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Chang'e 5 Landing Camera Crater Detection Dataset
<p>132 hand-labelled images from the Chang'e 5 Landing Camera. Visible impact craters in each image have their crater rim inscribed by a bounding ellipse.</p> <p>On average, there are approximately 50 labelled craters per image.</p> <p>The first 100 images of the landers descent were labelled - this is the intended training set.</p> <p>Every 10 images of the remaining 313 were then labelled - this is the intended testing set.</p> <p> </p> <p>File Descriptions:</p> <p>CE5-ellipse-labels: joblib dump of ellipse parameters per image.</p> <p>change5-*.json: Raw labels as produced by the labelling software of choice, Label Studio.</p> <p> </p> <p>Images:</p> <p>The images used in this work were produced and processed by the Ground Research and Application System (GRAS) of China's Lunar and Planetary Exploration Program (https://moon.bao.ac.cn). Specifically, the first 413 images from the Chang'e 5 landing camera level 2A were used. The images can be downloaded from here: <a href="https://moon.bao.ac.cn/ce5web/searchOrder_hyperSearchData.search?pid=CE5/LCAM/level/2A" target="_blank" rel="noopener">https://dx.doi.org/10.12350/CLPDS.GRAS.CE5.LCAM-2A.vA</a>.</p> <p> </p> <p>Reference and Acknowledgement:</p> <p>Users of these annotations and associated data are requested to cite both the original dataset source (https://moon.bao.ac.cn) and the following paper:</p> <p>Matthew Rodda, Sofia McLeod, Ky Cuong Pham, and Tat-Jun Chin. (2024). Camera-Pose Robust Crater Detection from Chang'e 5. doi: https://doi.org/10.48550/arXiv.2406.04569</p> <p> </p> <p>BibTeX:</p> <pre><code>@misc{rodda2024camerapose, title={Camera-Pose Robust Crater Detection from Chang'e 5}, author={Matthew Rodda and Sofia McLeod and Ky Cuong Pham and Tat-Jun Chin}, year={2024}, eprint={2406.04569}, archivePrefix={arXiv}, primaryClass={cs.CV} }</code></pre>
Fig. 1 in A preliminary camera trapping study of mammals of Monti Lepini (Central Italy)
Fig. 1 - Geographical location of the Lepini Mountains area and positions of the camera traps (the different colours group the two arrays of cameras). / Collocazione geografica dei Monti Lepini e schema di posizionamento delle fototrappole (i colori differenti sono per le due disposizioni consecutive di ogni sessione).
Fig. 4 in A novel method to obtain accurate length estimates of carnivorous reef fishes from a single video camera
Fig. 4. Boxplots showing the length distributions (mm) for Acanthistius patachonicus in five Submarine Parks within Golfo Nuevo, estimated by the naive (n = 132), Reference scale at the bait level (RSB) (n = 98) and Mirrored baited underwater video system (MBUV) (n = 132) methods. The numbers of fish measured with the MBUV in each reef are indicated, and the widths of the boxes within each reef are proportional to those numbers. The x-axis was truncated to avoid loss of detail.
Fig. 1 in A novel method to obtain accurate length estimates of carnivorous reef fishes from a single video camera
Fig. 1. Mirrored baited underwater video system (MBUV). (a) MBUV settled on bottom for calibration purposes: 1) video signal and remote control, 2) strained cables marked every 200 mm segments, 3) placement of the bait holder, 4) mirrored surface; (b) Camera view of a MBUV deployment showing the naive length of a sea bass Acanthistius patachonicus (NL), the length of its reflected image (LRI), and the reference scales (RS = 200 mm) used for applying the MBUV and Reference scale at the bait level (RSB) procedures. Four fishes (1-4) could be accurately measured in this snapshot; fish #4 could be sized even when its body laid wholly outside of the mirrored area but was partially reflected by it.
Fig. 3 in A novel method to obtain accurate length estimates of carnivorous reef fishes from a single video camera
Fig. 3. Histogram and boxplot showing the distribution of the relative errors of length estimates for three plastic silhouettes of 112 mm, 360 mm and 444 mm. The boxplot below indicates a median relative error = -0.9%.
Fig. 5 in A novel method to obtain accurate length estimates of carnivorous reef fishes from a single video camera
Fig. 5. Scatter plots with marginal histograms showing fish length estimates (mm) obtained with (a) Mirrored baited underwater video system (MBUV) and naive methods (n = 132), and with (b) MBUV and Reference scale at the bait level (RSB) methods (n = 67), in five Submarine Parks within Golfo Nuevo. Solid circles in (a) represent truncated lengths (>550 mm). The corresponding naive estimates for the truncated lengths are indicated next to the circles. Solid black lines represent the regression 1:1 in both boxes.
