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，这是据报道首次将小型无人机系统(UAS)用于火山喷发的紧急反应。机器人辅助搜救中心(CRASAR)为希洛消防局和夏威夷县民防提供了44次小型无人机飞行。喷发图像被携带有毒气体的蒸汽羽流部分遮住了，这是以前从未遇到过的。这些羽状物干扰了响应人员理解战术情况，因为它遮挡了下面的地面，经常阻止软件生成有用的地形图。虽然火山喷发相当罕见，但同样的羽流问题可能会发生在其他危险物质事件中。用于消除雾霾的机器学习技术只取得了部分成功，因为羽流带来了与消除城市雾霾或烟雾截然不同的一系列挑战。该项目进行快速研究，以近乎实时的方式移除或减少剧照和视频中的羽流，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人工智能大会上举办后续研讨会。该项目根据正在进行的夏威夷雷拉尼火山喷发事件创建一个UAS开源图像数据集。它使用数据集来扩展和改进消除雾霾的算法，这些算法将帮助夏威夷的公共安全机构和火山学家看穿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流干扰了对熔岩区域边界的解释，并在拼接图像时引入了错误，或者导致细节被平均。烟雾是一种均匀、细小的视觉现象，而羽流是不均匀的、厚重的，限制了现有技术的实用性，需要有重点的研究。该数据集为真实图像语料库提供了机会，这些语料库可以作为机器学习训练数据，并能够比较前后的结果。该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一个新的机器学习领域，用于图像中不同种类的厚羽毛。全面的数据集将使计算机视觉、机器学习和应急信息学方面的基础工作成为可能。这项研究将立即改善莱拉尼火山喷发事件的应急管理和总体应急管理。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.