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

夏威夷基拉韦厄火山正在喷发，这是首次有报道称小型无人机系统（UAS）被用于火山喷发的应急响应。机器人辅助搜索和救援中心（CRASAR）为希洛消防部门和夏威夷县民防飞行了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羽流遮挡，这是以前从未遇到过的。这些烟雾干扰了救援人员对战术形势的理解，因为它模糊了下面的地面，经常阻止软件生成有用的地面地图。虽然火山喷发相当罕见，但同样的羽流问题很可能发生在其他有害物质事件中。用于除雾的机器学习技术只取得了部分成功，因为羽流带来的挑战与消除城市雾霾或烟雾截然不同。该项目进行快速研究，从剧照和视频中近实时地去除或减少羽流，以支持对正在发生的灾难的反应。它将使数据集可用，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2019年夏威夷AAAI人工智能会议上举办后续研讨会。这个项目创建了一个UAS开源图像数据集，来自正在进行的夏威夷Leilani火山喷发事件。它使用数据集来扩展和改进除雾算法，这将帮助夏威夷公共安全机构和火山学家透过干扰绘制熔岩范围和体积的蒸汽和气体羽流。与二氧化硫混合的蒸汽羽状物干扰了对熔岩场边界的解释，并在将图像拼接在一起时引入错误，或者导致细节被平均掉。烟雾是一种均匀的、薄的视觉现象，而羽流是不均匀的、厚的，这限制了当前技术的实用性，需要重点研究。该数据集为真实图像的语料库提供了一个机会，可以作为机器学习训练数据，并可以比较前后的结果。这个项目的智力价值是双重的。它提供了一个独特的机会来探索机器学习的新领域，用于图像中的异质厚羽。全面的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。该研究将立即改善莱兰尼火山喷发事件的应急管理和总体应急管理。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.