DDOS: The Drone Depth and Obstacle Segmentation Dataset The Drone Depth and Obstacle Segmentation (DDOS) dataset comprises synthetic aerial images captured by drones, along with corresponding depth maps and pixel wise semantic segmentation masks. DDOS is purpose built to support research and development in computer vision, focusing on tasks such as depth estimation and obstacle segmentation from aerial imagery. Emphasizing the detection of thin structures like wires and effective navigation in diverse weather conditions, DDOS serves as a valuable resource for advancing algorithms in autonomous drone technology. Paper: DDOS: The Drone Depth and Obstacle Segmentation Dataset Data Structure DDOS is organised as follows: Data Splits: Train: Contains 300 flights with a total of 30k images for training. Validation: Contains 20 flights with a total of 2k images for validation during model development. Test: Contains 20 flights with a total of 2k images for the final evaluation of the trained model. Environments: Neighbourhood: Contains data captured in urban and residential environments. Park: Contains data captured in park and natural environments. Flights: Each flight is represented by…
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