Find research datasets worth reusing

opencc-bySep 2022View details →

VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation

VegAnn - Vegetation Annotation - dataset, a collection of 3795 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions.

opencc-by-4.0Feb 2023View details →

WE3DS: An RGB-D image dataset for semantic segmentation in agriculture

Here, we introduce a novel RGB-D image database (WE3DS) for semantic segmentation in crop farming. It contains 2,568 RGB-D images (color image and distance map) and hand-annotated ground-truth masks for semantic segmentation and is the first RGB-D image dataset for multi-class plant species semantic segmentation task. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup.   Please cite the original source when using this dataset. Kitzler, F.; Barta, N.; Neugschwandtner, R.W.; Gronauer, A.; Motsch, V. WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. Sensors 2023, 23, 2713. <a href="https://doi.org/10.3390/s23052713">https://doi.org/10.3390/s23052713 </a>

opencc-zeroSep 2021View details →

IODP Expedition 385 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-zeroApr 2023View details →

IODP Expedition 396 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-zeroSep 2016View details →

IODP Expedition 354 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions

Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca. We use the communes administrative division defined by DANE (Departamento Administrativo Nacional de Estadística) under "municipios" in the MGN2021 at  <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a> images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds during the observation period according to QA60 band following the example         given in GEE dataset info page, and took the median of the resulting pixels         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: ESA WorldCover 10m V100         labels mapped to the interval [1,11] according to the following map         { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }         pixel value zero is reserved for invalid data.         see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a>                  see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a>         <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions

Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg We use the communes administrative division which is standardized across Europe by EUROSTAT at: <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a> This is roughly equivalent to the notion municipalities in most countries. From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders are taken from NUTS_RG_01M_2021_3035.shp. images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds during the observation period according to QA60 band following the example         given in GEE dataset info page, and took the median of the resulting pixels         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: ESA WorldCover 10m V100         labels mapped to the interval [1,11] according to the following map         { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }         pixel value zero is reserved for invalid data.         see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a>                  see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions

Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca. We use the communes administrative division defined by DANE (Departamento Administrativo Nacional de Estadística) under "municipios" in the MGN2021 at  <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a> images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds during the observation period according to QA60 band following the example         given in GEE dataset info page, and took the median of the resulting pixels         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: Global Human Settlement Layers, Population Grid 2015         labels range from 0 to 31, with the following meaning:            label value     original value in GEE dataset            0               0            1               1-10            2               11-20            3               21-30            ...            31              >=291          see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions

Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg We use the communes adminitrative division which is standardized across Europe by EUROSTAT at: <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a> This is roughly equivalent to the notion municipalities in most countries. From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders are taken from NUTS_RG_01M_2021_3035.shp. images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds acoording to QA60 band following the example         given in GEE dataset info page at:         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: Global Human Settlement Layers, Population Grid 2015         labels range from 0 to 31, with the following meaning:            label value     original value in GEE dataset            0               0            1               1-10            2               11-20            3               21-30            ...            31              >=291          see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a>   <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

opencc-zeroMay 2019View details →

IODP Expedition 369 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Jan 2022View details →

Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Zoo

# Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Gym ## Overview * Test datasets and files for testing the [segmentation gym](https://github.com/Doodleverse/segmentation_gym) program for image segmentation * Data set made by Daniel Buscombe, Marda Science LLC. This is version 5.0 * Dataset consists of a time-series of Landsat-8 images of Cape Hatteras National Seashore, courtesy of the U.S. Geological Survey. * Imagery spans the period February 2015 to September 2021. * Labels were created by Daniel Buscombe, Marda Science, using the labeling program [Doodler](https://github.com/Doodleverse/dash_doodler). Download this file and unzip to somewhere on your machine (although *not* inside the `segmentation_gym` folder), then see the relevant page on the [segmentation gym wiki](https://github.com/Doodleverse/segmentation_gym/wiki) for further explanation. This dataset and associated models were made by Dr Daniel Buscombe, Marda Science LLC, for the purposes of demonstrating the functionality of Segmentation Gym. The labels were created using [Doodler](https://github.com/Doodleverse/dash_doodler/). Previous versions: 1.0. https://zenodo.org/record/5895128#.Y1G5s3bMIuU original release, Oct 2021, conforming to Segmentation Gym functionality on Oct 2021 2.0 https://zenodo.org/record/7036025#.Y1G57XbMIuU, Jan 23 2022, conforming to Segmentation Gym functionality on Jan 23 2022 This is version 5.0, created 7/20/23, and has been tested with Segmentation Gym using doodleverse-utils 0.0.33 https://pypi.org/project/doodleverse-utils/0.0.33/   ## file structure ```{sh} /Users/Someone/my_segmentation_zoo_datasets                     │   ├── config                     │   |    └── *.json                     │   ├── capehatteras_data                     |   |   ├── fromDoodler                     |   |   |     ├──images                     │   |   |     └──labels                     |   |   ├──npzForModel                     │   |   └──toPredict                     │   └── modelOut                     │       └── *.png                     │   └── weights                     │       └── *.h5 ``` ## config There are 4 config files: 1. `/config/hatteras_l8_resunet.json` 2. `/config/hatteras_l8_vanilla_unet.json` 3. `/config/hatteras_l8_resunet_model2.json` 4. `/config/hatteras_l8_segformer.json`     The first two are for res-unet and unet models respectively. The third one differs from the first only with specification of kernel size. It is provided as an example of how to conduct model training experiments, modifying one hyperparameter at a time in the effort to create an optimal model. The last one is based on the new Segformer model architecture. They all contain the same essential information and differ as indicated below ``` {   "TARGET_SIZE": [768,768], # the size of the imagery you wish the model to train on. This may not be the original size   "MODEL": "resunet", # model name. Otherwise, "unet" or "segformer"   "NCLASSES": 4, # number of classes   "KERNEL":9, # horizontal size of convolution kernel in pixels   "STRIDE":2, # stride in convolution kernel   "BATCH_SIZE": 7, # number of images/labels per batch   "FILTERS":6, # number of filters   "N_DATA_BANDS": 3, # number of image bands   "DROPOUT":0.1, # amount of dropout   "DROPOUT_CHANGE_PER_LAYER":0.0, # change in dropout per layer   "DROPOUT_TYPE":"standard", # type of dropout. Otherwise "spatial"   "USE_DROPOUT_ON_UPSAMPLING":false, # if true, dropout is used on upsampling as well as downsampling   "DO_TRAIN": false, # if false, the model will not train, but you will select this config file, data directory, and the program will load the model weights and test the model on the validation subset   if true, the model will train from scratch (warning! this will overwrite the existing weights file in h5 format)   "LOSS":"dice", # model training loss function, otherwise "cat" for categorical cross-entropy   "PATIENCE": 10, # number of epochs of no model improvement before training is aborted   "MAX_EPOCHS": 100, # maximum number of training epochs   "VALIDATION_SPLIT": 0.6, #proportion to use for validation   "RAMPUP_EPOCHS": 20, # [LR-scheduler] rampup to maximim   "SUSTAIN_EPOCHS": 0.0, # [LR-scheduler] sustain at maximum   "EXP_DECAY": 0.9, # [LR-scheduler] decay rate   "START_LR":  1e-7, # [LR-scheduler] start lr   "MIN_LR": 1e-7, # [LR-scheduler] min lr   "MAX_LR": 1e-4, # [LR-scheduler] max lr   "FILTER_VALUE": 0, #if >0, the size of a median filter to apply on outputs (not recommended unless you have noisy outputs)   "DOPLOT": true, #make plots   "ROOT_STRING": "hatteras_l8_aug_768", #data file (npz) prefix string   "USEMASK": false, # use the convention 'mask' in label image file names, instead of the preferred 'label'   "AUG_ROT": 5, # [augmentation] amount of rotation in degrees   "AUG_ZOOM": 0.05, # [augmentation] amount of zoom as a proportion   "AUG_WIDTHSHIFT": 0.05, # [augmentation] amount of random width shift as a proportion   "AUG_HEIGHTSHIFT": 0.05,# [augmentation] amount of random width shift as a proportion   "AUG_HFLIP": true, #  [augmentation] if true, randomly apply horizontal flips   "AUG_VFLIP": false, #  [augmentation] if true, randomly apply vertical flips   "AUG_LOOPS": 10, #[augmentation] number of portions to split the data into (recommended > 2 to save memory)   "AUG_COPIES": 5  #[augmentation] number iof augmented copies to make   "SET_GPU": "0" #which GPU to use. If multiple, list separated by a comma, e.g. '0,1,2'. If CPU is requested, use "-1"   "WRITE_MODELMETADATA": false, #if true, the prompts `seg_images_in_folder.py` to write detailed metadata for each sample file   "LOSS_WEIGHTS": false, #if true, apply per-class weights to loss function   "SET_PCI_BUS_ID": true, #if true, make keras aware of the PCI BUS ID (advanced or nonstandard GPU usage)   "TESTTIMEAUG": true, #if true, apply test-time augmentation when model in inference mode   "WRITE_MODELMETADATA": true,# if true, write model metadata per image when model in inference mode   "OTSU_THRESHOLD": true# if true, and NCLASSES=2 only, use per-image Otsu threshold rather than decision boundary of 0.5 on softmax scores } ``` ## capehatteras_data Folder containing all the model input data ```{sh}                     │   ├── capehatteras_data: folder containing all the model input data                     |   |   ├── fromDoodler: folder containing images and labels exported from Doodler using [this program](https://github.com/dbuscombe-usgs/dash_doodler/blob/main/utils/gen_images_and_labels_4_zoo.py)                     |   |   |     ├──images: jpg format files, one per label image                     │   |   |     └──labels: jpg format files, one per image                     |   |   ├──npz4gym npz format files for model training using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/train_model.py) that have been created following the workflow [documented here](https://github.com/dbuscombe-usgs/segmentation_zoo/wiki/Create-a-model-ready-dataset) using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/make_nd_dataset.py)                     │   |   └──toPredict: a folder of images to test model prediction using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/seg_images_in_folder.py) ``` ## modelOut PNG format files containing example model outputs from the train ('_train_' in filename) and validation ('_val_' in filename) subsets as well as an image showing training loss and accuracy curves with `trainhist` in the filename. There are two sets of these files, those associated with the residual unet trained with dice loss contain `resunet` in their name, and those from the UNet are named with `vanilla_unet`. ## weights There are model weights files associated with each config files.

