# DDOS: The Drone Depth and Obstacle Segmentation Dataset

arXiv: https://arxiv.org/abs/2312.12494 · HTML: https://mlsanity.com/paper/2312.12494/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2204.00987/) (2022)
- [DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation](https://mlsanity.com/paper/2203.14211/) (2022)
- [SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations](https://mlsanity.com/paper/2112.04680/) (2021)
- [TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines](https://mlsanity.com/paper/2010.10032/) (2020)
- [SynDrone -- Multi-modal UAV Dataset for Urban Scenarios](https://mlsanity.com/paper/2308.10491/) (2023)
- [Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation](https://mlsanity.com/paper/2010.01910/) (2020)

## Built on by (most cited first, 10 of 10)

- [SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection](https://mlsanity.com/paper/2411.05633/) (2024)
- [OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective](https://mlsanity.com/paper/2512.20770/) (2025)
- [ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling](https://mlsanity.com/paper/2503.17856/) (2025)
- [AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World](https://mlsanity.com/paper/2606.29716/) (2026)
- [Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning](https://mlsanity.com/paper/2510.10068/) (2025)
- [Learning to Project for Cross-Task Knowledge Distillation](https://mlsanity.com/paper/2403.14494/) (2024)
- [Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves](https://mlsanity.com/paper/2405.14024/) (2024)
- [UCorr: Wire Detection and Depth Estimation for Autonomous Drones](https://mlsanity.com/paper/2509.14989/) (2025)
- [EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation](https://mlsanity.com/paper/2604.09694/) (2026)
- [DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV](https://mlsanity.com/paper/2607.21438/) (2026)

## Abstract (arXiv)

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.