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18 results for “cloud detection”
Dataset for the paper "Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset"
<p>We present a large-scale anomaly detection dataset collected from IBM Cloud's Console over approximately 4.5 months. This high-dimensional dataset captures telemetry data from multiple data centers, specifically designed to aid researchers in developing and benchmarking anomaly detection methods in large-scale cloud environments. It contains 39,365 entries, each representing a 5-minute interval, with 117,448 features/attributes, as interval_start is used as the index. The dataset includes detailed information on request counts, HTTP response codes, and various aggregated statistics. The dataset also includes labeled anomaly events identified through IBM's internal monitoring tools, providing a comprehensive resource for real-world anomaly detection research and evaluation.</p> <p><strong>File Descriptions</strong></p> <ul> <li><code>location_downtime.csv</code> - Details planned and unplanned downtimes for IBM Cloud data centers, including start and end times in ISO 8601 format.</li> <li><code>unpivoted_data.parquet</code> - Contains raw telemetry data with 413 million+ rows, covering details like location, HTTP status codes, request types, and aggregated statistics (min, max, median response times).</li> <li><code>anomaly_windows.csv</code> - Ground truth for anomalies, listing start and end times of recorded anomalies, categorized by source (Issue Tracker, Instant Messenger, Test Log).</li> <li><code>pivoted_data_all.parquet</code> - Pivoted version of the telemetry dataset with 39,365 rows and 117,449 columns, including aggregated statistics across multiple metrics and intervals.</li> <li><code>demo/demo.[ipynb|html]</code>: This demo file provides examples of how to access data in the Parquet files, available in Jupyter Notebook (<code>.ipynb</code>) and HTML (<code>.html</code>) formats, respectively.</li> </ul> <p>Further details of the dataset can be found in <strong>Appendix B: Dataset Characteristics</strong> of the <a href="https://arxiv.org/abs/2411.09047">paper</a> titled <strong><em>"Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset."</em></strong> Sample code for training anomaly detectors using this data is provided in <a href="https://doi.org/10.5281/zenodo.14598119" target="_blank" rel="noopener">this package</a>.</p> <p> </p> <p>When using the dataset, please cite it as follows:</p> <pre><code>@misc{islam2024anomaly,</code><br><code> title={Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset}, </code><br><code> author={Mohammad Saiful Islam and Mohamed Sami Rakha and William Pourmajidi and Janakan Sivaloganathan and John Steinbacher and Andriy Miranskyy},</code><br><code> year={2024},</code><br><code> eprint={2411.09047},</code><br><code> archivePrefix={arXiv},</code><br><code> url={https://arxiv.org/abs/2411.09047}</code><br><code>}</code></pre> <p> </p>
Reference dataset for comparison of cloud detection algorithms for Sentinel-2 imagery
<p>Sentinel-2 cloud mask reference dataset generated and analyzed as part of Tarrio, K., Tang, X., Masek, J.G., Claverie, M., Ju, J., Qiu, S., Zhu, Z. and Woodcock, C.E., 2020. Comparison of cloud detection algorithms for Sentinel-2 imagery. Science of Remote Sensing, 2, p.100010. [https://www.sciencedirect.com/science/article/pii/S2666017220300092](https://www.sciencedirect.com/science/article/pii/S2666017220300092)</p> <p><strong>1. Reference masks</strong></p> <p><strong>Algorithms:</strong></p> <ul> <li>Fmask 1.x</li> <li>Fmask 2.x</li> <li>Fmask 4.x</li> <li>Tmask</li> <li>Sen2Cor</li> <li>MAJA</li> <li>LaSRC</li> </ul> <p><strong>Locations:</strong></p> <ul> <li>South Africa (35JPM)</li> <li>Senegal (28PDC)</li> <li>Switzerland (32TLT)</li> <li>France (31TCJ, 31TFJ)</li> <li>Morocco (29RNQ)</li> </ul> <p><strong>Standardized legend:</strong></p> <p>Original algorithm outputs were standardized to the same categorical legend.</p> <ul> <li>0 = clear land</li> <li>1 = clear water</li> <li>2 = cloud shadow</li> <li>3 = snow/ice</li> <li>4 = cloud</li> </ul> <p>All reference masks processed to both 10m and 30m resolution, with the exception of Tmask, which is available only at a 30m resolution.</p> <p><strong>Mask naming convention:</strong></p> <p>All processed masks are named according to the following convention:<br> M<*resolution*><*S2 MGRS tile ID*><*YYYY*><*DOY*><*algorithm*><br> e.g. **M30T28PDC2016351TMASK**</p> <p><br> <strong>2. Interpreted sample points</strong></p> <p>Sample points were selected based on agreement among different map products. This record includes a shapefile with the final interpretations for each of the sampled sites. (See publication for additional information.)</p>
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>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
<p>1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m<sup>2</sup>) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.</p>
Data in support of the publication "First Detection and Thermal Characterization of Terminator CO2 Ice Clouds with ExoMars/NOMAD" on Geophysical Research Letters
<p>This archive contains the data underlying the 3 Figures in the main manuscript, including the vertical profiles of retrieved aerosols, vertical profiles of CO2 saturation ratio and spatial distribution of the retrieved clouds.</p>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
Open the record for dataset details and reuse information.