opencc-zeroMay 2021View details →

IODP Expedition 382 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-zeroAug 2023View details →

IODP Expedition 392 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Jul 2023View details →

ToF-RGB Fusion Dataset

This dataset contains depth maps recorded by an ESPROS epc635 Time-of-Flight camera and RGB images from a Raspberry Pi camera module V2, for use in the fusion of these sensors to increase the resolution and framerate of the ToF camera. The dataset contains three scenes, one of a paper dodecahedron and two of a wooden grid with holes of various sizes, with between 86 and 230 consecutive video frames. In each scene, the camera moves toward the object. Ground truth depth maps were created in Blender and are provided alongside the recorded images. The framerate of the RGB camera is 24 fps. The ToF images are captured at 12 fps but show large fluctuations and are not synchronized with the RGB camera. The content of the included files are: <ul> <li>ToF Dataset: Recorded HDR ToF camera depth maps (160x60), ground truth depth maps (2560x960), and recorded RGB images (2560x960). The depth maps contain the depth values in millimeters, stored as 16-bit PNG files. RGB and ground truth depth maps are named continuously by their frame number. The ToF depth maps are named with a frame number, based on the closest RGB image in the time domain.</li> <li>Additional Data: All data used in the creation of the ToF dataset (All recorded depth maps and color images, calibration images of a 4x5 checkerboard calibration pattern with a square size of 43mm, calibration parameters, Blender files)</li> </ul>

dryad40/100

Data from: Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut

Open the record for dataset details and reuse information.

publicMay 2025View details →

opencc-by-4.0Dec 2019View details →

KFuji RGB-DS dataset

The KFuji RGB-DS dataset is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The reader is referred to visit articles [1] and [2] for a description of methodology and further information about this dataset: [1] Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 2019. Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities. Computers and Electronics in Agriculture, 162, 689-698. DOI: 10.1016/j.compag.2019.05.016 [2] Gené-Mola J, Vilaplana V, Rosell-Polo JR, Morros JR, Ruiz-Hidalgo J, Gregorio E. 2019. KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data. Data in brief, 25 (2019), 104289. DOI: 10.1016/j.dib.2019.104289

opencc-by-4.0Dec 2015View details →

RGB-W Dataset

Abstract Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional quasi-free modality, namely, the wireless signal emitted by individuals' cell phones, referred to as RGB-W. The received signal strength acts as a rough proxy for depth and a reliable cue on a person's identity. Although the measured signals are noisy, we demonstrate that the combination of visual and wireless data significantly improves the localization accuracy. We introduce a novel image-driven representation of wireless data which embeds all received signals onto a single image. We then evaluate the ability of this additional data to (i) locate persons within a sparsity-driven framework and to (ii) track individuals with a new confidence measure on the data association problem. Our solution outperforms existing localization methods. It can be applied to the millions of currently installed RGB cameras to better analyze human behavior and offer the next generation of high-accuracy location-based services. Conference Paper PDF: <a href="http://Conference Paper PDF: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf">http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf</a> Metadata <pre><code>+----------------+-----------------+-----------+-----------+--------------+----------+ | Sequence Name | Length (mm:ss) | # Frames | # People | # W Devices | Download | +----------------+-----------------+-----------+-----------+--------------+----------+ | conference-1 | 01:53 | 1,697 | 5 | 5 | 116 MiB | | conference-2 | 05:18 | 4,782 | 12 | 12 | 379 MiB | | conference-3 | 23:31 | 21,165 | 1 | 2 | 1.3 GiB | | conference-4 | 06:27 | 4,832 | 1 | 2 | 357 MiB | | conference-5 | 06:03 | 4,525 | 2 | 2 | 290 MiB | | patio-1 | 07:22 | 6,636 | 4 | 4 | 474 MiB | | patio-2 | 04:36 | 4,144 | 2 | 2 | 258 MiB | | Full Dataset | 55:10 | 47,781 | -- | -- | 3.2 GiB | +----------------+-----------------+-----------+-----------+--------------+----------+</code></pre> Citation If you would like to cite our work, please use the following. Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision (ICCV). Santiago, Chile. IEEE. <pre>@inproceedings{alahi2015rgb, title={RGB-W: When vision meets wireless}, author={Alahi, Alexandre and Haque, Albert and Fei-Fei, Li}, booktitle={International Conference on Computer Vision}, year={2015} }</pre>

