The region-based segmentation approach has been a major research area for many medical image applications. A vision guided autonomous system has used region-based segmentation information to operate heavy machinery and locomotive machines intended for computer vision applications. The dataset contains raw images in .png format fro brain tumor in various portions of brain.The dataset can be used fro training and testing. Images are calssified into three main regions as frontal lobe(level -1, level-2), optus-lobe(level-1), medula_lobe(level-1,level-2,level-3).


<p>This is the image dataset for satellite image processing&nbsp; which is a collection therml infrared and multispectral images .</p>


Dataset images
Thermal infrared images and multispectral images
image size:512x512
file :.h5


In the field of 3D reconstruction, although there exist some standard datasets for evaluating the segmentation results of close-up 3D models, these datasets cannot be used to evaluate the segmentation results of 3D models based on satellite images. To address this issue, we provide a standard dataset for evaluating the segmentation results of satellite images and their corresponding DSMs. In this dataset, the satellite images maintain an exact correspondence with the DSMs, thus the segmentation results of both satellite images and DSMs can be evaluated by our proposed dataset.


DIDA is a new image-based historical handwritten digit dataset and collected from the Swedish historical handwritten document images between the year 1800 and 1940. It is the largest historical handwritten digit dataset which is introduced to the Optical Character Recognition (OCR) community to help the researchers to test their optical handwritten character recognition methods. To generate DIDA, 250,000 single digits and 200,000 multi-digits are cropped from 75,000 different document images. 


The accompanying dataset for the CVSports 2021 paper: DeepDarts: Modeling Keypoints as Objects for Automatic Scoring in Darts using a Single Camera

Paper Abstract:


The recommended way to load the labels is to use the pandas Python package:

import pandas as pd

labels = pd.read_pickle("labels.pkl")

See github repository for more information:


We present a 4D Light Field (LF) video dataset collected by the camera matrix, to be used for designing and testing algorithms and systems for LF video coding and processing. For collecting these videos, a 10x10 LF capture matrix composed of 100 cameras is designed and implemented, and the resolution of each camera is 1920x1056. The videos are taken in real and varying illumination conditions. The dataset contains a total of nine groups of LF videos, of which eight groups are collected with a fixed camera matrix position and a fixed orientation.


The stream data of all LF videos are stored under the directory LF_video_data, and each video contains 100 stream files. The stream files are in 1920 x 1056 24 frames per second MP4 format without audio. There are 100 mat files in the Calibration_mat directory, which store all camera parameters of 100 lenses within the matrix. The depth estimation results are stored under the directory Depth_evaluation_result, which is for reference only.


The Jackal UGV, from Clearpath Robotics, was used as the data collecting platform. This skid-steer four-wheel-drive vehicle comes with an onboard IMU, two DC motors with encoders that measure wheel angular speeds, and current sensors that measure motor current outputs. On each side of the robot, the front wheel and back wheel are jointed with a gearbox and so spin together at the same rate and direction. The IMU provided vehicle attitude measurements in terms of Euler angles, as well as linear acceleration and angular rate of the vehicle body in three Euclidean axes.


Each HDF5 file contains four types of data entries: timestamps, signals, images, and labels. A .ipynb code example is included to demonstrate how to retrieve and format data appropriately. 

The .ipynb script requires h5py, numpy, and matplotlib libraries.

** If you plan to load the entire dataset into your memory, make sure your PC has >16 Gb RAM


The dermoscopic images considered in the paper "Dermoscopic Image Classification with Neural Style Transfer" are available for public download through the ISIC database (!/topWithHeader/wideContentTop/main). These are 24-bit JPEG images with a typical resolution of 768 × 512 pixels. However, not all the images in the database are in satisfactory condition.


[NEW] Urb3DCD V2 is now avalaible!


Urban Point Clouds simulator

We have developed a simulator to generate time series of point clouds (PCs) for urban datasets. Given a 3D model of a city, the simulator allows us to introduce random changes in the model and generates a synthetic aerial LiDAR Survey (ALS) above the city. The 3D model is in practice issued from a real city, e.g. with a Level of Detail 2 (LoD2) precision. From this model, we extract each existing building as well as the ground. By adding or removing buildings in the model, we can simulate construction or demolition of buildings. Notice that depending on area the ground is not necessarily flat. The simulator allows us to obtain as many 3D PCs over urban changed areas as needed. It could be useful especially for deep learning supervised approaches that require lots of training dates. Moreover, the created PCs are all directly annotated by the simulator according to the changes, thus no time-consuming manual annotation needs to be done with this process.

For each obtained model, the ALS simulation is performed thanks to a flight plan and ray tracing with the Visualisation ToolKit (VTK) python library.  Space between flight lines is computed in accordance to predefined parameters such as resolution, covering between swaths and scanning angle. Following this computation, a flight plan is set with a random starting position and direction of flight in order to introduce more variability between two acquisitions. Moreover, Gaussian noise can be added to simulate errors and lack of precision in LiDAR range measuring and scan direction.

