Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management during dry seasons in Australia. However, many of these stone structures in remote or vegetated landscapes remain undocumented due to limited accessibility and the high cost of manual mapping. Deep learning-based segmentation offers a scalable approach for automated mapping of such features, but challenges remain: 1.the visual occlusion of low-lying dry-stone walls by dense vegetation and 2.the scarcity of labeled training data. This study presents DINO-CV, a self-supervised cross-view pre-training framework based on knowledge distillation, designed for accurate and data-efficient mapping of dry-stone walls using Digital Elevation Models (DEMs) derived from high-resolution airborne LiDAR. By learning invariant geometric and geomorphic features across DEM-derived views, (i.e., Multi-directional Hillshade and Visualization for Archaeological Topography), DINO-CV addresses the occlusion by vegetation and data scarcity challenges. Applied to the Budj Bim Cultural Landscape at Victoria, Australia, a UNESCO World Heritage site, the approach achieves a mean Intersection over Union (mIoU) of 68.6% on test areas and maintains 63.8% mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for large-scale, automated mapping of cultural heritage features in complex and vegetated environments. Beyond archaeology, this approach offers a scalable solution for environmental monitoring and heritage preservation across inaccessible or environmentally sensitive regions.
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Facebook AI Research（Facebook人工智能研究院）
The popularity of media sharing platforms in recent decades has provided an abundance of open source data that remains underutilized by heritage scholars. By pairing geotagged internet photographs with machine learning and computer vision algorithms, we build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit, and quantify visual culture and experiences in Cuzco, Peru. Leveraging large-scale in-the-wild tourist photos, our goals are to (1) understand how the intensifica…
AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage
Democritus University of Thrace（德谟克利特色雷斯大学）；Athena Research Center（雅典娜研究中心）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；University of Warsaw（华沙大学）
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；University of Stuttgart（斯图加特大学）
UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this paper, we present a multi-UAV approach for faster mapping of archaeological sites. Employing a team of UAVs not only reduces the mapping time by distribution of coverage area, but also improves the map accuracy by exchange of information. Through extensive experiments in a realistic simulation (AirSim), we demonstrate the advantages of using a collab…
Graz University of Technology（格拉茨技术大学）；St. Pölten University of Applied Sciences（圣珀尔滕应用科学大学）；ArcTron 3D（ArcTron 3D公司）；University of Cambridge（剑桥大学）；Parco Archeologico Comunale di Seradina-Bedolina（塞拉迪纳-贝多利纳市考古公园）
The development of powerful 3D scanning hardware and reconstruction algorithms has strongly promoted the generation of 3D surface reconstructions in different domains. An area of special interest for such 3D reconstructions is the cultural heritage domain, where surface reconstructions are generated to digitally preserve historical artifacts. While reconstruction quality nowadays is sufficient in many cases, the robust analysis (e.g. segmentation, matching, and classification) of reconstructed 3D data is still an open topic. In this paper, we target the automatic and interactive segmentation o…
California Institute of Technology（加州理工学院）；University of Oxford（牛津大学）
Large-scale image databases remain largely biased towards objects and activities encountered in a select few cultures. This absence of culturally-diverse images, which we refer to as the hidden tail, limits the applicability of pre-trained neural networks and inadvertently excludes researchers from under-represented regions. To begin remedying this issue, we curate Turath-150K, a database of images of the Arab world that reflect objects, activities, and scenarios commonly found there. In the process, we introduce three benchmark databases, Turath Standard, Art, and UNESCO, specialised subsets …
High-resolution optical and acoustic remote sensing datasets of the Puck Lagoon, Southern Baltic
Gdynia Maritime University（格丁尼亚海事大学）；Maritime Institute（海事研究所）；Cyprus University of Technology（塞浦路斯理工大学）；Technische Universität Berlin（柏林工业大学）；Gdańsk University of Technology（格但斯克理工大学）；Nicolaus Copernicus University in Toruń（托伦尼古拉·哥白尼大学）；Centre for Underwater Archaeology（水下考古中心）；University of Adelaide（阿德莱德大学）；Technical University Wien（维也纳工业大学）；MEWO SA（MEWO股份公司）；National Technical University of Athens（雅典国立技术大学）
Study of cultural-heritage objects with embellished realistic and abstract designs made up of connected and intertwined curves crosscuts a number of related disciplines, including archaeology, art history, and heritage management. However, many objects, such as pottery sherds found in the archaeological record, are fragmentary, making the underlying complete designs unknowable at the scale of the sherd fragment. The challenge to reconstruct and study complete designs is stymied because 1) most fragmentary cultural-heritage objects contain only a small portion of the underlying full design, 2) …
University of South Carolina（南卡罗来纳大学）；South Carolina Institute of Archaeology and Anthropology, University of South Carolina（南卡罗来纳大学南卡罗来纳考古与人类学研究所）；Center for Digital Humanities, University of South Carolina（南卡罗来纳大学数字人文中心）；Tianjin University（天津大学）
Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map. Different from most classical low-level image segmentation methods that are known to be very sensitive to the noise and occlusions, we propose a new supervised learning algorithm based on Convolutional Neural Network (CNN) to implicitly learn and utilize more curve geometry and pattern information for addressing…
Solving Jigsaw Puzzles in the Wild: Human-Guided Reconstruction of Cultural Heritage Fragments
DAIS, Ca’ Foscari University of Venice（威尼斯Ca’ Foscari大学DAIS部门）；Department of Historical Studies, University of Milan（米兰大学历史研究系）；Zhejiang Normal University（浙江师范大学）
Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model
Key Scientific Research Base of Paleoenvironment Reconstruction and Subsistence Studies in Archaeology (Shandong University)（考古学古环境重建与生存研究重点科学研究中心（山东大学））；Key Laboratory of Archaeological Sciences and Technology (Shandong University)（考古科学与技术重点实验室）
Ecological Legacies of Pre-Columbian Settlements Evident in Palm Clusters of Neotropical Mountain Forests
Leiden Institute of Advanced Computer Science (LIACS), Leiden University（莱顿大学莱顿高级计算机科学研究所）；Computational Social Sciences and Humanities Laboratory, Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS)（巴塞罗那超级计算中心-国家超级计算中心计算社会科学与人文实验室）；Subdirección de Gestión del Patrimonio, Instituto Colombiano de Antropología e Historia（哥伦比亚人类学与历史研究所遗产管理副局）；Department of Humanities, CASEs group, Universitat Pompeu Fabra（庞培法布拉大学人文系CASEs研究组）；Facultad de Artes, Universidad Nacional de Colombia（哥伦比亚国立大学艺术学院）；Naturalis Biodiversity Center（荷兰自然生物多样性中心）；Division of Earth Observation and Geoinformatics (DIOTG), General Coordination of Earth Sciences (CG-CT), National Institute for Space Research (INPE)（巴西国家空间研究所地球科学总协调部地球观测与地理信息司）；Departmento de Antropología, Universidad del Magdalena（马格达莱纳大学人类学系）；ICREA（加泰罗尼亚研究及高级研究机构）；GAES, The University of the Witwatersrand（威特沃特斯兰德大学GAES）
Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction
Department of Computer Science, Sapienza University（计算机科学系，萨皮恩扎大学）；Department of Mathematics and Computer Science, University of Udine（数学与计算机科学系，乌迪内大学）
AI-driven Structure Detection and Information Extraction from Historical Cadastral Maps (Early 19th Century Franciscean Cadastre in the Province of Styria) and Current High-resolution Satellite and Aerial Imagery for Remote Sensing
University of Graz（格拉茨大学）；Know Center, Graz（格拉茨知识中心）
This work presents the development of a flexible microscopic chemical imaging platform for cultural heritage that utilizes wavelength-tunable oblique illumination from a point source to obtain per-pixel reflectance spectra in the VIS-NIR range. The microscope light source can be adjusted on two axes allowing for a hemisphere of possible illumination directions. The synthesis of multiple illumination angles allows for the calculation of surface normal vectors, similar to phase gradients, and axial optical sectioning. The extraction of spectral reflectance images with high spatial resolutions fr…
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration
South China University of Technology（南方科技大学）；Intsig Information Co., Ltd.（Intsig信息有限公司）；INTSIG-SCUT Joint Lab on Document Analysis and Recognition（INTSIG-SCUT文档分析与识别联合实验室）；SCUT-Zhuhai Institute of Modern Industrial Innovation（南方科技大学珠海现代工业创新研究院）
This short article presents an alternative view of high resolution imaging from various sources with the aim of the discovery of potential sites of archaeological importance, or sites that exhibit `anomalies' such that they may merit closer inspection and analysis. It is conjectured, and to a certain extent demonstrated here, that it is possible for advanced civilizations to factor in erosion by natural processes into a large scale design so that main features be preserved even with the passage of millions of years. Alternatively viewed, even without such intent embedded in a design left for p…
National Technical University of Athens（雅典国立技术大学）；University of Malta（马耳他大学）
In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-$R$ tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimen…
EpiAgent: An Agent-Centric System for Ancient Inscription Restoration
School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其跨学科应用关键实验室（东南大学），教育部，中国）；Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China（计算机网络与信息集成关键实验室（东南大学），教育部，中国）；Nanjing University Museum, Nanjing University, China（南京大学博物馆，南京大学，中国）；The China Centre for Linguistic and Strategic Studies, Nanjing University, China（中国语言战略研究中心，南京大学，中国）
DeepAndes: A Self-Supervised Vision Foundation Model for Multi-Spectral Remote Sensing Imagery of the Andes
Department of Electrical and Computer Engineering, Vanderbilt University（电气与计算机工程系，范德比尔特大学）；Department of Anthropology, Vanderbilt University（人类学系，范德比尔特大学）；Data Science Institute, Vanderbilt University（数据科学研究院，范德比尔特大学）；Department of Computer Science, Vanderbilt University（计算机科学系，范德比尔特大学）；Department of Mathematics, Vanderbilt University（数学系，范德比尔特大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Department of Anthropology, Brown University（人类学系，布朗大学）；Department of Radiology, Weill Cornell Medicine（放射学系，韦尔·科恩医学中心）
ENSTA, Institut Polytechnique Paris（巴黎综合理工学院ENSTA）；Inria（法国国家信息与自动化研究所）；DaSCI Andalusian Institute in Data Science and Computational Intelligence, University of Granada（格拉纳达大学DaSCI安达卢西亚数据科学与计算智能研究所）；University of Granada（格拉纳达大学）；Free University of Bozen-Bolzano（波尔扎诺自由大学）
The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience. In contrast, symbolic AI systems that convert concepts into rules or symbols -- such as knowledge graphs -- are easier to explain. However, they present lower generalisation and scaling capabilities. A very important challenge is to fuse DL representations with expert knowle…
University of Southampton（南安普顿大学）；The University of Western Ontario（西安大略大学）
LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus fa…
