CU (survey) database – The full CU (Collection Unit, i.e. “tract”) database, which includes all tract-survey data from all teams together in one place. This file is a .CSV export from FileMaker. Each entry includes data about each tract surveyed (see data dictionary). Tract locations are available via accompanying GIS shape files. NOTE: some tract database entries lack complete location data, e.g., a UTM Northing is present but not the Easting. These are available via the spatial data files work: https://deepblue.lib.umich.edu/data/concern/data_sets/k0698807d?locale=en. and CU (survey) database, by team – A copy of each team’s (A-K) Collection Unit (CU; i.e. “tract”) database is also included. These files are .CSV exports from the original FileMaker database.
GIS shape files for each tract along with additional, generic spatial data, including files for tract visibility, vegetation, overall pottery density, and overall tile density. The latter two are not chronologically specific; they include all pottery and tile counts by tract, regardless of age.
Each tract has a photo, a few have associated feature photos. Each photo is labelled with the date it was taken, and a consecutive number: ex. “A-150610-001”. Photos are in folders by team, and by date: Team A (362 megs), Team B (963 megs), Team C (638 megs), Team D (1.45 GB), Team E ( 1.41 GB), Team F (619 megs), Team G (461 megs), Team H (233 megs), Team I (817 megs), Team J (903 megs), and Team K (226 megs). Each folder is accompanied by an Excel photo log, exported to CSV, that provides captions.
PDFs of the reports written by survey team leaders at the end of the season, including the report as submitted and a final edited version. There are two reports for each team. [NOTE: in some cases, only the final edited version of a report is included.]
All databases, field notebooks, paper maps, GIS files, photographs, and photo descriptions related to the intensive survey, of tracts and tumuli, and the collection of sites have been made available in PASH Deep Blue Data Realm 1. The data are broadly organized by team (A-K). The surveyed land was divided up into “tracts”. Tracts are labeled with team letter and a consecutive number: e.g., A-001, A-002, B-003, C-122, D-035.
This work is composed of PDFs of scans of miscellaneous documents related to a particular site, including maps, wall drawings, original notes, etc. For those sites that were systematically surface collected (Sites 001, 002, 003, and 011), scans of the site collection grid and raw counts of collected artifacts (on a “Site Collection Form”) are also included.
UWHandles is a dataset for 6D object pose estimation in underwater fisheye images. It provides 6D pose and 2D bounding box annotations for 3 different graspable handle objects used for ROV manipulation. The dataset consists of 28 image sequences collected in natural seafloor environments with a total of 20,427 annotated frames. and Meta repository for the dataset
https://github.com/gidobot/UWHandles
Billings, G., & Johnson-Roberson, M. (2020). SilhoNet-fisheye: Adaptation of a ROI based object pose estimation network to monocular fisheye images. IEEE Robotics and Automation Letters, 5(3), 4241-4248.
UWslam is a dataset for underwater stereo and hybrid monocular fisheye + stereo SLAM in natural seafloor environments. The dataset includes a spiral survey of a shallow reef captured with a diver operated stereo rig and 4 hybrid image sequences captured with a deep ocean ROV in different deep ocean environments. Ground truth pose estimates for the spiral stereo trajectory were obtained by processing the image sequence through COLMAP. Ground truth pose estimates for the hybrid sequences were obtained by distributing fiducials on the seafloor before capturing an image sequence and processing the image sequences with the ROS based TagSLAM package.
G. Billings, R. Camilli and M. Johnson-Roberson, "Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems," in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6798-6805, July 2022, doi: 10.1109/LRA.2022.3176448.
