The dataset includes 458 hi-res images together with their alpha maps (BW) indicating the crack presence. The ground truth for semantic segmentation has two classes to conduct binary pixelwise classification. The photos are captured in various buildings located in Middle East Technical University.

You can access a larger dataset containing images with 227x227 px dimensions for classification which are produced from this dataset from http://dx.doi.org/10.17632/5y9wdsg2zt.1 .

Discover the Remote Sensing Object Segmentation Dataset Perfect for GIS, AI driven environmental studies, and satellite image analysis.

The visuAAL Skin Segmentation Dataset contains 46,775 high quality images divided into a training set with 45,623 images, and a validation set with 1,152 images. Skin areas have been obtained automatically from the FashionPedia garment dataset. The process to extract the skin areas is explained in detail in the paper 'From Garment to Skin: The visuAAL Skin Segmentation Dataset'.

If you use the visuAAL Skin Segmentation Dataset, please, cite:

• https://doi.org/10.5281/zenodo.6973396
• https://doi.org/10.1007/978-3-031-13321-3_6

How to use:

1. Download the FashionPedia dataset from https://fashionpedia.github.io/home/Fashionpedia_download.html
2. Download the visuAAL Skin Segmentation Dataset. The dataset consists of two folders, namely train_masks and val_masks. Each folder corresponds to the training and validation sets in the original FashionPedia dataset.
3. After extracting the images from FashionPedia, for each image existing in the visuAAL skin segmentation dataset, the original image can be found with the same name (file_name in the annotations file).

A sample of image data in the FashionPedia dataset is:

{'id': 12305,

'width': 680,

'height': 1024,

'file_name': '064c8022b32931e787260d81ed5aafe8.jpg',

'license': 4,

'time_captured': 'March-August, 2018',

'original_url': 'https://farm2.staticflickr.com/1936/8607950470_9d9d76ced7_o.jpg',

'isstatic': 1,

'kaggle_id': '064c8022b32931e787260d81ed5aafe8'}

NOTE: Not all the images in the FashionPedia dataset have the correponding skin mask in the visuAAL Skin Segmentation Dataset, as there are images in which only garment parts and not people are present in them. These images were removed when creating the visuAAL Skin Segmentation Dataset. However, all the instances in the visuAAL skin segmentation dataset have their corresponding match in the FashionPedia dataset.

BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by Martin et al.. The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.

This dataset images are collected from tropical Malaysian forests and encompasses a diverse range of arthropod species captured under various lighting and environmental conditions.

Here are a few use cases for this project:

1. Infrastructure Maintenance: This model can be extremely useful in detecting and classifying cracks in different infrastructure forms like bridges, roads, buildings, etc. Ensuring timely repairs and maintenance can reduce potential accidents and enhance safety.

2. Aerospace Industry: The model may also be used in the aerospace industry to identify cracks in different parts of aircraft, like the fuselage or engine components. This can contribute to improving flight safety and prolonging the service life of the aircraft.

3. Auto Industry Quality Control: The model can be used in the auto industry for detecting cracks in vehicle components during the manufacturing process. Early detection can help ensure high-quality products and reduce recall costs.

4. Archaeological Preservation: The segmentation model can be used by archaeologists and museum curators to detect cracks in ancient artifacts and structures. This can help prevent further damage and aid in restoration and preservation efforts.

5. Energy Sector: In the energy sector, especially renewable energy like wind turbines or solar panels, the model could be used to check for cracks that might affect efficiency and safety. Identifying cracks early can help prevent expensive downtime and repairs.

The Outdoor Objects Semantic Segmentation Dataset is developed for applications in media & entertainment and robotics, consisting of a variety of internet-collected images with resolutions ranging from 1024 x 726 to 2358 x 1801 pixels. This dataset employs bounding box and key points annotations to segment various outdoor elements, including human body parts, natural scenery, architectural structures, pavements, transportation means, and more.

Delve into the Pupils Segmentation Dataset Essential for ophthalmology tech, AI driven vision studies, and advanced eye research.

Pan-STARSS imaging data and associated labels for galaxy segmentation into galactic centers, galactic bars, spiral arms and foreground stars derived from citizen scientist labels from the Galaxy Zoo: 3D project.

