Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation

IF 2.1 3区 地球科学 0 ARCHAEOLOGY
Daniel Canedo, João Fonte, Rita Dias, Tiago do Pereiro, Luís Gonçalves‐Seco, Marta Vázquez, Petia Georgieva, António J. R. Neves
{"title":"Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation","authors":"Daniel Canedo, João Fonte, Rita Dias, Tiago do Pereiro, Luís Gonçalves‐Seco, Marta Vázquez, Petia Georgieva, António J. R. Neves","doi":"10.1002/arp.1958","DOIUrl":null,"url":null,"abstract":"Recent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR‐derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post‐processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F<jats:sub>1</jats:sub> score of 66%. In England experiments, the system achieved a 59% F<jats:sub>1</jats:sub> score when fine‐tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.","PeriodicalId":55490,"journal":{"name":"Archaeological Prospection","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archaeological Prospection","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1002/arp.1958","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ARCHAEOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Recent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR‐derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post‐processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F1 score of 66%. In England experiments, the system achieved a 59% F1 score when fine‐tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.
利用深度多模态分割技术自动检测遥感图像中的山丘堡垒
遥感和人工智能领域的最新进展有可能彻底改变考古遗址的自动探测。然而,解读遥感图像是一项极具挑战性的任务,再加上考古遗址错综复杂的形状,这些都会阻碍计算机视觉系统的性能。本作品介绍了一种经过训练的计算机视觉系统,用于在遥感图像中高效检测山丘。该系统配备了一个经过调整的多模态语义分割模型,将激光雷达衍生的 LRM 图像与航空正射影像进行特征融合,生成一个二进制掩模,精确定位检测到的山丘。后处理包括边际和区域滤波器,以去除边缘推断和较小的异常点。由此产生的推断结果将进行硬性正向和负向挖掘,由考古专家对其进行分类,为训练数据填充新的样本,以重新训练分割模型。由于计算机视觉系统在搜索过程中更有可能遇到背景图像,因此训练数据有意偏向于负面示例。这种方法旨在减少误报的数量,在将机器学习解决方案应用于遥感图像时通常会出现这种情况。在伊比利亚西北部的实验中,经过一次硬正负挖掘迭代后,误报数量从 5678 个急剧下降到 40 个,降幅达 99.3%,F1 得分为 66%。在英格兰的实验中,该系统经过微调并在全国范围内部署后,F1得分率达到59%。尽管山堡和其他类型的围墙具有典型的复杂多变的形状,但该系统还是成功地探测到了这些围墙,从而证明了它对各种考古遗址的可扩展性。未来的工作将探索考古预测建模,以确定具有较高考古潜力的区域,从而集中搜索,解决处理时间方面的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Archaeological Prospection
Archaeological Prospection 地学-地球科学综合
CiteScore
3.90
自引率
11.10%
发文量
31
审稿时长
>12 weeks
期刊介绍: The scope of the Journal will be international, covering urban, rural and marine environments and the full range of underlying geology. The Journal will contain articles relating to the use of a wide range of propecting techniques, including remote sensing (airborne and satellite), geophysical (e.g. resistivity, magnetometry) and geochemical (e.g. organic markers, soil phosphate). Reports and field evaluations of new techniques will be welcomed. Contributions will be encouraged on the application of relevant software, including G.I.S. analysis, to the data derived from prospection techniques and cartographic analysis of early maps. Reports on integrated site evaluations and follow-up site investigations will be particularly encouraged. The Journal will welcome contributions, in the form of short (field) reports, on the application of prospection techniques in support of comprehensive land-use studies. The Journal will, as appropriate, contain book reviews, conference and meeting reviews, and software evaluation. All papers will be subjected to peer review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信