This dataset is created for ocean front evolution trend recognition and tracking. 

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Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (PHDIndic_11), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.

Instructions: 

See the attached pdf in documentation for more details about the dataset and benchmark results. Cite the following paper if you use the dataset for research purpose.

Obaidullah, S.M., Halder, C., Santosh, K.C. et al. PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimed Tools Appl 77, 1643–1678 (2018). https://doi.org/10.1007/s11042-017-4373-y

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This multispectral remote sensing image data contained pixels of size (1024 x 1024) for the region around Kolkata city in India and was obtained with LISS-III sensor. There are four spectral bands, i.e., two from visible spectrum (green and red) and two from the infrared spectrum (near-infrared and shortwave infrared). The spatial resolution and spectral variation over the wavelength are 23.5m and 0.52 - 1.7 μm, respectively.

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This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).

Instructions: 

The images in our dataset are in georeferenced TIFF (geotiff) format with 10 bands. We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016). We used the Schroeder conditions to process each patch, producing over 1 million patches with at least one fire pixel and the same amount of patches with no fire pixels, randomly selected from the original images.

The dataset is organized as follow. 

It is divided into South American regions for easy downloading. For each region of South America we have a zip file for images of active fire, its masks, and non-fire images. For example:

 - Uruguay-fire.zip

 - Uruguay-mask.zip

 - Uruguay-nonfire.zip

Within each South American region zip files there are the corresponding zip files to each Landsat-8 WRS (Worldwide Reference System). For example:

- Uruguay-fire.zip;

      - 222083.zip

      - 222084.zip

      - 223082.zip

      - 223083.zip

      - 223084.zip

      - 224082.zip

      - 224083.zip

      - 224084.zip

      - 225081.zip

      - 225082.zip

      - 225083.zip

      - 225084.zip

Within each of these Landsat-8 WRS zip files there are all the corresponding 128x128 image patches for the year 2018. 

 

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1810 Views

This aerial image dataset consists of more than 22,000 independent buildings extracted from aerial images with 0.0075 m spatial resolution and 450 km^2 covering in Christchurch, New Zealand. The most parts of aerial images are down-sampled to 0.3 m ground resolution and cropped into 8,189 non-overlapping tiles with 512* 512. These tiles make up the whole dataset. They are split into three parts: 4,736 tiles for training, 1,036 tiles for validation and 2,416 tiles for testing.

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The PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield (UWF) fundus photography (FP). PRIME-FP20 provides 15 high-resolution UWF FP images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the valid data region for the images. For each UWF FP image, a concurrently captured UWF fluorescein angiography (FA) is also included. 

Instructions: 

UWF FP images, UWF FA images, labeled UWF FP vessel maps, and binary UWF FP validity masks are provided, where the file names indicate the correspondence among them.

 

Users of the dataset should cite the following paper

L. Ding, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning,'' IEEE Trans. Medical Imaging, accepted for publication, to appear.

 

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This dataset contains light-field microscopy images and converted sub-aperture images. 

 

The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.

 

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RECOVERY-FA19 dataset is established for development and evaluation of retinal vessel detection algorithms in fluorescein angiography (FA). RECOVERY-FA19 provides 8 high-resolution ultra-widefield FA images acquired using Optos California P200DTx camera and corresponding labeled binary vessel maps.

Instructions: 

Ultra-widefield fluorescein angiography images and corresponding labeled vessel maps are provided where the file names indicate the correspondence between them.

The vessel ground-truth labeling for the RECOVERY-FA19 dataset was performed using the methodology proposed in: 

L. Ding, M. H. Bawany, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``A novel deep learning pipeline for retinal vessel detection in fluorescein angiography,'' IEEE Trans. Image Proc., vol. 29, no. 1, pp. 6561–6573, 2020. 

Code for evaluating vessel segmentation and replicating results from the above paper can be found in the CodeOcean capsule referenced in the paper. Users of the dataset, should cite the above paper.

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