These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover.

Instructions: 

The dataset is composed of 336 sequences corresponding to areas in West and South-East Africa, Middle-East, and Australia. Each time series is located in a given folder named with the sequence ID (0001... 0336).

Two json files, S1list.json and S2list.json are provided to describe respectively the Sentinel-1 and Sentinel-2 images.The keys are the total number of images in the sequence, the folder name, the geography of the observed area, and the description of each image in the series. The SAR images description contains also the URLs to download the images.Each image is described by its acquisition date, its label (FLOODING: boolean), a boolean (FULL-DATA-COVERAGE: boolean) indicating if the area is fully or partially imaged, and the file prefix. For SAR images the orbit (ASCENDING or DESCENDING) is also indicated.

The Sentinel-2 images were obtained from the Mediaeval 2019 Multimedia Satellite Task [1] and are provided with Level 2A atmospheric correction. For one acquisition, there are 12 single-channel raster images provided corresponding to the different spectral bands.

The Sentinel-1 images were added to the dataset. The images are provided with radiometric calibration and range doppler terrain correction based on the SRTM digital elevation model. For one acquisition, two raster images are available corresponding to the polarimetry channels VV and VH.

The original dataset was split into 267 sequences for the train and 67 sequences for the test. Here all sequences are in the same folder.

 

To use this dataset please cite the following papers:

Flood Detection in Time Series of Optical and SAR Images, C. Rambour,N. Audebert,E. Koeniguer,B. Le Saux,  and M. Datcu, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 1343--1346

The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop

 

This dataset contains modified Copernicus Sentinel data [2018-2019], processed by ESA.

[1] The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop

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Depths to the various subsurface anomalies have been the primary interest in all the applications of magnetic methods of geophysical prospection. Depths to the subsurface geologic features of interest are more valuable and superior to all other properties in any correct subsurface geologic structural interpretations.

Instructions: 

The Neural Network Pattern Recognition help to select the appropriate data sets, create and train the network, and evaluate its performance using the cross-entropy and convolution matrices in MATLAB with fusion python. The Neural Network utilizes a Two-Layer feed-forward network to solve the pattern recognition problem with a six inputs data (i.e., the SI values), a Hidden Layer and a SoftMax Output Layer Neurons. The method excellently classified vector attributes when sufficient neuron in the hidden layer is selected. In this study, a six inputs data from the various SI values obtained was used in one hundred, (100) hidden layers of the neutrons, and weights combined with a six layers output of neutrons, and weights to generate the six-final output that represent each of the SI values depths as shown.

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This is a collection of paired thermal and visible ear images. Images in this dataset were acquired in different illumination conditions ranging between 2 and 10700 lux. There are total 2200 images of which 1100 are thermal images while the other 1100 are their corresponding visible images. Images consisted of left and right ear images of 55 subjects. Images were capture in 5 illumination conditiond for every subjects. This dataset was developed for illumination invariant ear recognition study. In addition it can also be useful for thermal and visible image fusion research.

 

Instructions: 

Any work made public, in whatever form, based directly or indirectly on any part of the DATABASE will include the following reference: 

Syed Zainal Ariffin, S. M. Z., Jamil, N., & Megat Abdul Rahman, P. N. (2016). DIAST Variability Illuminated Thermal and Visible Ear Images Datasets. In Proceeding of 2016 Signal Processing : Algorithms, Architectures, Arrangements, and Applications (SPA), 2016. DOI : 10.1109/SPA.2016.7763611

 

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This is a dataset having paired thermal-visual images collected over 1.5 years from different locations in Chitrakoot, India and Prayagraj, India. The images can be broadly classified into greenery, urban, historical buildings and crowd data.

The crowd data was collected from the Maha Kumbh Mela 2019, Prayagraj, which is the largest religious fair in the world and is held every 12 years.

Instructions: 

The images are classified according to the thermal imager they were used to capture them with.

The SONEL thermal images are inside register_sonel.

The FLIR images are in register_flir and register_flir_old. There are 2 image zip files because FLIR thermal imagers reuse the image names after a certain limit.

The unregistered images are kept as files inside each base zip as unreg.

