{"action":"create","ckan_id":null,"date_created":"Sat, 28 Mar 2026 10:58:23 GMT","date_finished":null,"harvest_job_id":"d312fd5d-2fa0-42e6-85d8-2d9562200655","harvest_source_id":"3dddf3ae-84a3-4731-88b4-6a0b57d0e503","id":"6b5f9330-2cb3-42d7-a5aa-4543445d7d93","identifier":"https://datainventory.usbr.gov/rise/item/6272","parent_identifier":null,"source_hash":"fa6a83de0ad704d5ee5d54050588430b9c44c6fdba2c7fd6f320318fe64170f6","source_raw":"{\"accessLevel\": \"public\", \"accrualPeriodicity\": \"irregular\", \"bureauCode\": [\"010:10\"], \"contactPoint\": {\"@type\": \"vcard:Contact\", \"fn\": \"RISE Team\", \"hasEmail\": \"mailto:data@usbr.gov\"}, \"description\": \"Report summarizing automated concrete crack mapping using deep learning. Crack mapping concrete structures is a way to document and monitor cracks. In the past, crack mapping has been very labor\\r\\nintensive from data collection to documentation. The use of UAS and photogrammetry has allowed for faster and more comprehensive data collection and products including high-resolution orthoimages used to identify and document cracks. In\\r\\naddition, deep learning models can be used to automatically identify cracks from the orthoimages. This paper presents the process used to develop a deep learning model for automatic crack detection from data collected by UAS.\", \"distribution\": [{\"@type\": \"dcat:Distribution\", \"accessURL\": \"https://data.usbr.gov/catalog/4420/item/6272\", \"description\": \"Landing page for \\\"S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning \\\"\", \"mediaType\": \"text/html\", \"title\": \"RISE Item Details Page URL for \\\"S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning \\\"\"}, {\"@type\": \"dcat:Distribution\", \"description\": \"\\\"S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning \\\" as a PDF file\", \"downloadURL\": \"https://data.usbr.gov/rise/content-rise-public/rise/catalog-item/binary/ST-2020-20105-01 Final.pdf\", \"mediaType\": \"application/pdf\", \"title\": \"PDF File for \\\"S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning \\\"\"}], \"identifier\": \"https://datainventory.usbr.gov/rise/item/6272\", \"keyword\": [\"Data Visualization\"], \"landingPage\": \"https://data.usbr.gov/catalog/4420/item/6272\", \"modified\": \"2020-10-01T22:05:07Z\", \"publisher\": {\"@type\": \"org:Organization\", \"name\": \"Bureau of Reclamation\"}, \"spatial\": \"{\\\"type\\\":\\\"Polygon\\\",\\\"coordinates\\\":[[[-105.12484,39.72238],[-105.1217,39.72366],[-105.1199,39.72123],[-105.12164,39.72052],[-105.12285,39.72213],[-105.12431,39.72153],[-105.12484,39.72238]]]}\", \"title\": \"S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning \"}","source_transform":null,"status":"error"}