{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Bing Ouyang", "hasEmail": "mailto:bouyang@fau.edu"}, "dataQuality": true, "description": "The dataset contains the data from the field deployment of the Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) system at PNNL-Sequim test site in 2017. The dataset contains scanning of manmade models of baracuda and turtles as well as technical target placed in the field of view of 6 sets of LiDAR transmitter and receiver pairs. In addition, the system also operated to capture any marine life may come within the field of view. This included several encounters of Harbor Seal. In total, about 5TB of LiDAR data was recorded.\n\nUMSLI is a LiDAR system uses lower power red (638nm, 180mW) laser that is invisible to the the marine animals. The red laser beams are scanned out of three bidirectional transmitters reflecting signals back to the receiver as it hits particles in the water, when a laser beam reflects as it hits marine life it is called a hard target. The main purpose of the system is to monitor MHK sites to detect and alert the site manager of any potential encounter between the energy generation equipment and protected marine life. \n\nThis submission includes a data lake resource for the raw data and experiment video recordings, MATLAB scripts for data parsing and analysis, and a folder of project publications. For more information on the data, see the \"Data Description\" resource below. \n\nUMSLI is developed at Harbor Branch Oceanographic Institute at Florida Atlantic University under DOE funding (DE-EE0006787 and DE-EE0007828).\n", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=umsli%2F", "description": "Link for the UMSLI data lake. Includes videos and raw data broken out by month. For more information on the data see the \"Data Description\" resource.", "format": "HTML", "mediaType": "text/html", "title": "2. UMSLI Data Lake on AWS"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/Marine_Animal_Classification_With_Correntropy-Loss-Based_Multiview_Learning.pdf", "description": "Journal Article detailing the multiview learning classification method used in the UMSLI project and its importance. ", "format": "pdf", "mediaType": "application/pdf", "title": "Marine Animal Classification With Correntropy Loss-Based Multiview Learning.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/Marine_animal_classification_using_UMSLI_in_HBOI%20optical%20test%20facility.pdf", "description": "Journal article detailing the marine animal classification methods developed in the UMSLI project. ", "format": "pdf", "mediaType": "application/pdf", "title": "Marine Animal Classification Using UMSLI in HBOI Optical Test Facility.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/NI_TDMS_MATLAB.zip", "description": "Code to parse and analyze data using National Instruments native library which significantly increases parsing speed. ", "format": "zip", "mediaType": "application/zip", "title": "MATLAB Scripts Updated.zip"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/Publications%20%281%29.zip", "description": "Folder containing conference papers and technical reports from the UMSLI project.", "format": "zip", "mediaType": "application/zip", "title": "Publications.zip"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/UMSLI%20Data%20Description.docx", "description": "Description of the data lake data and directory structure.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "1. Data Description.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/UMSLI%20Matlab%20and%20Photos%20%282%29.zip", "description": "Folder containing MATLAB scripts to parse and analyze data, as well as photos captured during experiment for analysis.", "format": "zip", "mediaType": "application/zip", "title": "MATLAB Scripts and Photos.zip"}, {"@type": "dcat:Distribution", "accessURL": "https://mhkdr.openei.org/files/507/Underwater_LiDAR_Image_Enhancement_Using_a_GAN_Based_Machine_Learning_Technique.pdf", "description": "Journal article detailing the GAN based machine learning technique used for LiDAR image enhancement in the UMSLI project.", "format": "pdf", "mediaType": "application/pdf", "title": "Underwater LiDAR Image Enhancement Using a GAN Based Machine Learning Technique.pdf"}, {"@type": "dcat:Distribution", "accessURL": "https://registry.opendata.aws/marine-energy-data/", "description": "AWS public dataset program registry page for data released under the Department of Energy's Water Power Technologies Office (DOE WPTO) Marine Energy Data Lake. The registry page contains information about dataset documentation, access, and contact, for each of the Marine Energy Data Lake datasets.", "format": "HTML", "mediaType": "text/html", "title": "Marine Energy Data Registry on AWS"}], "identifier": "https://data.openei.org/submissions/8475", "issued": "2023-09-22T06:00:00Z", "keyword": ["Data", "Florida Atlantic University", "Harbor Branch", "Hydrokinetic", "Imager", "LiDAR", "MHK", "Marine", "Multi-Static", "PNNL", "Raw Data", "Scanning", "UMSLI", "Unobtrusive", "encounter", "energy", "marine animal", "power", "red laser", "scan"], "landingPage": "https://mhkdr.openei.org/submissions/507", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2026-01-21T15:42:28Z", "programCode": ["019:009"], "projectLead": "Carrie Noonan", "projectNumber": "EE0006787", "projectTitle": "Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations", "publisher": {"@type": "org:Organization", "name": "Florida Atlantic University"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}", "title": "Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) Dataset"}