摘要翻译：
重力物质流，如雪崩、泥石流和落石是高寒地区常见的事件，对运输路线有很大影响。在过去的几十年里，为了系统地处理这一威胁，已经制定了危险区域地图。这些地图标明了宜居地区的脆弱地带，以便有效规划减灾措施和发展住区。危险区域地图已被证明是减少极端事件中死亡人数的有效工具。它们是在一个复杂的过程中创建的，基于经验、经验模型、物理模拟和历史数据。因此，绘制这类地图的费用昂贵，而且仅限于极为重要的区域，例如永久有人居住的地区。在这项工作中，我们将危险区域制图的任务解释为一个分类问题。特定区域的每个点都必须根据其易损性进行分类。在区域范围内，这导致了一个分割问题，即必须在各自的危险区域内划分总面积。最近人工智能的发展，即卷积神经网络，导致了一个非常相似的任务，图像分类和语义分割，即计算机视觉的重大改进。我们使用卷积神经网络来识别可能发生灾难性雪崩的地形，并将其覆盖范围内的点标记为脆弱点。对所有点重复这个过程可以让我们生成一个人工危险区域图。根据Tirolean Oberland的危险区图，我们证明了该方法是可行的和有希望的。然而，在这些技术能够可靠地应用之前，还需要更多的训练数据和方法的进一步改进。
---
英文标题：
《Predicting Natural Hazards with Neuronal Networks》
---
作者：
Matthias Rauter and Daniel Winkler
---
最新提交年份：
2018
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  Gravitational mass flows, such as avalanches, debris flows and rockfalls are common events in alpine regions with high impact on transport routes. Within the last few decades, hazard zone maps have been developed to systematically approach this threat. These maps mark vulnerable zones in habitable areas to allow effective planning of hazard mitigation measures and development of settlements. Hazard zone maps have shown to be an effective tool to reduce fatalities during extreme events. They are created in a complex process, based on experience, empirical models, physical simulations and historical data. The generation of such maps is therefore expensive and limited to crucially important regions, e.g. permanently inhabited areas.   In this work we interpret the task of hazard zone mapping as a classification problem. Every point in a specific area has to be classified according to its vulnerability. On a regional scale this leads to a segmentation problem, where the total area has to be divided in the respective hazard zones. The recent developments in artificial intelligence, namely convolutional neuronal networks, have led to major improvement in a very similar task, image classification and semantic segmentation, i.e. computer vision. We use a convolutional neuronal network to identify terrain formations with the potential for catastrophic snow avalanches and label points in their reach as vulnerable. Repeating this procedure for all points allows us to generate an artificial hazard zone map. We demonstrate that the approach is feasible and promising based on the hazard zone map of the Tirolean Oberland. However, more training data and further improvement of the method is required before such techniques can be applied reliably.
---
PDF链接：
https://arxiv.org/pdf/1802.07257