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28 results for “supervised classification”
Datasets for "Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Classification Algorithms in Reiner Gamma and Mare Ingenii"
<p>Final surface reflectance data at 2.6 m/pixel resolution with floating point values are available as GeoTiff and ASCII text files. Definition files for the K-Means and MLC algorithms in classifying swirl units are also available as ASCII text files. See README file for further details.</p> <p>Data used in the research article:</p> <p>Chuang, F.C., M.D. Richardson, J.R. Weirich, A.A. Sickafoose, and D.L. Domingue, 2022. Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii. The Planetary Science Journal, 3:231. doi://10.3847/PSJ/ac8f43</p> <p> </p>
Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020
Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.
Fast MLE and Supervised Classification for the Beta-Liouville Multinomial -- Gold Standard Data
<p>Gold standard datasets used in the publication Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial. Datasets were prepared by Cardoso-Cachopo (2007).</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
<p>We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection".</p> <p>A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class.</p> <p> </p> <p><strong>DATA:</strong></p> <p><strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: <a href="https://zenodo.org/record/5514180#.Y6SM7y-B2_w">https://zenodo.org/record/5514180</a> . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class).</p> <p><strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class).</p> <p>SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5</p> <p>DeCALS: decals.zip</p> <p><strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field.</p> <p>SDSS (wide filed): the same data as in 2)</p> <p>SDSS (Strip 82 deep field): sdss_stripe82.zip</p> <p>All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: <a href="https://github.com/deepskies/DeepAstroUDA">https://github.com/deepskies/DeepAstroUDA</a> .</p>
A didactical dataset to learn supervised classification with candy
<h2>A didactical dataset to learn supervised classification</h2><p>It was obtained from university level students measuring candy that was mixed and distributed in bowls to them. The goal of this dataset creation was to expose the students to the data taking process. Further, the dataset is meant for classification.</p><h3>Dataset Structure</h3><p>The dataset consists of 6 csv files:</p><ul><li><strong>peanuts.csv</strong> represents the entire dataset (a concatenation of all group?.csv files) omitting the sample column</li><li><strong>peanuts_all.csv</strong> represents the entire dataset (a concatenation of all group?.csv files)</li><li>files matching <strong>group[1-5].csv </strong>represent the measurements of each group</li></ul><h3>Data Representation</h3><p>Each file contains 5 columns. </p><ul><li>color, int values, 0: white, 1: black, 2: brown, 3: other</li><li>shape, int values, 0: irregular, 1 round, 2: lens-like</li><li>height, float values, in millimeter</li><li>width, float values, in millimeter</li><li>label, category, peanut/nopeanut</li></ul><p>For more information on the didactical background, see the <a href="https://proceedings.mlr.press/v141/huppenkothen21a.html">original publication</a> that presented the concept for this activity.</p>
Data from: Supervised classification of plant communities with artificial neural networks
<p>This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:</p><p>Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. <i>Journal of Vegetation Science</i> 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x</p><p>The meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):</p><ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>
IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift Evaluation Dataset
<p>The Chinese Acoustic Scene (CAS) 2023 dataset is a large-scale dataset that serves as a foundation for research related to environmental acoustic scenes. The dataset includes 10 common acoustic scenes, with a total duration of over 130 hours. Each audio clip is 10 seconds long with metadata about the recording location and timestamp. The dataset was collected by members of the <em>Joint Laboratory of Environmental Sound Sensing at the School of Marine Science and Technology, Northwestern Polytechnical University</em>. The data collection period spanned from April 2023 to September 2023, covering 22 different cities across China. The CAS 2023 dataset was collected using the XS-SN-2BE1 manufactured by <em>Xi'an Lianfeng Acoustic Technologies Co., Ltd</em> (https://www.lfxstek.com/). </p> <p>The ICME 2024 <em>Semi-supervised Acoustic Scene Classification under Domain Shift</em> challenge (https://2024.ieeeicme.org/grand-challenge-proposals/, https://ascchallenge.xshengyun.com/) dataset consists of development (https://zenodo.org/records/10616533) and evaluation datasets, all derived from the CAS 2023 dataset. The evaluation dataset includes 1,100 recordings, where data are selected from 12 cities, with 5 unseen cities specifically chosen to provide a more comprehensive evaluation of submissions under domain shift.</p> <p>Baseline: https://github.com/JishengBai/ICME2024ASC</p> <p>Acoustic scenes (10): Bus, Airport, Metro, Restaurant, Shopping mall, Public square, Urban park, Traffic street, Construction site, Bar</p>
Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap
<p>This dataset contains input, intermediary, and output files for the following paper:</p> <p>Yann Forget, Catherine Linard and Marius Gilbert. "<em>Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap</em>", 2018.</p> <p>The dataset is composed of three archives:</p> <ul> <li><code>input.zip</code> : contains raw input data required to run the study ;</li> <li><code>intermediary.zip</code> : contains processed data required for the analysis ;</li> <li><code>output.zip</code> : contains the output tables and images of the study.</li> </ul> <p>Alternatively, output images of the study can be previewed <a href="http://maupp.ulb.ac.be/page/forget2018/">here</a> in interactive maps.</p> <p>The source code used to produce the outputs is availabe <a href="https://zenodo.org/record/1292005">here</a>.</p>
Classifications of auroral phenomena in THEMIS All-Sky images obtained via self-supervised learning
Open the record for dataset details and reuse information.
