利用人工智能检测与追踪新月形沙丘（barchan dunes）
Detection and tracking of barchan dunes using Artificial Intelligence
AI总结：
针对以往AI方法无法识别相互作用的新月形沙丘群的问题，研究用受控实验及地火卫星图像训练神经网络，首次实现不同环境下多类型图像中相互作用沙丘的检测追踪，准确率超70%，推动沙丘自动监测发展。
摘要：
新月形沙丘（barchan）是地球及其他天体上普遍存在的新月形沙丘，它们聚集形成新月形沙丘场并在其中相互作用。过去几十年间，卫星图像被大量用于探测地球和火星表面的新月形沙丘，人工智能（AI）已成为监测这类床面形态的重要工具。但以往研究中的自动检测仅局限于孤立沙丘，无法成功识别相互作用的新月形沙丘群。本文通过开展实验并利用AI分析获取的图像，探究新月形沙丘的自动检测与追踪方法。我们先用存在复杂沙丘相互作用的受控实验图像训练神经网络，再用地球和火星的卫星图像完成训练。研究首次表明，经过恰当训练的神经网络能够在不同环境中、利用不同类型的图像（对比度、色彩、视角、分辨率等）识别并追踪相互作用的新月形沙丘，置信度得分（准确率）超过70%。本研究结果推动了新月形沙丘自动监测技术的发展，对地球、火星及其他天体上的人类活动具有重要应用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