opencc-by-4.0Jan 2021View details →

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Basic data from publication <a href="https://doi.org/10.3390/rs13010147">https://doi.org/10.3390/rs13010147</a> All_TDRdata.csv contains the data from 48 TDR sensors (30 cm) installed in the three rainout shelters. <ul> <li>Sensors 1 - 18 were installed vertically to obtain soil moisture content averaged over the 10 - 40 cm profile, on 6 locations per shelter</li> <li>Sensors 19-21 were installed diagonally to obtain soil moisture content averaged over the 20 - 40 cm profile on one location per shelter</li> <li>Sensors 22-24 were installed diagonally to obtain soil moisture content averaged over the 40 - 60 cm profile on one location per shelter</li> <li>Sensors 25-27 were installed horizontally to obtain soil moisture content at 10 cm depth on one location per shelter</li> <li>Sensors 28-30 were installed horizontally to obtain soil moisture content at 20 cm depth on one location per shelter</li> <li>Sensors 31-33 were installed horizontally to obtain soil moisture content at 30 cm depth on one location per shelter</li> <li>Sensors 34-36 were installed horizontally to obtain soil moisture content at 40 cm depth on one location per shelter</li> <li>Sensors 37-39 were installed horizontally to obtain soil moisture content at 50 cm depth on one location per shelter</li> <li>Sensors 40-42 were installed horizontally to obtain soil moisture content at 60 cm depth on one location per shelter</li> <li>Sensors 43-45 were installed horizontally to obtain soil moisture content at 70 cm depth on one location per shelter</li> <li>Sensors 46-48 were installed horizontally to obtain soil moisture content at 80 cm depth on one location per shelter</li> </ul> Climate.txt contains the daily averaged microclimatic data PhenotypingData.csv contains the phenotypic data from the UAV flights and the breeder scores

opencc-by-4.0Dec 2020View details →

MINDS-Libras Dataset (RGB-D sensor data)

Brazilian Sign Language (Libras) data set with 20 signs for sign language and gesture recognition benchmark: - Acontecer (To happen) - Aluno (Student) - Amarelo (Yellow) - América (America) - Aproveitar (To enjoy) - Bala (Candy) - Banco (Bank) - Banheiro (Bathroom) - Barulho (Noise) - Cinco (Five) - Conhecer (To know) - Espelho (Mirror) - Esquina (Corner) - Filho (Son) - Maçã (Apple) - Medo (Fear) - Ruim (Bad) - Sapo (Frog) - Vacina (Vaccine) - Vontade (Will) Each one of the signs was recorded 5 times by 12 signers, using a Chroma Key background. Among the signers are men and women with basic to advanced knowledge in Libras.  The RGB-D sensor (kinect v2) available the RGB videos (1920 x 1080) and depth videos (640 x 480) in "mp4" format, and the body points and face data in "txt" file. <ul> <li>The body file has seven different information (Position X, Y and Z; Orientation X, Y and Z; TrackingState; LeftHandState; RightHandState; ColorPosition X and Y; and DepthPosition X and Y) about the 25 points: (1) Spine Base, (2) Spine Mid, (3) Neck, (4) Head, (5) Shoulder Left, (6) Elbow Left, (7) Wrist Left, (8) Hand Left, (9) Shoulder Right, (10) Elbow Right, (11) Wrist Right, (12) Hand Right, (13) Hip Left, (14) Knee Left, (15) Ankle Left, (16) Foot Left, (17) Hip Right, (18) Knee Right, (19) Ankle Right, (20) Foot Right, (21) Spine Shoulder, (22) Hand Tip Left, (23) Thumb Left, (24) Hand Tip Right and (25) Thumb Right. There are 13 lines (or data) for each frame. This order is repeated sequentially up to 1950 lines (13 lines $\times$ 150 frames), representing the sign video.</li> </ul>   <ul> <li>Regarding to the face data, the same organisation was adopted. In this case, we have seven information (FaceBox, FaceRotation, HeadPivot, AnimationUnit, FaceModel X, Y and Z; ColorFaceModel X and Y; and DepthFaceModel X and Y), describing 11 data, distributed in 1650 (11 lines $\times$ 150 frames) lines in the ``.txt'' file.</li> </ul> (Former name: Libras-20)