WHUL8-CDb, a block-level labelled cloud detection dataset collected from Landsat 8
<p>WHUL8-CDb was collected for the training of weakly-supervised cloud detection methods. It includes 500 globally distributed Landsat 8 Level-1C images (Fig. 1) collected following the distribution of the L8 Biome dataset as it comes to biomes and dominant land cover types. The motivation is that clouds are similar with each other in different regions, while the backgrounds are very different. In order to increase the diversity of training samples, we selected images with more background pixels by setting the cloud percentage from 0 to 50 when searching images. To handle all cloud situations, 5 images full of clouds were collected and included in WHUL8-CDb.</p> <p>The WHUL8-CDb dataset canbe downloaded on the following links:</p> <p>First part (1~11) : <a href="http://doi.org/10.5281/zenodo.6420035">https://doi.org/10.5281/zenodo.6420035</a></p> <p>Second part (12~22) : <a href="http://doi.org/10.5281/zenodo.6467663">https://doi.org/10.5281/zenodo.6467663</a></p> <p>Third part (23~33): <a href="http://doi.org/10.5281/zenodo.6510533">https://doi.org/10.5281/zenodo.6510533</a></p> <p>Fourth part (34~44): <a href="http://doi.org/10.5281/zenodo.6510679">https://doi.org/10.5281/zenodo.6510679</a></p> <p>Fifth part (45~55): <a href="http://doi.org/10.5281/zenodo.6510681">https://doi.org/10.5281/zenodo.6510681</a></p> <p>Sixth part (56~65): <a href="http://doi.org/10.5281/zenodo.6510681">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.6510683">10.5281/zenodo.6510683</a></p>
Detection of Structural Components in Point Clouds of Existing RC Bridges
<p><em>The cost and effort </em><em>of</em><em> modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While these methods work well in synthetic datasets or idealized cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this paper, we propose a novel top-down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges over-segments into individually labelled point clusters. The results of 10 real-world bridge point cloud experiments indicate that our method achieves an average detection precision of 98.8%. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labelled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labelled point clusters.</em></p>
WHUS2-CD+ dataset for sentinel-2 cloud detection validation
<p>WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. Wu, Z. Hu, C. Jian, S. Luo, L. Mou, X. Zhu, and M. Molinier, "A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features," in IEEE Transactions on Geoscience and Remote Sensing, 2021. <a href="https://doi.org/10.1109/TGRS.2021.3069641">https://doi.org/10.1109/TGRS.2021.3069641</a>.</p> <p>[2] Z. Wu, J. Li, Y. Wang, Z. Hu and M. Molinier, "Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1792-1796, Oct. 2020. <a href="https://doi.org/10.1109/LGRS.2019.2955071">https://doi.org/10.1109/LGRS.2019.2955071</a>.</p> <p>The training and testing list is (The challenging senes are marked in bold):</p> <table> <tbody> <tr> <td>Training set</td> </tr> <tr> <td>S2A_MSIL1C_20190714T043711_N0208_R033_T46TFN_20190714T073938</td> </tr> <tr> <td>S2A_MSIL1C_20191219T040151_N0208_R004_T47SQU_20191219T055033</td> </tr> <tr> <td>S2A_MSIL1C_20190630T045701_N0207_R119_T45SWC_20190630T080543</td> </tr> <tr> <td>S2A_MSIL1C_20191215T042151_N0208_R090_T46RGV_20191215T065406</td> </tr> <tr> <td>S2A_MSIL1C_20180930T044701_N0206_R076_T45SXR_20180930T074413</td> </tr> <tr> <td>S2A_MSIL1C_20200317T024541_N0209_R132_T51TWM_20200317T053350</td> </tr> <tr> <td>S2A_MSIL1C_20180816T053641_N0206_R005_T44TKK_20180816T093424</td> </tr> <tr> <td>S2A_MSIL1C_20191023T040821_N0208_R047_T47TQF_20191023T074550</td> </tr> <tr> <td>S2A_MSIL1C_20180824T031541_N0206_R118_T50TKL_20180824T061636</td> </tr> <tr> <td>S2A_MSIL1C_20191118T025011_N0208_R132_T50RMN_20191118T071843</td> </tr> <tr> <td>S2A_MSIL1C_20190916T023551_N0208_R089_T50RQS_20190916T042547</td> </tr> <tr> <td>S2A_MSIL1C_20190819T031541_N0208_R118_T49SFU_20190819T065332</td> </tr> <tr> <td>S2A_MSIL1C_20190815T051651_N0208_R062_T44TPN_20190815T090034</td> </tr> <tr> <td>S2A_MSIL1C_20200410T022551_N0209_R046_T51TXG_20200410T042047</td> </tr> <tr> <td>S2A_MSIL1C_20191002T025551_N0208_R032_T50TQQ_20191002T054113</td> </tr> <tr> <td>S2A_MSIL1C_20180429T032541_N0206_R018