Deep convolutional neural networks for remote sensing investigation of looting of the archeological site of Al-Lisht, Egypt

Looting of archaeological sites is a global problem. To quantify looting on a nationwide scale and to assess the validity and scope of the looting reports and modern encroachment, satellite archaeologist have turned to mapping looting from space. High-resolution satellite imagery has become a powerful tool and resource for monitoring looting and site destruction remotely and proves to be an independent way to cross check and analyze against varied and unreliable reports from media and government agencies. It is estimated that over a quarter of Egypt’s 1100 known archaeological areas have sustained major damage and site destruction directly linked to looting. The organized looting and illicit trafficking of art and antiques, known as cultural racketeering, is a multi-billion dollar worldwide criminal industry that thrives in Egypt during times of political and economic turmoil and potentially funds drug cartels, armed insurgents, and even terrorist networks. This study analyzes methods used to monitor site looting at the archaeological site of al-Lisht which is located in the Egyptian governorate of Giza south of Cairo. Monitoring damage and looting over time has been largely dependent upon direct human interpretation of images. The manual image comparison method is laborious, time consuming, and prone to human-induced error. Recently, partially-supervised methods using deep convolutional neural networks (CNNs) have shown astounding performance in object recognition and detection. This study seeks to demonstrate the viability of using deep convolutional neural networks (CNNs) within the field of archaeology and cultural heritage preservation for the purpose of augmenting or replacing the manual detection of looting. It brings recent advancements from the field of Artificial Intelligence to an applied GIS challenge at the intersection of remote sensing and archaeology. The objective is to show that CNNs are a more accurate and expedient method for the detecting of looting with wide-ranging application beyond this specific research.

考古遗址盗掘是一项全球性难题。为在全国范围内量化盗掘活动，并评估盗掘报告与现代侵占行为的有效性与波及范围，卫星考古学家转而借助太空成像开展盗掘点位测绘。高分辨率卫星影像已成为远程监测盗掘与遗址破坏的有力工具与资源，同时可作为独立手段，对媒体与政府机构发布的各类不可靠报告进行交叉核验与分析。据估算，埃及已知的1100处考古遗址中，已有超过四分之一遭受了与盗掘直接相关的严重破坏与损毁。有组织的文物盗掘与艺术品、古董非法贩运活动（即文化劫掠犯罪）是一项年产值达数十亿美元的全球性犯罪产业，在埃及政治与经济动荡时期尤为猖獗，其所得资金甚至可能用于资助贩毒集团、武装叛乱分子及恐怖网络。本研究针对位于开罗以南埃及吉萨省的利斯赫特（al-Lisht）考古遗址，对其遗址盗掘监测方法展开分析。长期以来，遗址破坏与盗掘情况的监测主要依赖人工对影像进行判读。人工影像比对法不仅耗时费力、工作量繁重，还极易引入人为误差。近年来，基于深度卷积神经网络（deep convolutional neural networks, CNNs）的半监督方法在目标识别与检测领域展现出优异性能。本研究旨在论证，将深度卷积神经网络应用于考古学与文化遗产保护领域，以辅助乃至替代人工盗掘检测方法的可行性。本研究将人工智能（Artificial Intelligence）领域的最新进展，应用于遥感与考古学交叉领域的地理信息系统（Geographic Information System, GIS）实际问题中。本研究的目标在于证明，卷积神经网络是一种更为精准高效的盗掘检测手段，且其应用场景可覆盖本研究之外的更多领域。