Dataset Description

To conduct fair qualitative and quantitative evaluation of PC change detection techniques, we have build some datasets based on LoD2 models of the first and second districts of Lyon  (, France. For each simulation, buildings have been added or removed to introduce changes in the model and to generate a large number of pairs of PCs. We also consider various initial states across simulations, and randomly update the set of buildings from the first date through random addition or deletion of  buildings to create the second landscape. In addition, flight starting position and direction are always set randomly. As a consequence, the acquisition patterns will not be the same between generated PCs, thus each acquisition may not have exactly the same visible or hidden parts.

From terrestrial LiDAR surveying to photogrammetric acquisition by satellite images, there exist many different types of sensors and acquisition pipelines to obtain 3D point clouds for urban areas, resulting in PCs with different characteristics. {By providing different acquisition parameters to our simulator}, our goal was to provide a variety of sub-datasets with heterogeneous qualities to reproduce the real variability of LiDAR sensors or to mimic datasets coming from a photogrammetric pipeline with satellite images (by using a tight scan angle with high noise). Thus, we generated the following sub-datasets:

  • ALS with low resolution, low noise for both dates
  • ALS with high resolution, low noise for both dates
  • ALS with low resolution, high noise for both dates
  • ALS with low resolution, high noise, tight scan angle (mimicking photogrammetric acquisition from satellite images) for both dates
  • Multi-sensor data, with low resolution, high noise at date 1, and high resolution, low~noise at date 2

Notice that sub-datasets 3 and 4 are quite similar but the latter provides less visible facades, thanks to the smaller scanning angle and overlapping percentage.

Finally, for the first configuration (ALS low resolution, low noise), we provided the 3~following different training sets:

  • Small training set: 1 simulation
  • Normal training set: 10 simulations
  • Large training set: 50 simulations

More details about the configuration of acquisition are provided in the documentation file and in the publication Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets, de Gélis et al. (2021)

Technical details

All PCs are available at PLY format. Each train, val, test folder contains sub-folders containing pairs of PCs : pointCloud0.ply and pointCloud1.ply for both first and second dates.

Each ply file contain the coordinates X Y Z of each points and the label:

  • 0 for unchanged points
  • 1 for points on a new building
  • 2 for for points on a destruction.

The label is given in a scalar field named label_ch. Notice that the first PC (pointCloud0.ply) has a label field even if it is set at 0 for every points because change are set in comparison to previous date.


If you use this dataset for your work, please use the following citation:

@article{degelis2021change,  title={Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets},  author={{de G\'elis}, I. and Lef\`evre, S. and Corpetti, T. },  journal={Remote Sensing},  volume={13},  pages={2629},  year={2021},  publisher={Multidisciplinary Digital Publishing Institute}}

For more details :


[NEW] Urb3DCD V2

A second version of this dataset is now available. We enhanced realism of our city models by adding vegeation and mobile objects (car and trucks).

Now the change annotation contains seven classes: unchanged, new building, demolition, new vegetation, vegetation growth, vegetation loss and mobile objetcs.

A mono-date semantic labelisation of each point clouds is now also avalaible.

We propose two sub-datasets in this second version:

  • ALS with low resolution, low noise for both dates
  • Multi-sensor data, with low resolution, high noise at date 1, and high resolution, low noise at date 2

Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans.



“Dataset-S1” contains two folders for COVID-19 and Normal DICOM images, named as “COVID-S1” and “Normal-S1”, respectively. Within the same folder, three CSV files are available. The first one, named as “Radiologist-S1.csv”, contains labels assigned to the corresponding cases by three experienced radiologists. The second CSV file, “Clinical-S1.csv”, includes the clinical information as well as the result of the RT-PCR test, if available. The third file is named “LDCT-SL-Labels-S1.csv” and contains the slice-level labels related to COVID-19 cases. In other words, slices demonstrating infection are specified in this file.

Each row in this CSV file corresponds to a specific case, and each column represents the slice number in the volumetric CT scan. Label 1 indicates a slice with the evidence of infection, while 0 is assigned to slices with no evidence of infection.

Note that slices in each case should be sorted based on the “Slice-Location” value to match with the provided labels in the CSV file. The Slice Location values are stored in DICOM files and accessible from the following DICOM tag: (0020,1041) – DS – Slice Location

 “Dataset-S2” contains 100 COVID-19 positive cases, confirmed with RT-PCR test. 68 cases have related imaging findings, whereas 32 do not reveal signs of infection. These two groups are placed in two folders of “PCP-Lung-Positive “and “PCP-Lung-Negative”. “Dataset-S2” also includes a CSV file, namely “Clinical-S2.csv” presenting the clinical information.