_T49SCV_20180429T062304</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T51UWS_20200506T043639</td> </tr> <tr> <td>S2A_MSIL1C_20200325T034531_N0209_R104_T47RQL_20200325T065315</td> </tr> <tr> <td>S2A_MSIL1C_20190928T031541_N0208_R118_T49RBJ_20190928T061248</td> </tr> <tr> <td>S2A_MSIL1C_20180827T032541_N0206_R018_T48RYV_20180827T062627</td> </tr> <tr> <td>S2A_MSIL1C_20200222T030731_N0209_R075_T49QEE_20200222T060244</td> </tr> <tr> <td>S2A_MSIL1C_20180722T030541_N0206_R075_T49RFP_20180722T060550</td> </tr> <tr> <td>S2A_MSIL1C_20180729T025551_N0206_R032_T49RGL_20180729T055945</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T50SPE_20200506T052918</td> </tr> <tr> <td>Testing set</td> </tr> <tr> <td>S2A_MSIL1C_20180930T030541_N0206_R075_T49QDD_20180930T060706</td> </tr> <tr> <td>S2A_MSIL1C_20191105T023901_N0208_R089_T51STR_20191105T054744</td> </tr> <tr> <td>S2A_MSIL1C_20190812T032541_N0208_R018_T48RXU_20190812T070322</td> </tr> <tr> <td>S2A_MSIL1C_20190602T021611_N0207_R003_T52TES_20190602T042019</td> </tr> <tr> <td>S2A_MSIL1C_20190328T033701_N0207_R061_T49TCF_20190328T071457</td> </tr> <tr> <td>S2A_MSIL1C_20191001T050701_N0208_R019_T45TXN_20191002T142939</td> </tr> <tr> <td>S2A_MSIL1C_20200416T042701_N0209_R133_T46SFE_20200416T074050</td> </tr> <tr> <td>S2A_MSIL1C_20200528T050701_N0209_R019_T44SPC_20200528T082127</td> </tr> <tr> <td><strong>S2A_MSIL1C_20210207T023851_N0209_R089_T52UCU_20210207T040210</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210126T052111_N0209_R062_T44SNE_20210126T063836</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210102T054231_N0209_R005_T43SFB_20210102T065941</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20201206T041141_N0209_R047_T47SMV_20201206T053320</strong></td> </tr> </tbody> </table>
Dataset for "Enhancing Cloud Detection in Sentinel-2 Imagery: A Spatial-Temporal Approach and Dataset"
<p>This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).</p> <p>Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.</p> <p><strong>Dataset </strong><strong> </strong><strong>Structure</strong>:</p> <p>Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.</p> <p>For each sample, it includes:</p> <ol> <li>Sample ID;</li> <li>Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);</li> <li>Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;</li> <li>Ordinal list for each timestamp;</li> <li>Sample weight list (reserved);</li> </ol> <p>Here is a demonstration function for parsing the TFRecord file:</p> <pre><code>import tensorflow as tf # init Tensorflow Dataset from file name def parseRecordDirect(fname): sep = '/' parts = tf.strings.split(fname,sep) tn = tf.strings.split(parts[-1],sep='_')[-2] nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64) t = tf.data.Dataset.from_tensors(tn).repeat().take(nn) t1 = tf.data.TFRecordDataset(fname) ds = tf.data.Dataset.zip((t, t1)) return ds keys_to_features_direct = { 'localid': tf.io.FixedLenFeature([], tf.int64, -1), 'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''), 'labels': tf.io.FixedLenFeature((), tf.string, ''), 'dates': tf.io.FixedLenFeature((), tf.string, ''), 'weights': tf.io.FixedLenFeature((), tf.string, '') } # The Decoder (Optional) class SeriesClassificationDirectDecorder(decoder.Decoder): """A tf.Example decoder for tfds classification datasets.""" def __init__(self) -> None: super().__init__() def decode(self, tid, ds): parsed = tf.io.parse_single_example(ds, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) sample_dict = { 'tid': tid, # tile ID 'dates': dates, # Date list 'localid': parsed['localid'], # sample ID 'imgs': decoded, # image array 'labels': label, # label list 'weights': weight } return sample_dict # simple function def preprocessDirect(tid, record): parsed = tf.io.parse_single_example(record, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) return tid, dates, parsed['localid'], decoded, label, weight t1 = parseRecordDirect('filename here') dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE) # </code></pre> <p><strong>Class Definition:</strong></p> <ul> <li>0: clear</li> <li>1: opaque cloud</li> <li>2: thin cloud</li> <li>3: haze</li> <li>4: cloud shadow</li> <li>5: snow</li> </ul> <p><strong>Dataset Construction:</strong></p> <p>First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products. <br>It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution. </p> <p>Then, the time series image patches of two shapes are cropped with each point as the center.<br>The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.<br>And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.</p> <p>The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.</p>
