标题： 使用人工智能检测和跟踪新月形沙丘 显示英文标题

标题： Detection and tracking of barchan dunes using Artificial Intelligence

摘要： 沙丘是地球上和其他天体上普遍存在的新月形沙丘，它们在沙丘场中组织在一起，相互作用。 在过去几十年中，卫星图像被广泛用于检测地球和火星表面的沙丘，人工智能（Artificial Intelligence）已成为监测这些地貌的重要工具。 然而，以前的研究中报道的自动检测仅限于孤立的沙丘，并不能成功识别相互作用的沙丘群。 在本文中，我们通过进行实验并利用人工智能探索获取的图像，研究沙丘的自动检测和跟踪。 在使用沙丘之间发生复杂相互作用的控制实验图像训练神经网络后，我们对地球和火星的卫星图像也进行了相同的操作。 我们首次展示，经过适当训练的神经网络可以在不同环境中识别和跟踪相互作用的沙丘，使用不同类型的照片（对比度、颜色、视角、分辨率等），置信度（准确率）高于70%。 我们的结果为自动监测沙丘迈出了进一步的一步，对地球、火星和其他天体上的人员活动具有重要的应用价值。 显示英文摘要

摘要： Barchans are crescent-shape dunes ubiquitous on Earth and other celestial bodies, which are organized in barchan fields where they interact with each other. Over the last decades, satellite images have been largely employed to detect barchans on Earth and on the surface of Mars, with AI (Artificial Intelligence) becoming an important tool for monitoring those bedforms. However, automatic detection reported in previous works is limited to isolated dunes and does not identify successfully groups of interacting barchans. In this paper, we inquire into the automatic detection and tracking of barchans by carrying out experiments and exploring the acquired images using AI. After training a neural network with images from controlled experiments where complex interactions took place between dunes, we did the same for satellite images from Earth and Mars. We show, for the first time, that a neural network trained properly can identify and track barchans interacting with each other in different environments, using different image types (contrasts, colors, points of view, resolutions, etc.), with confidence scores (accuracy) above 70%. Our results represent a step further for automatically monitoring barchans, with important applications for human activities on Earth, Mars and other celestial bodies.