Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.

Data still being move to IEEE-dataport. 

Instructions: 

Bag files contais multiple topics. Proposed method uses mainly Velodyne lidar pointcloud information and DJI imu

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About

Dataset described in: 

Daudt, R.C., Le Saux, B., Boulch, A. and Gousseau, Y., 2019. Multitask learning for large-scale semantic change detection. Computer Vision and Image Understanding, 187, p.102783.

 

This dataset contains 291 coregistered image pairs of RGB aerial images from IGS's BD ORTHO database. Pixel-level change and land cover annotations are provided, generated by rasterizing Urban Atlas 2006, Urban Atlas 2012, and Urban Atlas Change 2006-2012 maps. 

 

The dataset is split into five parts:

    - 2006 images 

Instructions: 

 

Please contact us if you have any questions.

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8580 Views

Master data has played a significant role in improving operational efficiencies and has attracted the attention of many large businesses over the decade. Recent professional searches have also proved a significant growth in the practice and research of managing these master data assets.

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338 Views

Pressing demand of workload along with social media interaction leads to diminished alertness during work hours. Researchers attempted to measure alertness level from various cues like EEG, EOG, Video-based eye movement analysis, etc. Among these, video-based eyelid and iris motion tracking gained much attention in recent years. However, most of these implementations are tested on video data of subjects without spectacles. These videos do not pose a challenge for eye detection and tracking.

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Four fully annotated marine image datasets. The annotations are given as train and test splits that can be used to evaluate machine learning methods.

Instructions: 

The following classes of fauna were used for annotation:

  • anemone
  • coral
  • crustacean
  • ipnops fish
  • litter
  • ophiuroid
  • other fauna
  • sea cucumber
  • sponge
  • stalked crinoid

For a definition of the classes see [1].

A dataset file contains the following files:

  • annotations/test.csv: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model.
  • annotations/train.csv: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model.
  • images/: Directory that contains all the original image files.
  • dataset.json: JSON file that contains information about the dataset.
    • name: The name of the dataset.
    • images_dir: Name of the directory that contains the original image files.
    • metadata_file: Path to the CSV file that contains image metadata.
    • test_annotations_file: Path to the CSV file that contains the test annotations.
    • train_annotations_file: Path to the CSV file that contains the train annotations.
    • annotation_patches_dir: Name of the directory that should contain the scale- and style-transferred annotation patches.
    • crop_dimension: Edge length of an annotation or style patch in pixels.
  • metadata.csv: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
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CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.

Instructions: 

Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.

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Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.

Instructions: 

=================  Authors  ===========================

Lichao Mou,lichao.mou@dlr.de

Yuansheng Hua, yuansheng.hua@dlr.de

Pu Jin, pu.jin@tum.de

Xiao Xiang Zhu, xiaoxiang.zhu@dlr.de

 

=================  Citation  ===========================

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

@article{eradataset,

  title= {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},

  author= {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},

  journal= {IEEE Geoscience and Remote Sensing Magazine},

  year= {in press}

}

 

==================  Notice!  ===========================

This dataset is ONLY released for academic uses. Please do not further distribute the dataset on other public websites.

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1197 Views

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 6 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 folders.

 

The work associated with this database, which details the registration method, the overall logic behind the creation of this database, resizing factors and the reason why there are unregistered images, is a work on thermal image colorization has been submited to IEEE for consideration, and is currently pre printed and available on arXiv.

We ask that you refer to this work when using this database for your work.

A Novel Registration & Colorization Technique for Thermal to Cross Domain Colorized Images 

 

If you find any problem with the data in this dataset (missing images, wrong names, superfluous python files etc), please let us know and we will try to correct the same.

 

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 folder 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 folder contains just the raw files

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1170 Views

As developers create or analyze an application,they often want to visualize the code through some graphical notation that aids their understanding of the code’s structure or behavior. In order to do this, we develop a integrated debugger.The debugger first record the walkthrough of application as assembly instructions by dynamic way.Then compression mapping block transforms previous outcome into three-dimensional-linked list structure,which then transformed into tree structure by the improved suffix tree algorithm.

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The zizania image dataset consists of a total of 4900 zizanias. The quantity of high quality samples is 2648 and defective quality samples is 2252.

There are four classes in the apple image dataset, which are apples with a diameter greater than 90 mm, between 80 mm and 90 mm, less than 80 mm, and diseases and insect pests. The quantity distributionin above categories are 3647 (51.19%), 2464 (34.59%), 558 (7.83%), 455 (6.39%).

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