</li> <li>Georeferenced PDF figures </li> <li>Shapefiles </li> </ul>
Rapid Impact Crater Relaxation Caused by An Insulating Methane Clathrate Crust on Titan: Data and Marc Files
<p><span>Data files for several figures in the manuscript "Rapid Impact Crater Relaxation Caused by An Insulating Methane Clathrate Crust on Titan" Published in The Planetary Science Journal. This includes data for the following figures: 4, 6, 7, 8 and 10. Two example Hexagon Marc-Mentat mud files for the axisymmetric thermal simulation and mechanical simulation of a 10 km thick clathrate, 85 km diameter crater are also included.</span></p> <p><span>Each column is self-explanatory except for the two relative depth data files. "Relative_Depth_Deep_Fig8" includes the results for simulations that use the initially deeper crater depth, and "Relative_Depth_Shallow_Fig8" includes the results for simulations that use the initially shallower crater depth. The columns are labeled with a shorthand notation for pairs of columns that represent the relative crater depth at specified times in the simulation. An example of time is “t(yr)_v21_120_5” and the corresponding relative depth column is “v21_Rd_120_5.” Time is given in years and relative depth is unitless. These specific examples provide results for a simulation that has a viscosity cutoff of 10^21 Pa s and a 120 km diameter crater with a 5 km thick methane clathrate crust overlying water ice.</span></p>
Dataset - Lagain et al., 2021. Latitudinal dependency of the impact rate: any room for a recalibration of crater chronologies ?
<p>data related to the following article: Lagain A. et al. (2021). Latitudinal dependency of the impact rate: any room for a recalibration of crater chronologies ? submitted to <em>EPSL </em>(July 2021).</p>
Model Age Derivation of Large Martian Impact Craters, using automatic crater counting methods / Dataset
<ul> <li>"counting_area" folder: shapefiles of the mapped ejecta layers considered in this study</li> <li>"scc" folder: .scc files readable on CraterStats listing the size and location of craters detected by our CDA and recognized as primaries by the ASCI. The counting area considered for each crater slightly vary from the area indicated in the shapefile due to the removal of Thiessen polygons associated to secondary craters by the ASCI.</li> </ul> <p>The ASCI code and toolbox implementable to ESRI ArcGIS (10.6) is discoverable here: https://github.com/curtin-crater-detection/secondary-crater-removal<br> </p>
Petrophysical data for 29 samples from the Chicxulub impact crater.
<p>Note: ΙΈ-porosity, ρ<sub>b</sub>-bulk density, ρ<sub>g</sub>-grain density, k-permeability, F-formation factor, m-cementation exponent, τ<sup>2</sup>-tortuosity, C<sub>s</sub>-surface conductivity, Vp-acoustic velocity of compressional waves. Uncertainty for porosity, density, permeability, velocity and conductivity is 5%. Uncertainty for formation factor, cementation exponent and tortuosity is 8%). Lith <sup>1 </sup>and Unit <sup>1</sup> after Morgan et al. (2017), Unit <sup>2</sup> after de Graaf et al. (2021, UIM-upper impact melt rock unit, LIMB-lower impact melt rock-bearing unit)) and Kaskes et al. (2021).</p> <p> </p> <p>Morgan, J. V., Gulick, S. P. S., Bralower, T. J., Chenot, E., Christeson, G. L., Claeys, P., et al. (2016). The formation of peak rings in large impact craters. Science, 354(6314), 878–882. <a href="https://doi.org/10.1126/science.aah6561">https://doi.org/10.1126/science.aah6561</a></p> <p>de Graaff, S. J., Kaskes, P., Déhais, T., Goderis, S., Vinciane, D., Ross, C. H., et al. (2021). New insights into the formation and emplacement of impact melt rocks within the Chicxulub impact structure, following the 2016 IODP-ICDP Expedition 364. Geological Society of America Bulletin. <a href="https://doi.org/doi:">https://doi.org/doi:</a> <a href="https://doi.org/10.1130/B35795.1">https://doi.org/10.1130/B35795.1</a></p> <p>Kaskes, P., de Graaff, S. J., Feignon, J. G., Déhais, T., Goderis, S., Ferrière, L., et al. (2021). Formation of the crater suevite sequence from the Chicxulub peak ring: A petrographic, geochemical, and sedimentological characterization. Geological Society of America Bulletin. <a href="https://doi.org/https://doi.org/10.1130/B36020.1">https://doi.org/https://doi.org/10.1130/B36020.1</a></p>
A catalogue of impact craters with diameters larger than 200 m in the Chang'e-6 landing area
<p>Chang'e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. This dataset is craters with diamters larger than 200m in the 134 km × 246 km landing area. The craters are extracted by an automated method and checked manually. </p>
Data for Chandnani and Herrick, Influence of Target Properties on Wall Slumping in Lunar Impact Craters within the Simple-to-Complex Transition
<p>Data files for Figures 3 and S1 of the manuscript. These are craters counted in slump and ejecta units for the purpose of comparing their relative ages.</p> <p>The directory sf_shapefiles contains subdirectories for the 20 craters for which a slump unit and an ejecta unit had crater counts performed. The AREA_* shapefiles are polygons that outline the area studied, and the CRATER_* files are the individual craters counted. These files were created using CraterTools (<a href="https://www.geo.fu-berlin.de/en/geol/fachrichtungen/planet/software/_content/software/index.html">https://www.geo.fu-berlin.de/en/geol/fachrichtungen/planet/software/_content/software/index.html</a>) in ArcGIS, but that software is not necessary to use the shapefiles. </p> <p>The subdirectory SFD_figs_color and SFC_suppl_figs_color are, respectively, the CraterStats (https://www.geo.fu-berlin.de/en/geol/fachrichtungen/planet/software/_content/software/index.html) files used to generate Figure 3 of the main manuscript and Supplemental Figure S1. Each crater comprises a subdirectory of those main directories. Within each subdirectory, the *.plt file is the file directing CraterStats how to make the figure. The *.png file is the output figure from CraterStats, and the *.jpg file is the .png file after it has been cropped and the error bars have been changed from grey to black. The *.txt file provides the output from CraterStats that, among other things, provides the data fit of isochrons to the data. The *.diam files are data extracted from the shapefiles and provide the input into Craterstats. They have the lat-lon-diameter information on the craters and the counting area. All of these are commented text files, so you don’t need CraterStats to look at them and use them.</p>
Data for the paper "Effects of surface and subsurface water/ice on spatial distributions of impact crater ejecta on Mars"
<p>This file contains input data for iSALE simulations reported in the publication:</p><p><i>"</i>Effects of surface and subsurface water/ice on spatial distributions of impact crater ejecta on Mars<i> by Aleksandra Sokolowska, Nicolas Thomas, and Kai Wuennemann</i></p>
Study on the degradation pattern of impact crater communities in Yutu-2's rovering area
<p>Here are the DEM data and DOM data created using images taken by the Yutu-2 rover along the route between the 27th and 33rd moon days.</p> <p>And the extracted impact craters contain their degradation levels.</p> <p>All impact craters are classified into 5 categories (A-AB-B-BC-C)</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.