Fig. 2 in A novel method to obtain accurate length estimates of carnivorous reef fishes from a single video camera
Fig. 2. Calibration (a) and 'Height' (b) functions for the Mirrored baited underwater video system (MBUV) system. The solid lines represent the fitted models. Shaded areas represent 95% confidence regions. Jittered rugs on the x-axis indicate the distribution of the NL/LRI ratio and NL/LRI ratio-1 data, respectively. NL = naive length, LRI = length of the reflected image.
Fig. 2 in On the activity of two medium-sized canids: the Golden Jackal (Canis aureus) and the Red Fox (Vulpes vulpes) in the Natural Bark "Sinite Kamani" (Bulgaria) revealed by camera traps
Fig. 2. Number of all pictures of Red Fox (Vulpes vulpes) taken during 24h (expressed for one hour time interval) during seasons.
Fig. 4 in On the activity of two medium-sized canids: the Golden Jackal (Canis aureus) and the Red Fox (Vulpes vulpes) in the Natural Bark "Sinite Kamani" (Bulgaria) revealed by camera traps
Fig. 4. Daytime activity (feeding on dog food) of the Red Fox and the Golden Jackal registered by camera traps at the Sinite Kamani Natural Park.
Fig. 1 in On the activity of two medium-sized canids: the Golden Jackal (Canis aureus) and the Red Fox (Vulpes vulpes) in the Natural Bark "Sinite Kamani" (Bulgaria) revealed by camera traps
Fig. 1. Location of Sinite Kamani Natural Park and schematic position of the camera traps through the park (black circles).
Fig. 3 in On the activity of two medium-sized canids: the Golden Jackal (Canis aureus) and the Red Fox (Vulpes vulpes) in the Natural Bark "Sinite Kamani" (Bulgaria) revealed by camera traps
Fig. 3. Number of all pictures of Golden Jackal (Canis aureus) taken during 24h (expressed for one hour time interval) during seasons.
Рис. 2. Черношапочные сурки и их местообитания на хребте КоΑар: A — виΑ на ЦентраΛьный КоΑар и ΑоΛину р. СреΑний Сакукан; B — местообитание сурков поΑ переваΛом; C — местообитание сурков по берегам р. Того; D — местообитание сурков на вершине гребня, каΑр с фотоΛовушки; E — сурки; F — черношапочный сурок обΛизывает пΛасты каменного угΛя, каΑр из виΑеосъемки Fig. 2. Black-capped marmots and their habitats on the Kodar Ridge: A — view of the Central Kodar and the valley of the Middle Sakukan River; B — habitat of marmots under the mountain pass; C — habitat of marmots along the banks of the Togo River; D — marmot habitat at the top of the mountain ridge, camera trap frame; E — marmots; F — the black-capped marmot licks coal, freeze frame from video in On the ecology of the Doppelmayer`s Black-capped marmot (Marmota camtschatica doppelmayeri Birula, 1922): Kodar Mountain Ridge, Transbaikalia, Russia
Рис. 2. Черношапочные сурки и их местообитания на хребте КоΑар: A — виΑ на ЦентраΛьный КоΑар и ΑоΛину р. СреΑний Сакукан; B — местообитание сурков поΑ переваΛом; C — местообитание сурков по берегам р. Того; D — местообитание сурков на вершине гребня, каΑр с фотоΛовушки; E — сурки; F — черношапочный сурок обΛизывает пΛасты каменного угΛя, каΑр из виΑеосъемки Fig. 2. Black-capped marmots and their habitats on the Kodar Ridge: A — view of the Central Kodar and the valley of the Middle Sakukan River; B — habitat of marmots under the mountain pass; C — habitat of marmots along the banks of the Togo River; D — marmot habitat at the top of the mountain ridge, camera trap frame; E — marmots; F — the black-capped marmot licks coal, freeze frame from video
Рис. 5. Пятнистый оΛень неоΑнократно снят фотоΛовушками в бассейнах рек Обор и Àурмин. 15.10.2020. 14.49. Фото А. С. БатаΛова Fig. 5. Sika deer repeatedly photographed by camera traps in the basins of the rivers Obor and Durmin; 15.10.2020. 14:49. Photo by A. S. Batalova in New data on the distribution of sika deer Cervus nippon Temminck, 1838 in the Lower Amur Region
Рис. 5. Пятнистый оΛень неоΑнократно снят фотоΛовушками в бассейнах рек Обор и Àурмин. 15.10.2020. 14.49. Фото А. С. БатаΛова Fig. 5. Sika deer repeatedly photographed by camera traps in the basins of the rivers Obor and Durmin; 15.10.2020. 14:49. Photo by A. S. Batalova
