RAPID: Collaborative Research: Machine Learning for Dehazing Unmanned Aerial System Imagery from Volcanic Eruptions

夏威夷的基拉韦厄火山正在喷发，这是第一次报道小型无人机系统（UAS）被用于火山喷发的应急响应。机器人辅助搜救中心（CRASAR）为希洛消防局和夏威夷县民防局飞行了44架小型无人机。火山喷发的图像部分被携带有毒气体的蒸汽柱遮挡，这是以前从未遇到过的。羽流干扰了反应人员对战术情况的理解，因为它掩盖了下面的地面，并且经常阻止软件生成有用的表面地图。虽然火山爆发相当罕见，但在其他有害物质事件中可能会出现同样的羽流问题。用于除雾的机器学习技术只取得了部分成功，因为与消除城市雾霾或烟雾相比，羽流带来了一系列非常不同的挑战。该项目进行快速研究，以近实时地从静止图像和视频中消除或减少羽流，以支持对正在发生的灾害的反应。它将提供数据集，以便它们可以用于训练和评估新的机器学习算法。该项目将在夏威夷举行的2019年AAAI人工智能会议上举办后续研讨会。该项目将从正在进行的夏威夷Leilani火山喷发事件中创建UAS开源图像数据集。它使用数据集来扩展和改进去雾算法，这将有助于夏威夷公共安全机构和火山学家看穿蒸汽和气体的羽流，这些羽流干扰了熔岩的范围和体积。夹杂着二氧化硫的蒸汽柱干扰了对熔岩区边界的解释，并在拼接图像时引入了错误，或导致细节被平均化。烟雾是一种均匀的、稀薄的视觉现象，而羽流是异质的和厚的，限制了当前技术的实用性，需要重点研究。该数据集为真实的图像语料库提供了一个机会，该语料库可以用作机器学习训练数据，并能够比较前后结果。该项目的智力价值是双重的。它为探索机器学习的新领域提供了一个独特的机会，用于图像中的异质厚羽流。综合数据集将使计算机视觉，机器学习和应急信息学的基础工作成为可能。 该研究将立即改善Leilani火山爆发事件的应急管理和一般的应急管理。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

The ongoing eruption of the Kilauea volcano in Hawaii is the first reported time that small unmanned aerial systems (UAS) have been used for the emergency response to a volcanic eruption. The Center for Robot-Assisted Search and Rescue (CRASAR) flew 44 small UAS flights for the Hilo Fire Department and Hawaii County Civil Defense. The eruption imagery was partially occluded by plumes of steam carrying toxic gases, something that had not been encountered before. The plumes interfere with responders comprehending the tactical situation because it obscures the ground below and often prevents software from generating useful surface maps. While volcanic eruptions are fairly rare, the same plume problem is likely to occur in other hazardous material events. Machine learning techniques for dehazing were only partially successful because plumes present a very different set of challenges than removing urban haze or smog. This project conducts rapid research to remove or reduce plumes, from stills and video, in near real-time in order to support responses to the ongoing disaster. It will make the datasets available so that they can be used for training and evaluating new machine learning algorithms. The project will host a follow up workshop at the 2019 AAAI Conference on Artificial Intelligence in Hawaii.This project creates a UAS open-source imagery dataset from the ongoing Leilani, Hawaii, volcanic eruption event. It uses the dataset to expand and refine dehazing algorithms that will help Hawaii public safety agencies and volcanologists see through the plumes of steam and gas that is interfering with mapping the extent and volume of the lava. Plumes of steam mingled with sulfur dioxide interfered with interpreting the boundaries of the lava field and introduced errors into stitching images together or caused details to be averaged out. Smog is a homogeneous, thin visual phenomenon while plumes are heterogeneous and thick, limiting the utility of current techniques and requiring focused research. The dataset offers an opportunity for a corpus of real imagery that can serve as machine learning training data and enable comparison of before and after results. The intellectual merit of the project is twofold. It provides a unique opportunity to explore a new area of machine learning for heterogeneous, thick plumes in images. The comprehensive dataset will enable foundational work in computer vision, machine learning, and emergency informatics. The research will immediately improve emergency management of the Leilani eruption event and emergency management in general.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.