Mars orbital image (HiRISE) labeled data set

This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels. Contents: map-proj/: Directory containing individual cropped landmark images labels-map-proj.txt: Class labels (ids) for each landmark image landmark_mp.py: Python dictionary that maps class ids to semantic names Attribution: If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301 Please also cite this paper, which provides additional details about the data set. Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence, 2018.  </p

Planetary body limb and plume labels for NASA images

This data set was compiled to aid in evaluating methods for automated analysis of images to detect planetary bodies and limbs.  It contains manually generated labels for 308 NASA images of planets and moons.  The labels annotate the location of the limb (edge) of the body and plumes emitted by the body, if any.  "Plume" in this context refers to any bright material emitted from the body, such as icy plumes from Enceladus or volcanic plumes from Io. 112 of the labeled images contain plumes. Contents: This data set covers images collected by the following instruments: Cassini Imaging Science Subsystem (ISS) Galileo Solid-State Imaging (SSI) MESSENGER Mercury Dual Imaging System (MDIS) New Horizons Long Rang Reconnaissance Imager (LORRI)                                                                      The target bodies include the planet Mercury; Jupiter's moons Callisto, Europa, Ganymede, and Io; and Saturn's moon Enceladus.     There is a directory for each instrument_target combination: cassini_iss_enceladus/: Cassini ISS narrow-angle camera observations of Saturn's moon Enceladus galileo_ssi_callisto/: Galileo SSI observations of Jupiter's moon Callisto galileo_ssi_europa/: Galileo SSI observations of Jupiter's moon Europa galileo_ssi_ganymede/: Galileo SSI observations of Jupiter's moon Ganymede galileo_ssi_io/: Galileo SSI observations of Jupiter's moon Io messenger_mdis_mercury/: MESSENGER MDIS narrow-angle and wide-angle observations of Mercury  new_horizons_lorri_io/: New Horizons LORRI observations of Jupiter's moon Io Source images:  The images that are associated with each label file can be obtained from the Planetary Data System (PDS) at https://pds-imaging.jpl.nasa.gov/search .  For Cassini ISS, MESSENGER MDIS, and New Horizons LORRI images, search on the product id from the label filename.  For example, the product id for   lor_0035092814_0x630_sci_label.yml is lor_0035092814_0x630_sci For Galileo SSI images, the filename does not include the product id. A list of the source product ids is included in the file named  galileo_image_ids.txt Label format: Labels are stored in YAML format.  The limb is annotated as a series of points marked along the limb such that a least-squares circle fit of those points provides a model of the body's limb ("points" field).  Plumes, when present, are indicated as one or more angular ranges (in radians) around the limb within which plume activity is present ("plumes"->"intervals" field).  Angles are specified starting with 0 radians (up) and proceeding clockwise.  The user who generated the labels is recorded in the "user" field.    Example (galileo_ssi_io/0085r_label.yml): Six points define the limb of the body, and there are two areas of plume activity.  comment: Points are in (x, y), i.e. (col, row), order. plumes: comment: Intervals in radians intervals: - [2.8540078295092326, 3.020573420947303] - [3.219430581132992, 3.352047930386058] user: mcameron points: - [123.09103311855094, 322.15933747194225] - [143.78220150957688, 79.06162214909591] - [89.23475042871942, 219.0261628709045] - [256.9650789538681, 407.0275241755675] - [176.84241267100913, 375.9514805780413] - [113.07652569773596, 121.72097703075002] user: mcameron Attribution: If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.2556063 . </p

Mars orbital image (HiRISE) labeled data set version 3

This data set contains a total of 73,031 landmarks. 10,433 landmarks were detected and extracted from 180 HiRISE browse images, and 62,598 landmarks were augmented from 10,433 original landmarks. For each original landmark, we cropped a square bounding box that includes the full extent of the landmark plus a 30-pixel margin to left, right, top and bottom. Each cropped landmark was resized to 227x227 pixels, and then was augmented to generate 6 additional landmarks using the following methods: 1. 90 degrees clockwise rotation 2. 180 degrees clockwise rotation 3. 270 degrees clockwise rotation 4. Horizontal flip 5. Vertical flip 6. Random brightness adjustment Contents: - map-proj-v3/: Directory containing individual cropped landmark images - labels-map-proj-v3.txt: Class labels (ids) for each landmark image - landmarks_map-proj-v3_classmap.csv: Dictionary that maps class ids to semantic names Attribution: If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.2538136</p

Mars novelty detection Mastcam labeled dataset

These datasets were used for experiments in the paper: Kerner, H. R., Wagstaff, K. L., Bue, B. D., Wellington, Jacob, S., D. F., Bell III, J. F., Kwan, C., Ben Amor, H. Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions. Under review, 2020. All source images are publicly released Experiment Data Records (EDRs) archived by the Planetary Data System (PDS).</p

Mars novelty detection Mastcam labeled dataset

These datasets were used for experiments in the paper: Kerner, H. R., Wellington, D. F., Wagstaff, K. L., Bell III, J. F., Ben Amor, H. Novelty Detection for Multispectral Images with Application to Planetary Exploration. In Proceedings of Innovative Applications in Artificial Intelligence (IAAI/AAAI), 2019.  The file cae_train.zip contains examples used for training the convolutional autoencoder. Each example is a Numpy array (.npy) of size 64x64x6 pixels. The file novel_images.zip contains the 332 tiles labeled as "novel" for containing novel geologic features. Each example is a Numpy array (.npy) of size 64x64x6 pixels. The file cnn_direct.zip contains examples used for fine-tuning pre-trained networks. Images are divided into "vis" (shorter wavelengths) and "nir" (longer wavelengths) and by their label of "typical" vs. "novel." These three-channel images are stored as .jpg files. All files are named with the following convention: sequence_id_XX*_{R,L}Y_solZZZZ_N.npy where XX* is the sequence ID for the image, {R,L}Y indicates the right (R) or left (L) eye of the camera and the image number in the sequence (Y), and ZZZZ is the four-digit sol (Martian day since the rover began operations) the image was acquired on. All source images are publicly released Experiment Data Records (EDRs) archived by the Planetary Data System (PDS).</p