This paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available.
Sixty-six new archaeological sites have been discovered thanks to the combined use of different remote sensing techniques and open access geospatial datasets (mainly aerial photography, satellite imagery, and airborne LiDAR). These sites enhance the footprint of the Roman military presence in the northern fringe of the River Duero basin (León, Palencia, Burgos and Cantabria provinces, Spain). This paper provides a detailed morphological description of 66 Roman military camps in northwestern Iberia that date to the late Republic or early Imperial eras. We discuss the different spatial datasets and GIS tools used for different geographic contexts of varied terrain and vegetation. Finally, it stresses out the relevance of these novel data to delve into the rationale behind the Roman army movements between the northern Duero valley and the southern foothills of the Cantabrian Mountains. We conclude that methodological approaches stimulated by open-access geospatial datasets and enriched by geoscientific techniques are fundamental to understand the expansion of the Roman state in northwestern Iberia during the 1st c. BC properly. This renewed context set up a challenging scenario to overcome traditional archaeological perspectives still influenced by the cultural-historical paradigm and the pre-eminence of classical written sources.
Airborne laser scanning (ALS) data is increasingly distributed freely for ever larger territories, albeit usually in only low resolution. This data source is extensively used in archaeology; however, various remains of past human activities are not recorded in sufficient detail, or are missing completely. The main purpose of this paper is to present a cost-effective approach providing reliable and accurate 3D documentation of the deserted medieval settlement of Hound Tor, a complex site consisting of preserved stone building walls and field system remains. The proposed procedure integrates ALS data with structure from motion (SfM) photogrammetry into a single data source (point cloud). Taking advantage of the benefits of both techniques (reclassified ALS data documents the hinterland, while SfM records the residential area in high detail), an enhanced 3D model has been created surpassing the available ALS data and reflecting the actual state of preserved features. The final outputs will help with the management of the site, its presentation to the general public, and also to enrich understanding of it. As both data sources are currently easily accessible and the proposed procedure has only limited budget requirements, it can be easily adopted and applied extensively (e.g., for virtual preservation of threatened complex sites and areas).
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