Situated between the Enguri and Khobistskali rivers, more than 30 settlement mounds (locally named Dikhagudzuba) provide evidence for a relatively densely populated landscape in the coastal lowlands of western Georgia during the Bronze Age. Compared to older mounds in eastern Georgia and other regions, these mounds differ not only in age but also in their average size and spatial distribution. Based on the interpretation of nine sediment cores, drone survey and structure‐from‐motion photogrammetry techniques, our study aims at (i) establishing a chronostratigraphic framework for the mounds based on 14C dating; (ii) reconstructing possible phases and gaps in human occupation; (iii) determining potential source areas of the mounds’ construction material; and (iv) identifying the environmental conditions at the time of their use. The three investigated mounds are similar in dimension and stratigraphy. Anthropogenic layers could clearly be identified and separated from the natural alluvial deposits below. According to the 14C age estimates, the mounds date to the first half of the 2nd millennium B.C.; this confirms the archaeological interpretation of their Bronze Age origin. While only one construction phase is assumed for two of the mounds, stratigraphic analysis suggests a successive enlargement of a third mound over at least 470 years. Paleoenvironmental conditions in the vicinity of the mounds were dominated by swampy, fluvial (channel) to alluvial (overbank) processes, as attested by river‐bank deposits and floodplain alluvium.

The cover image, by Hannes Laermanns et al., is based on the Research Article Bronze Age settlement mounds on the Colchian plain at the Black Sea coast of Georgia: A geoarchaeological perspective, DOI: https://doi.org/10.1002/gea.21670.

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<p>Cultural heritage monuments, that were created by mankind for centuries are scattered throughout the world. Most of them are experiencing impacts coming from nature and humans each year that result in damage and changing their common state. Many of the monuments are facing critical conditions and require diagnostics, study and planning and management of conservation/rehabilitation works. Due to the impact of environmental factors such as temperature, humidity, precipitation, the existence of complex structure of cracks, infiltrated water and runoff water streams, together with active tectonics in the region, Uplistsikhe and Vardzia rock-cut city monuments located in Georgia face problems and permanent destruction.</p><p>We have developed continuous monitoring systems that are installed in Vardzia and Uplistsikhe.</p><p>These systems are generating large amounts of data and it is almost impossible to analyze this data using conventional methods. In parallel with technological development, it is now possible to analyze big data using machine learning. We decided to use machine learning to address our problem. This approach gave us some interesting results. We were able to detect correlations between different sensors, see anomalies in data that gave us some clues about hazard zones. Additionally  models and predictions about the monument's condition were made.</p><p>Our work shows that machine learning could be used to estimate conditions make predictions about monuments state.</p><p>This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNS) [Grant Number fr17_90]</p>

<p>A variety of weathering processes is controlled by moisture movements in porous rock. However, the quantitative assessment of small-scale moisture levels and fluctuations in-situ, over longer time periods, is still a challenge. The aim of our investigation is to close this gap with a microwave-based moisture monitoring system, installed at the cave town Uplistsikhe in Georgia, which oldest structures date back to the early Iron Age (10<sup>th</sup>-9<sup>th</sup> centuries BC).</p><p>Two morphologically different cave structures were equipped with two pairs of sensors, each covering two depth ranges, at two positions to detect different moisture contents and sources. These are considered the main driver of the highly accelerated weathering processes and decay of Uplistsikhe.</p><p>With the long moisture monitoring dataset of 12 months, combined with meteorological data from the study site, seasonal moisture variations and environmental-rock interactions are detected. Preliminary data from the first eight months of monitoring is presented.</p>