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Found 29 dataset(s) matching "convolutional neural network".
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<p>This dataset contains the spatiotemporal data used to train the spatiotemporal deep neural networks described in "Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal...
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This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch...
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Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the...
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The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri,...
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Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building...
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The U.S. Geological Survey (USGS), in association with the Multi-Resolution Land Characteristics (MRLC) Consortium, produces the National Land Cover Database (NLCD) for the United States. The...
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The Delaware River Basin (DRB) covers portions of five states (Delaware, Maryland, New Jersey, New York, and Pennsylvania) and several geologic provinces, encompassing much of the complex geology...
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This model archive contains the input data, model code, and model outputs for machine learning models that predict daily non-tidal stream salinity (specific conductance) for a network of 459...
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This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the same horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to...
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The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of...
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The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of...
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The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of...
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The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of...
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The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of...
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We provide annotated fish imagery data for use in deep learning models (e.g., convolutional neural networks) for individual and species recognition. For individual recognition models, the dataset...
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The data are a set of fluorescent images that were generated to support the development of a machine learning model. The approach combines fluorescence imaging, deep learning, a mobile...
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The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of...
