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The rapid expansion of urban areas has been a feature of the industrial age. Landscape features have been replaced or transformed to accommodate the needs of an increasingly urban society. Our study focuses on the disappearance or transformation of man-made ponds in the historic region of Bohemia in the Czech Republic. Using current and historic maps, orthophotography and a digital terrain model, we have identified over one thousand former ponds within the 34 cities that were selected. The original dam structures have been identified in association with 437 ponds, most of which have been used as embankments for transport infrastructure. In areas within the immediate vicinity of the cities, modern agriculture now utilizes the sites of former ponds. The Main Map shows the results of our survey, revealing the quantity and nature of this landscape change together with a detailed case study in the city of Pilsen. [ABSTRACT FROM AUTHOR]

Due to complicated backgrounds and unclear target orientation, automated object detection is difficult in the field of archaeology. Most of the current convolutional neural network (CNN) object‐oriented detection techniques are based on a faster region‐based CNN (R‐CNN) and other one‐stage detectors that often lack adequate processing speeds and detection accuracies. Recently, the two‐stage detector Mask R‐CNN technique achieved impressive results in object detection and instance segmentation problems and was successfully applied in the analysis of archaeological airborne laser scanning (ALS) data. In this study, we outline a modified Mask R‐CNN technique that reliably and efficiently detects relict charcoal hearth (RCH) sites on light detection and ranging (LiDAR) data‐based digital elevation models (DEMs). Using image augmentation and image preprocessing steps combined with the deep learning‐based adaptive gradient method with a dynamic bound on the learning rate (AdaBound) optimization technique, we could improve the model's accuracy and significantly reduce its training time. We use DEMs based on high‐resolution LiDAR data and the visualization for archaeological topography (VAT) technique that give images with a very strong contrast of the terrain and the outline of the sites of interest in the North German Lowland. Therefore, the model can identify RCH sites with an average recall of 83% and an average precision of 87%. Techniques such as the modified Mask R‐CNN method outlined here will help to greatly improve our knowledge about archaeological site densities in the realm of historical charcoal production and past human‐landscape interactions. This method provides an accurate, time‐efficient and bias‐free large‐scale site mapping option not only for the North German Lowland but potentially for other landscapes as well. [ABSTRACT FROM AUTHOR]

With the increasing availability of high‐resolution digital elevation models (DEMs), large‐scale mapping of anthropogenic relief features has become feasible for more areas. However, the landscape‐scale distribution patterns of anthropogenic landforms and the quality of DEM‐based mapping can be highly heterogeneous. In this study, we mapped relict charcoal hearths (RCHs) in two study regions with differing environmental backgrounds, covering forest areas of more than 15,000 km2, analyzed the RCH distributions and evaluated possibilities for predictive modeling of RCH occurrence with respect to natural and cultural landscape structures. More than 45,000 RCHs were recorded in each region, with high site densities even in areas remote from charcoal‐consuming industries. Variations in the quality of DEM‐based mapping were related to small‐scale differences in the DEM quality and larger‐scale substrate heterogeneity. A clear association between RCHs and historical industrial sites was found in the Northern European Lowland; while the density of mapped RCHs was predominantly related to geology and morphology in the lower mountain ranges. The results show that variations in mapping quality across scales and the natural and cultural background of a region need to be considered so that the mapping of anthropogenic relief features can contribute to an improved understanding of land‐use history. [ABSTRACT FROM AUTHOR]

Urban development practices redistribute surface materials through filling, grading, and terracing, causing drastic changes to the geomorphic organization of the landscape. Many studies document the hydrologic, biologic, or geomorphic consequences of urbanization using space-for-time comparisons of disparate urban and rural landscapes. However, no previous studies have documented geomorphic changes from development using multiple dates of high-resolution topographic data at the watershed scale. This study utilized a time series of five sequential light detection and ranging (LiDAR) derived digital elevation models (DEMs) to track watershed geomorphic changes within two watersheds throughout development (2002-2008) and across multiple spatial scales (0.01-1km²). Development-induced changes were compared against an undeveloped forested watershed during the same time period. Changes in elevations, slopes, hypsometry, and surface flow pathways were tracked throughout the development process to assess watershed geomorphic alterations. Results suggest that development produced an increase in sharp topographic breaks between relatively flat surfaces and steep slopes, replacing smoothly varying hillslopes and leading to greater variation in slopes. Examinations of flowpath distributions highlight systematic modifications that favor rapid convergence in unchanneled upland areas. Evidence of channel additions in the form of engineered surface conduits is apparent in comparisons of pre- and post-development stream maps. These results suggest that topographic modification, in addition to impervious surfaces, contributes to altered hydrologic dynamics observed in urban systems. This work highlights important considerations for the use of repeat LiDAR flights in analyzing watershed change through time. Novel methods introduced here may allow improved understanding and targeted mitigation of the processes driving geomorphic changes during development and help guide future research directions for development-based watershed studies. [ABSTRACT FROM AUTHOR]

This contribution proposes to analyze a single archaeological object crossed by different conservation processes, to isolate and understand the role of geomorphological parameters in the formation and the conservation of anthropogenic geomorphic features. For these purposes, this study characterizes the remnant morphologies of the WW1 railroad in the Montagne de Reims on the western front (France, Rheims sector) through LiDAR-derived digital terrain models combined with archival documents (maps and historical photographs), spatial analysis and field surveys. This study shows that the Heavy Railway Artillery (Artillerie Lourde sur Voie Ferrée - ALVF) is a significant morphogenic feature, preserved at more than 70%. However, this morphology is subject to a strong variability. Ruggedness analysis shows a much more pronounced morphology on the slopes than on the plateaus, demonstrating a difference in formation and preservation processes. For formation processes, the study's findings reveal that geotechnical constraints related to lithology and geomorphology can be overridden for military purposes. For conservation processes, they are largely dependent on land use practices. This research demonstrates that (i) the role of landscape and topographical parameters in the interpretability of features is predominant; (ii) military engineering has been able to adapt to a much diversified topography. • Study of WW1 railway artillery in Montagne de Reims (Marne, France). • Variability analysis of war morphologies according to their geomorphological context. • Integrated methods combining textual and cartographic archives, GIS, Lidar. • The formation and preservation of the rail footprint is driven by geomorphology. • War overrides natural processes for military purposes. [ABSTRACT FROM AUTHOR]