Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands
Computer-aided methods for the automatic detection of archaeological objects are needed to cope with the ever-growing set of largely digital and easily available remotely sensed data. In this paper, a promising new technique for the automated detection of multiple classes of archaeological objects in LiDAR data is presented. This technique is based on R-CNNs (Regions-based Convolutional Neural Networks). Unlike normal CNNs, which classify the entire input image, R-CNNs address the problem of object detection, which requires correctly localising and classifying (multiple) objects within a large... Mehr ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2019 |
Reihe/Periodikum: | Journal of Computer Applications in Archaeology, Vol 2, Iss 1 (2019) |
Verlag/Hrsg.: |
Ubiquity Press
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Schlagwörter: | Remote sensing / Object detection / R-CNN / Machine learning / Archaeology / CC1-960 / Electronic computers. Computer science / QA75.5-76.95 |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-26804437 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://doi.org/10.5334/jcaa.32 |