Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation.
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...
| Publicado en: | Archaeological Prospection Vol. 32; no. 2; pp. 297 - 312 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Apr2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=185659949&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185659949 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 10752196 58D jtl: Archaeological Prospection issn: 10752196 maglogo: Y pubinfo: dt: Apr2025 vid: 32 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 185659949 10.1002/arp.1958 ppf: 297 ppct: 15 formats: tig: atl: Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation. aug: au: Canedo, Daniel Fonte, João Dias, Rita do Pereiro, Tiago Gonçalves‐Seco, Luís Vázquez, Marta Georgieva, Petia Neves, António J. R. affil: IEETA/DETI, University of Aveiro, Aveiro, Portugal Department of Archaeology and History, University of Exeter, Exeter, UK ERA Arqueologia, Calçada de Santa Catarina, Cruz Quebrada, Portugal Centre for the Humanities (CHAM), Faculdade de Ciências Sociais e Humanas, Universidade NOVA de Lisboa, Lisboa, Portugal ICArEHB, Campus de Gambelas, Universidade do Algarve, Faro, Portugal UMAIA, University of Maia, Maia, Portugal INESC TEC–Institute for Systems and Computer Engineering, Technology and Science, University of Porto, Porto, Portugal N2i, Polytechnic Institute of Maia, Maia, Portugal Instituto de Telecomunicações, Universidade de Aveiro, Aveiro, Portugal su: Computer vision Artificial intelligence Remote sensing Computer performance Computer systems sug: subj: Computer vision Artificial intelligence Remote sensing Computer performance Computer systems keyword: computer vision hillforts LiDAR multimodal semantic segmentation orthoimagery transformer ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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