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The Tunnel Cable Fire dataset is derived experimentally, this dataset contains images of cable flames at different stages, different cable layers, and different wind speeds, with a special focus on computer vision tasks such as fire detection and segmentation. These images have been enhanced with mosaic data for a total of 1812 datasets, including single and double layer cable fire images in the case of no wind and wind speed of 2.7m/s.

The lack of an objective and ready-to-use tool for preoperative planning in C2 pedicle screw placement surgery is notable. We developed C2-Net, a deep learning model for rapidly and accurately assessing C2 pedicle screw placement feasibility from CT images. C2-Net incorporates image segmentation and screw placement probability assessment modules.

The ability to train a robot to recognize human gestures is critical in enabling close proximity to Human-Robot Interaction (HRI). To that end, generating the appropriate dataset for the corresponding Machine Learning (ML) algorithm is essential. In this work, we introduced new datasets for hand gesture recognition. Given the complexity of generating thousands of physical hand gestures, we started with the basic hand gestures and developed additional synthetic gestures thus creating a comprehensive set.

Designing practical algorithms for damage detection in satellite images requires a substantial and well-labeled dataset for training, validation, and testing. In this paper, we collect GAZADeepDav: a high-resolution PlanetScope satellite imagery dataset with 7264 tiles for no damage and 6196 tiles for damage . This work is delving into the steps of collecting the dataset, Geotagging and employing deep learning architectures to distinguish damage in war zones while also providing valuable insights for researchers undertaking similar tasks in real-world applications.