Learning from the Rare: Overcoming Class Imbalance in Archaeological Object Detection with Boosting Methods

Detecting surface potsherds using low-altitude remote sensing is challenging due to severe class imbalance and limited training data. This study develops and validates a semi-automatic detection methodology that adapts threshold-optimized boosting classifiers (AdaBoost, XGBoost) to maximize ceramic...

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Detalhes bibliográficos

Principais autores:	Argyro Argyrou, Federico Fasson, Emeri Farinetti, Apostolos Papakonstantinou, Dimitrios D. Alexakis, Athos Agapiou
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-03-01
Colecção:	Heritage
Assuntos:	archeology remote sensing accuracy paradox machine learning boosting algorithms surface potsherds
Acesso em linha:	https://www.mdpi.com/2571-9408/9/3/99
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