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7 results for “ship detection”
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Ship tracks detected using machine learning algorithm
<p>The filtered, vector ship tracks detected using the linked machine learning algorithm and derived from the linked segmentation masks. Each dataset contains the date and other related data for each shiptrack polygon. The `_geo` dataset contains the polygons on a lat/lon coordinate system while the other provides the polygons on the MODIS swath (pixel) indices.</p>
SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations
<p><strong>SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations</strong></p> <p>Availability of domain-specific datasets is a challenge for object detection. In particular, conducting onsite experiments is risky and time-consuming for the maritime domain. It is not always possible to capture challenging situations and incorporate variations in the environment. Therefore, we present a simulation-based dataset for maritime environments consisting of 9471 high-resolution (1920x1080) images resembling the real world with precise annotations.</p> <p>The dataset was acquired using a simulation tool, AILiveSim [17]. AILiveSim is a 3D simulation platform for targeted scalable development and integration of autonomous systems through digital twins. The tool provides a realistic 3D model of the route of the watercraft extended from the city of Turku to Ruisalo in South-West Finland.</p> <p>Our dataset incorporates diversity of objects, weather, illumination, visible proportion and scale in images that resemble the real world. </p> <p>Please cite the following publication when using the dataset:</p> <p>M. Raza, H. Prokopova, S. Huseynzade, S. Azimi and S. Lafond, "SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations," <em>OCEANS 2022, Hampton Roads</em>, Hampton Roads, VA, USA, 2022, pp. 1-5, doi: 10.1109/OCEANS47191.2022.9977182.</p> <p>The publication is available at: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977182">https://doi.org/10.1109/OCEANS47191.2022.9977182</a></p> <p>A preprint version of the publication is available at <a href="https://arxiv.org/abs/2211.05237">https://arxiv.org/abs/2211.05237</a></p> <p> </p> <p> </p>
Training data for ship track detection machine learning algorithms
<p>The training data and labels used to train the linked machine learning algorithm</p>
Ship-detection-YOLOv8-main
<p>Ship-detection-YOLOv8-main</p>
SAR Ship Detection
<p>you can use the file.</p>
Improvement of Lightweight Small Object Ship Detection Network Based on YOLOv7-tiny 模型文件
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.