Рис. 1. Карта-схема заповеΔника «БоΛьшехехцирский» и распоΛожение фотоΛовушек на территории. ЛегенΔа: спΛошная черная Λиния — границы заповеΔника; пунктирная Λиния — границы заказника «Хехцирский»; красный кружок — место установки фотоΛовушки Fig. 1. The map of the Bolshekhekhtsirsky State Nature Reserve and the location of camera traps. Legend: solid black line boundaries of the reserve; dotted line — bou in New data on the mammalian fauna of the Bolshekhekhtsirsky Nature Reserve
Рис. 1. Карта-схема заповеΔника «БоΛьшехехцирский» и распоΛожение фотоΛовушек на территории. ЛегенΔа: спΛошная черная Λиния — границы заповеΔника; пунктирная Λиния — границы заказника «Хехцирский»; красный кружок — место установки фотоΛовушки Fig. 1. The map of the Bolshekhekhtsirsky State Nature Reserve and the location of camera traps. Legend: solid black line boundaries of the reserve; dotted line — bou
Figure 2. The class diagram for the Player and Camera movements-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.</p>
DS6.SSSA-02. Human_Walking_Dataset_at_SSSA. Dataset for characterizing the walking behavior of subjects and identification of changes in the motion patterns, based on RGB-D cameras.
<p>This dataset is used for characterizing the wakling behavior of subjects. It is based on RGB-D camerasand obtained through data collection experiments at the premises of the Percro Labotory, TeCIP Intitute, Scuola Superiore Sant'Anna (Pisa, Italy). Data are collected for the gait patterns of 9 healthy participants.</p>
Supporting data for "Snap happy: camera traps are an effective sampling tool when compared to alternative methods"
<p>Author recommendations and response ratios extracted from studies comparing camera traps to another survey method. These data underlie the analyses in a the journal article 'Snap happy: camera traps are an effective sampling tool when compared to alternative methods', published in the journal Royal Society Open Science (https://doi.org/10.1098/rsos.181748). </p>
Camera trap fauna survey in Talissieu (France) 2016-2018
<p>Mammals and birds presence identified from a camera trap survey in a mixed forest and fields environment in the Lavours marsh (France). This dataset gathers observations from April 2016 to November 2018 (with some missing days) from a single camera trap (2 successive models) placed in different locations in Talissieu (Ain, France).</p> <p>See <a href="https://doi.org/10.5281/zenodo.2533381">doi:10.5281/zenodo.2533381</a> for more information.</p> <p>location : 5.7211 45.8625 (WGS84)</p> <p>fields :</p> <pre>id_obs <int> identifier date_heure <dttm> UTC date time of observation (%Y-%m-%dT%H:%M:%SZ) cd_nom_taxref <int> TAXREF taxon identifier see https://inpn.mnhn.fr/programme/referentiel-taxonomique-taxref taxref_cd_ref <int> TAXREF valid taxon identifier taxon <chr> taxon name (binomial) espece <chr> taxon name (vernacular, french) nom_complet <chr> taxon name (binomial with author) nom_complet_html <chr> taxon name (binomial italicized, with author) classe <chr> class ordre <chr> order famille <chr> family rang <chr> rank (ES : species, GN : genus, FM : family, OR : order, CL : class) effectif <int> number of individuals sexe <chr> sex type <chr> V: visual direction <chr> direction of travel (mostly blank) temperature <int> ambiant temperature (°C) duree_estimee_min <int> duration in frame (min) rem_obs <chr> observation note id_session <int> session identifier debut_session <dttm> session date time start UTC (%Y-%m-%dT%H:%M:%SZ) fin_session <dttm> session date time end UTC (%Y-%m-%dT%H:%M:%SZ) duree_session_h <int> session duration (h) rem_session <chr> session note id_localisation <int> camera trap location identifier lieu <chr> camera trap location name x_wgs84 <dbl> camera trap longitude (decimal degrees WGS84) y_wgs84 <dbl> camera trap latitude (decimal degrees WGS84) alti <int> camera trap altitude (m, NGF) azimuth <int> camera trap azimuth (°) habitat <chr> habitat type environnement <chr> local environment type cible <chr> target camera <chr> camera model id_etude <int> study identifier nom_etude <chr> study name date <date> observation date (%Y-%m-%d) heure <dbl> observation hour (%I, UTC)</pre> <p> </p>
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
KUCL: Korea University Camera-LIDAR Dataset
