{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["006:55"], "contactPoint": {"fn": "Peter Bajcsy", "hasEmail": "mailto:peter.bajcsy@nist.gov"}, "description": "This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels.", "distribution": [{"accessURL": "https://isg.nist.gov/deepzoomweb/data/concreteScoring", "description": "his web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes.", "format": "TIFF file format", "title": "2D Segmentation of Concrete Samples for Training AI Models"}, {"accessURL": "https://github.com/usnistgov/WIPP-unet-train-plugin", "title": "UNet CNN Semantic-Segmentation Training plugin"}, {"accessURL": "https://github.com/usnistgov/WIPP-unet-inference-plugin", "title": "UNet CNN Semantic-Segmentation Inference plugin"}, {"accessURL": "https://doi.org/10.18434/M32155", "title": "DOI Access for 2D Segmentation of Concrete Samples for Training AI Models"}], "identifier": "ark:/88434/mds2-2155", "issued": "2019-12-31", "keyword": ["CS-MET computational metrology"], "landingPage": "https://data.nist.gov/od/id/mds2-2155", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2019-11-18 00:00:00", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "theme": ["Information Technology:Computational science"], "title": "2D Segmentation of Concrete Samples for Training AI Models"}