The material segmentation dataset comprises 3817 images gathered from the Virginia Department of Transportation (VDOT) Bridge Inspection Reports. There were four classes of material in the dataset: [concrete, steel, metal decking, and background]. The data was randomly sorted into training and testing using a custom script. 10% percent were reserved as the test set, and 90% were used as the training set. Therefore, there were 381 images in the test set and 3436 images in the training set. The original and the rescaled images used for training have been included. The images were resized to 512x512 for training and testing the DeeplabV3+ model. After training with the DeeplabV3+ model (DOI: 10.7294/16628620), we were able to achieve an F1-score of 94.2%. Details of the dataset, training process, and code can be referenced by reading the associated journal article. The GitHub repository information may be found in the journal article.If you are using the dataset in your work, please include both the journal article and the dataset citation.

The authors of the iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images dataset have introduced the first benchmark dataset for instance segmentation in aerial imagery, which merges instance-level object detection and pixel-level segmentation tasks. It contains 655,451 object instances spanning 15 different categories across 2,806 high-resolution images. Precise per-pixel annotations have been provided for each instance, ensuring accurate localization for detailed scene analysis. Compared to existing small-scale aerial image-based instance segmentation datasets, iSAID boasts 15 times the number of object categories and 5 times the number of instances.

Dataset

This dataset was created by Dhanvin Sankaranand

Contents

Assemble a diverse collection of images showcasing nails of various shapes, sizes, health conditions, and colors.

Dataset

This dataset was created by Himanshi Kawade

Released under Apache 2.0

Contents

"Image.zip" contains 955 corrrosion images, 1480 crack images, 1269 free lime images, 873 water leakage images, and 1244 spalling images. These images are labeled with numbers from 0 to 6 including the background. The "Label.zip" file contains the labeled images, and the "Image.json" file contains the label information.

The CheXmask Database presents a comprehensive, uniformly annotated collection of chest radiographs, constructed from five public databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest and VinDr-CXR. The database aggregates 657,566 anatomical segmentation masks derived from images which have been processed using the HybridGNet model to ensure consistent, high-quality segmentation. To confirm the quality of the segmentations, we include in this database individual Reverse Classification Accuracy (RCA) scores for each of the segmentation masks. This dataset is intended to catalyze further innovation and refinement in the field of semantic chest X-ray analysis, offering a significant resource for researchers in the medical imaging domain.

Alabama Buildings Segmentation dataset is the combination of BingMap satellite images and masks from Microsoft Maps. It is almost from Alabama, US (99%). Others from Columbia. Dataset contains 10200 satellite images and 10200 masks with weight ~ 17Gb. The satellite images from this dataset have resolution 0.5m/pixel, image size 1024x1024, ~1.5Mb/image. Dataset only contains pictures that have the total area of builbuilding in mask >= 1% area of that pictures. It means there are no images that do not have any building in this dataset.

This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung, and infections are labeled by two radiologists and verified by an experienced radiologist.
To promote the studies of annotation-efficient deep learning methods, we set up three segmentation benchmark tasks based on this dataset https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark.

In particular, we focus on learning to segment left lung, right lung, and infections using

• pure but limited COVID-19 CT scans;
• existing labeled lung CT dataset from other non-COVID-19 lung diseases;
• heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.

Context

https://www.airs-dataset.com/

AIRS (Aerial Imagery for Roof Segmentation) is a public dataset that aims at benchmarking the algorithms of roof segmentation from very-high-resolution aerial imagery. The main features of AIRS can be summarized as:

• 457km2 coverage of orthorectified aerial images with over 220,000 buildings
• Very high spatial resolution of imagery (0.075m)
• Refined ground truths that strictly align with roof outlines

Citation

@article{chen2019,
 title={Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings},
 author={Chen, Qi and Wang, Lei and Wu, Yifan and Wu, Guangming and Guo, Zhiling and Waslander, Steven L},
 journal={ISPRS Journal of Photogrammetry and Remote Sensing},
 volume={147},
 pages={42--55},
 year={2019},
 publisher={Elsevier}
}