Archaeological Prospection

Papers
(The H4-Index of Archaeological Prospection is 11. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models i44
Potential of deep learning segmentation for the extraction of archaeological features from historical map series42
The aerial panopticon and the ethics of archaeological remote sensing in sacred cultural spaces21
Exploration and reconstruction of a medieval harbour using hydroacoustics, 3‐D shallow seismic and underwater photogrammetry: A case study from Puck, southern Baltic Sea21
Ethical considerations for remote sensing and open data in relation to the endangered archaeology in the Middle East and North Africa project18
Nuna Nalluyuituq (The Land Remembers): Remembering landscapes and refining methodologies through community‐based remote sensing in the Yukon‐Kuskokwim Delta, Southwest Alaska14
New developments in drone‐based automated surface survey: Towards a functional and effective survey system14
Applying automated object detection in archaeological practice: A case study from the southern Netherlands13
The use of LiDAR in reconstructing the pre‐World War II landscapes of abandoned mountain villages in southern Poland13
Integration of 2D/3D ground penetrating radar and electrical resistivity tomography surveys as enhanced imaging of archaeological ruins: A case study in San El‐Hager (Tanis) site, northeastern Nile De12
Integrated use of unmanned aerial vehicle photogrammetry and terrestrial laser scanning to support archaeological analysis: The Acropolis of Selinunte case (Sicily, Italy)11
Investigating ancient agricultural field systems in Sweden from airborne LIDAR data by using convolutional neural network11
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