<p><strong>Overview</strong></p> <p>The Korea University Camera-LIDAR (KUCL) dataset contains images and point clouds acquired in indoor and outdoor environments for various applications (e.g., calibration of rigid-body transformation between camera and LIDAR) in robotics and computer vision communities.</p> <ul> <li>Indoor dataset: contains 63 pairs of images and point clouds ('indoor.zip'). We collected the indoor dataset in a static indoor environment with walls, floor, and ceiling.</li> <li>Outdoor dataset: 61 pairs of images and point clouds ('outdoor.zip'). We collected the outdoor dataset in an outdoor environment including buildings and trees.</li> </ul> <p><strong>Setup</strong></p> <p>The images were taken using a Point Grey Ladybug5 (<a href="https://www.ptgrey.com/ladybug5-30-mp-usb-30-spherical-digital-video-camera-black">specifications</a>) camera and point clouds were acquired with a Velodyne VLP-16 LIDAR (<a href="https://velodynelidar.com/vlp-16.html">specifications</a>). We rigidly mounted both sensors on the sensor frame during the overall data acquisition. Each pair of images and point clouds was discretely acquired while maintaining the sensor system standing still to reduce time-synchronization problems.</p> <p><strong>Description</strong></p> <p>Each dataset (zip file) is organized as follows:</p> <ul> <li>images/pano: This folder contains spherical panorama images (8000 X 4000) collected using the Ladybug5.</li> <li>images/pinhole/cam0~cam5: These folders contain rectified pinhole images (2448 X 2048) collected using six cameras (cam0~cam5) of the Ladybug5.</li> <li>images/pinhole/mask: This folder contains the mask (BW image) of each camera of the Ladybug5.</li> <li>images/pinhole/cam_param_pinhole.txt: This file contains extrinsic (transformation from the Ladybug5 to each lens) and intrinsic (focal length and center) parameters of each lens of the Ladybug5. For details of Ladybug5 coordinate system, please refer to the <a href="https://www.ptgrey.com/tan/10621">technical application note</a>.</li> <li>scans: This folder contains point clouds collected using the VLP-16 LIDAR in text files. The first line of each file is the number of points (N), and the remaining lines are points and corresponding reflectivities (N X 4).</li> </ul> <p>We also provide MATLAB <a href="https://drive.google.com/file/d/1aeYfmquaivnUWWTjJ6kBT1jtPEihr-1g/view?usp=sharing">functions</a> projecting point cloud onto spherical panorama and pinhole images. Before running the following functions, please unzip the dataset file ('indoor.zip' or 'outdoor.zip') under the main directory.</p> <ul> <li>run_pano_projection.m: This function projects points onto a spherical panorama image. Lines 19-20 select dataset and index of an image and a point cloud.</li> <li>run_pinhole_projection.m: This function projects points onto a pinhole image. Lines 19-21 select dataset, index of an image and a point cloud, and pinhole camera index.</li> </ul> <p>The rigid-body transformation between the Ladybug5 and the VLP-16 in each function is acquired using our edge-based Camera-LIDAR calibration method with Gaussian Mixture Model (GMM). For the details, please refer to our paper (<a href="https://doi.org/10.1002/rob.21893">https://doi.org/10.1002/rob.21893</a>).</p> <p><strong>Citation</strong></p> <p>Please cite the following paper when using this dataset in your work.</p> <ul> <li>Jaehyeon Kang and Nakju L. Doh, "Automatic Targetless Camera-LIDAR Calibration by Aligning Edge with Gaussian Mixture Model," Journal of Field Robotics, vol. 37, no. 1, pp.158-179, 2020.</li> <li>@ARTICLE {kang-2020-jfr,<br> AUTHOR = {Jaehyeon Kang and Nakju Lett Doh},<br> TITLE = {Automatic Targetless Camera–{LIDAR} Calibration by Aligning Edge with {Gaussian} Mixture Model},<br> JOURNAL = {Journal of Field Robotics},<br> YEAR = {2020},<br> VOLUME = {37},<br> NUMBER = {1},<br> PAGES = {158--179},<br> }</li> </ul> <p><strong>License information</strong></p> <p>The KUCL dataset is released under a Creative Commons Attribution 4.0 International License, <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></p> <p><strong>Contact Information</strong></p> <p>If you have any issues about the KUCL dataset, please contact us at <a href="mailto:kangjae07@gmail.com">kangjae07@gmail.com</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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
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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.