Identification of Archaeologically Relevant Areas Using Open Geodata

Meyer-Heß, M. Fabian

doi:10.1007/s42489-020-00049-w

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…A simple filter such as we applied would not suffice to remove these anthropogenic objects from the DEMs; extensive filtering is required (e.g. Meyer‐Heß, 2020; Van der Meulen et al, 2020). In built‐up areas, the natural relief might even be completely overprinted and not be retrievable at all by DEM filtering, and data sources other than DTMs and LSPs are required.…”

Section: Discussionmentioning

confidence: 99%

Comparing geomorphological maps made manually and by deep learning

Meij

Meijles

Marcos

et al. 2022

Earth Surf Processes Landf

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Geomorphological maps provide information on the relief, genesis and shape of the earth's surface and are widely used in sustainable spatial developments. The quality of geomorphological maps is however rarely assessed or reported, which limits their applicability. Moreover, older geomorphological maps often do not meet current quality requirements and require updating. This updating is time-consuming and because of its qualitative nature difficult to reproduce, but can be supported by novel computational methods. In this paper, we address these issues by (1) quantifying the uncertainty associated with manual geomorphological mapping, (2) exploring the use of convolutional neural networks (CNNs) for semi-automated geomorphological mapping and (3) testing the sensitivity of CNNs to uncertainties in manually created evaluation data.We selected a test area in the Dutch push-moraine district with a pronounced relief and a high variety of landforms. For this test area we developed five manually created geomorphological maps and 27 automatically created landform maps using CNNs. The resulting manual maps are similar on a regional level. We could identify the causes of disagreement between the maps on a local level, which often related to differences in mapping experience, choices in delineation and different interpretations of the legend. Coordination of mapping efforts and field validation are necessary to create accurate and precise maps. CNNs perform well in identifying landforms and geomorphological units, but fail at correct delineation. The human geomorphologist remains necessary to correct the delineation and classification of the computed maps. The uncertainty in the manually created data that are used to train and evaluate CNNs have a large effect on the model performance and evaluation. This also advocates for coordinated mapping efforts to ensure the quality of manually created training and test data. Further model development and data processing are required before CNNs can act as standalone mapping techniques.

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Section: Discussionmentioning

confidence: 99%

Comparing geomorphological maps made manually and by deep learning

Meij

Meijles

Marcos

et al. 2022

Earth Surf Processes Landf

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“…These terraces tend to be morphologically distinct, with sharper edges caused by the use of heavy machinery, but these still need to be manually removed. This sort of cleaning process is typical in many AFE studies (Davis et al 2019;Meyer-Heß, 2020;Meyer et al 2019). Data cleaning required roughly two weeks of additional work.…”

Section: Limitations Of Our Study and Afementioning

confidence: 99%

Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

Quintus

Davis

Cochrane

2023

Archaeological Prospection

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Lidar datasets have been crucial for documenting the scale and nature of human ecosystem engineering and land use. Automated analysis methods, which have been rising in popularity and efficiency, allow for systematic evaluations of vast landscapes. Here, we use a Mask R‐CNN deep learning model to evaluate terracing—artificially flattened areas surrounded by steeper slopes—on islands in American Sāmoa. Mask R‐CNN is notable for its ability to simultaneously perform detection and segmentation tasks related to object recognition, thereby providing robust datasets of both geographic locations of terracing features and their spatial morphometry. Using training datasets from across American Sāmoa, we train this model to recognize terracing features and then apply it to the island of Tutuila to undertake an island‐wide survey for terrace locations, distributions and morphologies. We demonstrate that this model is effective (F1 = 0.718), but limitations are also documented that relate to the quality of the lidar data and the size of terracing features. Our data show that the islands of American Sāmoa display shared patterns of terracing, but the nature of these patterns are distinct on Tutuila compared with the Manu'a island group. These patterns speak to the different interior configurations of the islands. This study demonstrates how deep learning provides a better understanding of landscape construction and behavioural patterning on Tutuila and has the capacity to expand our understanding of these processes on other islands beyond our case study.

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“…Location-based ranking is a suitable method for differentiating morphologically identical structures [14,20,28]. This is implemented via the Positive Layer from previous work [29] (Figure 3). During generation, relief imprints of modern infrastructure were estimated and removed from the area of Westphalia and Lippe.…”

Section: Datamentioning

confidence: 99%

Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology

Meyer-Heß

Pfeffer²,

Jürgens

2022

Remote Sensing

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Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis.

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Identification of Archaeologically Relevant Areas Using Open Geodata

Cited by 5 publications

References 19 publications

Comparing geomorphological maps made manually and by deep learning

Comparing geomorphological maps made manually and by deep learning

Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology

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