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 ...

Verfasser: Verschoof-van der Vaart, Wouter Baernd
Lambers, Karsten
Dokumenttyp: Artikel
Erscheinungsdatum: 2019
Verlag/Hrsg.: Ubiquity Press
Schlagwörter: Remote sensing / Object detection / R-CNN / Machine learning
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28765409
Datenquelle: BASE; Originalkatalog
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Link(s) : https://account.journal.caa-international.org/index.php/up-j-jcaa/article/view/32