Synergistic use of Far- and Mid-Infrared Spectral radiances for Polar Ice Cloud Detection from Space
<p>Modtran5 was used to generate all .plt radiance files from the tp5 files provided</p> <p>All ERA5 data used in this study is provided in .mat format </p> <p>Codes for plotting the data for figures is provided in "codes" folder </p> <p> </p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data
<p>Supplementary data of 3D-MSNet</p>
raycast: multi-view object detection in UAV image clouds (case-study data)
<p>This package contains image data for conducting multi- and single-view sewer inlet detection in UAV image clouds. The package consists in: - individual UAV images, taken with high overlap and corrected for lens distortion - orthophoto of the case study area, clipped to road boundaries.</p>
Sentinel-2 parallax-based cloud detection - sample dataset
<p>This repository archives the Sentinel-2 sample data used in:</p> <p>D. Frantz, E. Haß, A. Uhl, J. Stoffels, and J. Hill (2018): Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 215, 471-481. <a href="https://doi.org/10.1016/j.rse.2018.04.046">https://doi.org/10.1016/j.rse.2018.04.046</a></p>
A Satellite-Detected Terrestrial Gamma-ray Flash Produced by a Cloud-to-Ground Lightning Leader
<p>The data supports the manuscript entitled “A Satellite-Detected Terrestrial Gamma-ray Flash Produced by a Cloud-to-Ground Lightning Leader” that is submitted to GRL. These files can be opened by MATLAB. The data can be used freely for scientific purposes with appropriate citation.</p>
Onboard Detection of Snow, Ice, Clouds, and Other Processes
The detection of clouds within a satellite image is essential for retrieving surface geophysical parameters from optical and thermal imagery. Even a small percentage of cloud cover within a radiometer pixel can adversely affect the determination of surface variables such as albedo and temperature. Thus, onboard processing of satellite data requires reliable automated cloud detection algorithms that are applicable to a wide range of surface types. Unfortunately cloud-detection, particularly over snow- and ice-covered surfaces, is a problem that plagues the field of remote sensing because of the lack of spectral contrast. This paper discusses preliminary results based on kernel methods for unsupervised discovery of snow, ice, clouds, and other geophysical processes based on data from the MODIS instrument and discusses implementation in computationally constrained environments such as those found on satellites.
Point Cloud Curb Detection Dataset
<p>This dataset contains labeled point cloud data that was captured by a car equipped with a mobile laser scanner.</p> <p>It is part of the ICMLA publication <a href="https://ieeexplore.ieee.org/document/9680221">Large-Scale Curb Extraction Based on 3D Deep Learning and Iterative Refinement Post-Processing</a></p> <p>The dataset is supposed to serve as a benchmark for automated large-scale point cloud curb detection approaches.</p> <p> </p> <p>The dataset is derived from the publicly available KITTI360 dataset published in the paper</p> <p><a href="https://ps.is.tuebingen.mpg.de/publications/xie2016cvpr">Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer</a> by Jun Xie et al.</p> <p><br> LICENSE</p> <p><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)</a> License</p> <p>If you find this dataset useful, please cite the following publication:</p> <pre><code>@INPROCEEDINGS{9680221, author={Schmitz, Jan-Christoph and Bauer, Adrian and Kummert, Anton}, booktitle={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, title={Large-Scale Curb Extraction Based on 3D Deep Learning and Iterative Refinement Post-Processing}, year={2021}, volume={}, number={}, pages={558-563}, doi={10.1109/ICMLA52953.2021.00093}}</code></pre> <p> </p> <p>Supported by the State of North Rhine-Westphalia, Germany</p>
ScienceDex guides
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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.