The Altai Mountains are one of the most impressive and valuable archaeological areas in the world. Kurgans (burial mounds) of ancient civilizations, which are scattered across the vast Altai area, are an exceptionally valuable source of information for archaeology. These precious archaeological resources, which sometimes have been preserved intact in the permafrost underground for over two millennia, are now under various threats, such as natural disasters, farmland expansion, touristic development, and most notably global warming. A detailed map or inventory of the mounds is essential but is still not available. In this study, we test the deep convolutional neural network (CNN) technique for automatic detection of stone mounds from high-resolution satellite images in four regions in the Altai Mountains. We propose three improvement techniques to increase the performance of off-the-shelf object detection methods that are originally proposed for daily-life objects. Our results demonstrate that it is feasible to apply CNN to detect stone mounds, and the detection results are good enough to capture their spatial distribution. CNN-based object detection can largely narrow down the search area for archaeologists in yet un-surveyed regions, and is therefore useful for preparing field survey campaigns and directing archaeological fieldwork. We also applied the method to an un-surveyed Altai Mountain area and successfully discovered stone mounds that are yet undocumented. Our method can potentially be applied to construct an inventory for all stone mounds present in the whole Altai Mountain region.

阿尔泰山是世界上最令人印象深刻和最有价值的考古地区之一。散布在广阔的阿尔泰地区的古代文明的库尔干人（墓葬），是考古学信息的极有价值的来源。这些珍贵的考古资源有时在地下多年冻土中完好无损地保存了两千年，现在却面临各种威胁，例如自然灾害，农田扩张，旅游业发展以及最显着的全球变暖。详细的土丘地图或清单很重要，但仍不可用。在这项研究中，我们测试了深度卷积神经网络（CNN）技术，该技术可从阿尔泰山四个地区的高分辨率卫星图像中自动检测石堆。我们提出了三种改进技术，以提高最初为日常生活对象提出的现成对象检测方法的性能。我们的结果表明，将CNN用于检测石堆是可行的，并且检测结果足以捕获其空间分布。基于CNN的物体检测可以大大缩小考古人员在尚未调查的地区的搜索范围，因此对于准备野外勘测活动和指导考古野外工作很有用。我们还将该方法应用于未调查的阿尔泰山区，并​​成功发现了尚未记载的石丘。我们的方法可以潜在地用于构建整个阿尔泰山区所有石堆的清单。