Uncovering Archaeological Sites in Airborne LiDAR Data With Data-Centric Artificial Intelligence

Article Archaeology

Objective comparison of relief visualization techniques with deep CNN for archaeology

Alexandre Guyot et al.

Summary: Archaeology has been revolutionized by ALS technology, providing high-resolution synoptic views for identifying landform patterns resulting from human occupation. A new approach utilizing computer-vision algorithms has been developed to objectively benchmark and select the most suitable VTs for archaeological prospection. This study demonstrates the potential of the computer vision approach in enhancing the efficiency and objectivity of archaeological research.

JOURNAL OF ARCHAEOLOGICAL SCIENCE-REPORTS (2021)

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Article Environmental Sciences

Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia

Iban Berganzo-Besga et al.

Summary: This study introduces an algorithm that combines LiDAR and multispectral satellite data for large-scale automatic detection of burial mounds. By utilizing a combination of random forest and deep learning models, it significantly improves the detection rate and recall values compared to previous attempts, achieving the goal of detecting burial mounds across a wide area.

REMOTE SENSING (2021)

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Review Computer Science, Information Systems

Digital Terrain Models Generated with Low-Cost UAV Photogrammetry: Methodology and Accuracy

Sergio Ivan Jimenez-Jimenez et al.

Summary: This paper reviews the process and parameters of generating DTM using UAV photogrammetry, analyzing the impact of four aspects on the quality and accuracy of DTM. The study found that UAV photogrammetry can provide high-accuracy DTMs for both flat terrain and complex topography.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2021)

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Review Computer Science, Artificial Intelligence

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Omar Alghushairy et al.

Summary: This paper introduces the concept of outlier detection, discusses the importance of local outlier detection, and presents the Local Outlier Factor (LOF) method. It points out the deficiencies in current local outlier detection algorithms for data streams and proposes the development of better algorithms to effectively analyze local outliers in data streams.

BIG DATA AND COGNITIVE COMPUTING (2021)

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Article Computer Science, Information Systems