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  1. Fuzzy Classification of Sentinel 2 data with UAV-derived label data [dataset] [Download]

    Title: Fuzzy Classification of Sentinel 2 data with UAV-derived label data [dataset]
    Contributors: Data curator: Carbonneau, Patrice (Durham University, UK)
    Data collector: Belletti, Barbara (Politecnico Milano)
    Data collector: Micotti, Marco (Politecnico Milano)
    Mariani, Stefano (ISPRA)
    Castelletti, Andrea (Politecnico Milano)
    Bizzi, Simone (Universita Padova)
    Description:
    Keywords: Machine Learning, Fuzzy Classification, Sentinel 2, UAV
    Date Uploaded: 5 February 2020
  2. Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights] [Download]

    Title: Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights]
    Contributors: Creator: Atapour-Abarghouei, Amir (Durham University, UK)
    Contact person: Atapour-Abarghouei, Amir (Durham University, UK)
    Editor: Atapour-Abarghouei, Amir (Durham University, UK)
    Contact person: Breckon, Toby P. (Durham University, UK)
    Editor: Breckon, Toby P. (Durham University, UK)
    Description: The data file has been created using PyTorch ( https://pytorch.org
    ) and contains the weights of a generative model trained to perform monocular depth estimation and semantic segmentation. The file is needed as part of the pipeline for the project at  https://github.com/atapour/temporal-depth-segmentation
    Instructions on how the file can be used and how the code is run can also be found at  https://github.com/atapour/temporal-depth-segmentation
    Keywords: Monocular Depth Estimation, Semantic Segmentation, Temporal Consistency, Computer Vision
    Date Uploaded: 15 March 2019
  3. Pretrained Neural Network Models for Guo 2018 study - TensorFlow format [Download]

    Title: Pretrained Neural Network Models for Guo 2018 study - TensorFlow format
    Contributors: Creator: Guo, Tiancheng (Durham University, UK)
    Data collector: Guo, Tiancheng (Durham University, UK)
    Data curator: Guo, Tiancheng (Durham University, UK)
    Contact person: Breckon, Toby (Durham University, UK)
    Data curator: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Computer vision, Raindrop detection, Rain detection, Rain removal, Rain noise removal, Rain interference, Scene context, Raindrop saliency, Rain classification, Convolutional neural network (CNN), Deep learning
    Date Uploaded: 25 November 2018
  4. Pretrained Neural Network Models for Dunnings 2018 study - TensorFlow format [Download]

    Title: Pretrained Neural Network Models for Dunnings 2018 study - TensorFlow format
    Contributors: Creator: Dunnings, Andy (Durham University, UK)
    Editor: Dunnings, Andy (Durham University, UK)
    Contact person: Breckon, Toby (Durham University, UK)
    Editor: Breckon, Toby (Durham University, UK)
    Description:
    Keywords: Convolutional Neural Network
    Date Uploaded: 26 July 2018
  5. Fire Superpixel Image Data Set for Samarth 2019 study - PNG still image set [Download]

    Title: Fire Superpixel Image Data Set for Samarth 2019 study - PNG still image set
    Contributors: Creator: Samarth, Ganesh (Institute of Technology Dharwad, India)
    Contact person: Breckon, Toby (Durham University, UK)
    Editor: Bhowmik, Neelanjan (Durham University, UK)
    Description:
    Keywords: Convolutional Neural Network, fire detection
    Date Uploaded: 13 December 2019
  6. Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format [Download]

    Title: Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format
    Contributors: Contact person: Breckon, Toby (Durham University, UK)
    Creator: Samarth, Ganesh (Institute of Technology Dharwad, India)
    Editor: Bhowmik, Neelanjan (Durham University, UK)
    Description:
    Keywords: Convolutional Neural Network, fire detection
    Date Uploaded: 11 December 2019