论文标题
关于高分辨率无人机图像的全面语义细分，用于自然灾害损害评估
Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
论文作者
论文摘要
在本文中，我们提出了一个大规模的飓风迈克尔数据集，以用于灾难情景中的视觉感知，并分析最新的深层神经网络模型以进行语义细分。该数据集由大约2000个高分辨率航空图像组成，并带有带注释的地面真实数据用于语义分割。我们讨论数据集的挑战，并在此数据集上培训最先进的方法，以评估这些方法能够识别灾难情况的能力。最后，我们讨论了未来研究的挑战。
In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.
