  The advancement of autonomous drones, essential for sectors such as remote
sensing and emergency services, is hindered by the absence of training datasets
that fully capture the environmental challenges present in real-world
scenarios, particularly operations in non-optimal weather conditions and the
detection of thin structures like wires. We present the Drone Depth and
Obstacle Segmentation (DDOS) dataset to fill this critical gap with a
collection of synthetic aerial images, created to provide comprehensive
training samples for semantic segmentation and depth estimation. Specifically
designed to enhance the identification of thin structures, DDOS allows drones
to navigate a wide range of weather conditions, significantly elevating drone
training and operational safety. Additionally, this work introduces innovative
drone-specific metrics aimed at refining the evaluation of algorithms in depth
estimation, with a focus on thin structure detection. These contributions not
only pave the way for substantial improvements in autonomous drone technology
but also set a new benchmark for future research, opening avenues for further
advancements in drone navigation and safety.
