ABSTRACT

Morphological characteristics of cirques have been studied for decades; however, no repeatable set of metrics has been derived that can consistently identify them. Perhaps more importantly, there is no consensus definition of the form that distinguishes cirques and clusters of cirques from non-cirques. In our approach, we use Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs) in a Convolutional Neural Network (CNN) framework to identify cirques in 20 mountain ranges globally. We extracted bounding boxes of cirques in 19 of these study areas and used them to develop a training set for a cirque identification model. The trained model was applied to the Sierra Nevada California to assess whether this algorithmic approach derived from a global dataset could produce consistent results in complex terrain with mutually interacting cirque forms. Using commonalities revealed using this approach, we find that there is a basic, recognizable and morphometrically quantifiable cirque form. This approach can be used to automate the identification of cirque locations and to guide the quantification of cirque form independent of the subjective definitions of individual workers. The approach can also be used to understand cirque form under different environmental conditions, including similar forms on Mars.

摘要

马戏团的形态特征已经研究了几十年。但是，还没有派生出可以一致识别它们的可重复指标集。或许更重要的是，对于区分马戏团和马戏团群与非马戏团的形式没有达成一致的定义。在我们的方法中，我们在卷积神经网络 (CNN) 框架中使用航天飞机雷达地形任务 (SRTM) 数字高程模型 (DEM) 来识别全球 20 个山脉中的马戏团。我们在其中 19 个研究区域中提取了马戏团的边界框，并使用它们开发了一个用于马戏团识别模型的训练集。将经过训练的模型应用于加利福尼亚内华达山脉，以评估这种源自全球数据集的算法方法是否可以在具有相互交互的圆环形式的复杂地形中产生一致的结果。利用这种方法揭示的共性，我们发现有一个基本的、可识别的和形态上可量化的圆环形式。这种方法可用于自动识别马戏团位置并指导独立于个体工人的主观定义的马戏团形式的量化。该方法还可用于了解不同环境条件下的圆环形状，包括火星上的类似形状。这种方法可用于自动识别马戏团位置并指导独立于个体工人的主观定义的马戏团形式的量化。该方法还可用于了解不同环境条件下的圆环形状，包括火星上的类似形状。这种方法可用于自动识别马戏团位置并指导独立于个体工人的主观定义的马戏团形式的量化。该方法还可用于了解不同环境条件下的圆环形状，包括火星上的类似形状。