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
<p>There are 16,930 sound instances in our database with durations ranging 242 from 1 to 10 seconds, which correspond to (approximately) 15 hours of environmental 243 sounds. All sound files were converted into raw 16 bit encoding, mono-channel, and 16 244 kHz sampling rate, as various formats and rates were used in the original versions 245 retrieved from the web.</p>
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
<p>There are 16,930 sound instances in our database with durations ranging 242 from 1 to 10 seconds, which correspond to (approximately) 15 hours of environmental 243 sounds. All sound files were converted into raw 16 bit encoding, mono-channel, and 16 244 kHz sampling rate, as various formats and rates were used in the original versions 245 retrieved from the web.</p>
scPretrain: Multi-task self-supervised learning for cell type classification
<p>The dataset and code for paper, scPretrain: Multi-task self-supervised learning for cell type classification.</p>
Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steel battery tabs
<p>In this folder, excel files are stored with the results of signal processing that supported findings in the following paper:</p> <p>"Using photodiodes and supervised Machine Learning for automatic classification of weld defects in laser welding of thin foils copper-to-steell battery tabs".</p> <p>Matlab scripts and orginal signals will be uploaded soon with more detailed description.</p> <p> </p>
Data, code, models for "Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image"
<p>Data, code and models for https://ieeexplore.ieee.org/document/8410588/</p>
Supervised learning is an accurate method for network-based gene classification - Data
<p>This file contains the data that was used in the paper titled "Supervised learning is an accurate method for network-based gene classification" (https://doi.org/10.1093/bioinformatics/btaa150). Some data was excluded if the license was not permissive enough.</p>
Datasets for Fish Lidar Supervised Classification
<p>This repository contains all of the data used to develop the algorithms for our "Machine learning based region of interest detection in airborne lidar fisheries surveys" paper, which has been published in the SPIE Journal of Applied Remote Sensing. The software that processes the data can be found at <a href="https://doi.org/10.5281/zenodo.5021330">DOI 10.5281/zenodo.5021330</a>.</p> <p>Please cite our journal article if you use the data for research purposes: T. C. Vannoy et al., "Machine learning based region of interest detection in airborne lidar fisheries surveys," SPIE Journal of Applied Remote Sensing 15(3), 038503 (2021). <a href="https://doi.org/10.1117/1.JRS.15.038503">DOI 10.1117/1.JRS.15.038503</a>.</p>
Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification
<p>These are companion data and models of manuscript "Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification" that was submitted to Frontiers in Plant Science.</p> <p>The data is organized into three main folders: 'class_full_imgs', 'seg_full_imgs', and 'unlabeled'. </p> <p>The 'class_full_imgs' folder contains labeled data used to train the classification model, which is divided into train, validation, and test subfolders. Each of these subfolders contains 'oriented' and 'non-oriented' images. </p> <p>The 'seg_full_imgs' folder contains labeled data used to train the segmentation model. The 'InputImages' subfolder contains raw images, and the 'OutputImages' subfolder contains the segmented images. </p> <p>The 'unlabeled' folder contains images without any labels. These images were used for self-supervised pretraining of classification and segmentation models.</p> <p>The trained models can be found in Trained_models.zip. There are four zip files:<br> - "simclr_pretrained_bb.zip" contains the selected pretrained backbone trained via SimCLR.<br> - "nnclr_pretrained_bb.zip" contains the selected pretrained backbone trained via NNCLR.<br> - "finetuned_classification" contains classification models which have undergone end-to-end finetuning, split into supervised and self-supervision-pretrained models.<br> - "segmentation" contains image segmentation models, where the names refer to the pretraining method.</p>
Systematic review of validation of supervised machine learning models in accelerometer-based animal behaviour classification literature
Open the record for dataset details and reuse information.
Data from: Phenotype classification of zebrafish embryos by supervised learning
Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.