 

The naming classification is as follows:

·         FLIR

o   Registered images are named as <name>.jpg and <name>_color.png with the png file being the optical registered image

o   The raw files are named as FLIR<#number>.jpg and FLIR<#number+1>.jpg where the initial file is the thermal image

o   The unreg_flir zip contains just the raw files

·         SONEL

o   Registered images are named as <name>.jpg and <name>_color.png with the png file being the optical registered image

o   The raw files are named as IRI_<name>.jpg and VIS_< name >.jpg where the IRI file is the thermal image and VIS is the visual image

o   The unreg zip contains just the raw files

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The 2020 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) and the Technical University of Munich, aims to promote research in large-scale land cover mapping based on weakly supervised learning from globally available multimodal satellite data. The task is to train a machine learning model for global land cover mapping based on weakly annotated samples.

Last Updated On: 
Thu, 05/28/2020 - 11:57

The dataset contains high-resolution microscopy images and confocal spectra of semiconducting single-wall carbon nanotubes. Carbon nanotubes allow down-scaling of electronic components to the nano-scale. There is initial evidence from Monte Carlo simulations that microscopy images with high digital resolution show energy information in the Bessel wave pattern that is visible in these images. In this dataset, images from Silicon and InGaAs cameras, as well as spectra, give valuable insights into the spectroscopic properties of these single-photon emitters.

Instructions: 

The dataset is generated with docker containers from the measurement data. The measured data is in Igor Binary Waves. The specific format can be read with a custom reader an processed with various tools.

Processing will be applied automatically to various output formats using docker containers.

 

See current development status and dataset description will be updated on

https://gitlab.com/ukos-git/nanotubes

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Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.

Instructions: 

The MEx Multi-modal Exercise dataset contains data of 7 different physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers, a pressure mat and a depth camera.

Application

The dataset can be used for exercise recognition, exercise quality assessment and exercise counting, by developing algorithms for pre-processing, feature extraction, multi-modal sensor fusion, segmentation and classification.

 

Data collection method

Each subject was given a sheet of 7 exercises with instructions to perform the exercise at the beginning of the session. At the beginning of each exercise the researcher demonstrated the exercise to the subject, then the subject performed the exercise for maximum 60 seconds while being recorded with four sensors. During the recording, the researcher did not give any advice or kept count or time to enforce a rhythm.

 

Sensors

Obbrec Astra Depth Camera 

-       sampling frequency – 15Hz 

-       frame size – 240x320

 

Sensing Tex Pressure Mat

-       sampling frequency – 15Hz

-       frame size – 32*16

Axivity AX3 3-Axis Logging Accelerometer

-       sampling frequency – 100Hz

-       range – 8g

 

Sensor Placement

All the exercises were performed lying down on the mat while the subject wearing two accelerometers on the wrist and the thigh. The depth camera was placed above the subject facing down-words recording an aerial view. Top of the depth camera frame was aligned with the top of the pressure mat frame and the subject’s shoulders such that the face will not be included in the depth camera video.

 

Data folder

MEx folder has four folders, one for each sensor. Inside each sensor folder,

30 folders can be found, one for each subject. In each subject folder, 8 files can be found for each exercise with 2 files for exercise 4 as it is performed on two sides. (The user 22 will only have 7 files as they performed the exercise 4 on only one side.)  One line in the data files correspond to one timestamped and sensory data.

 

Attribute Information

 

The 4 columns in the act and acw files is organized as follows:

1 – timestamp

2 – x value

3 – y value

4 – z value

Min value = -8

Max value = +8

 

The 513 columns in the pm file is organized as follows:

1 - timestamp

2-513 – pressure mat data frame (32x16)

Min value – 0

Max value – 1

 

The 193 columns in the dc file is organized as follows:

1 - timestamp

2-193 – depth camera data frame (12x16)

 

dc data frame is scaled down from 240x320 to 12x16 using the OpenCV resize algorithm

Min value – 0

Max value – 1

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3D-videos database.

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Changes in left ventricular (LV) aggregate cardiomyocyte  orientation and deformation underlie cardiac function and dysfunction. As such, in vivo aggregate cardiomyocyte "myofiber" strain has mechanistic significance, but currently there exists no established technique to measure in vivo cardiomyocyte strain.

 

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