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The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features.

Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated.

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.

Alluvial floodplains were one of the major venues of the development and long-term transformation of urban agrarian-based societies. The historical relationship between human societies and riverine environments created a rich archaeological record, but it is one that is not always easy to access due to the dynamism of alluvial floodplains and the geomorphological processes driven their hydrological regimes. Alluvial floodplains are also targeted for urban and agricultural expansion, which both have the potential to pose threats to cultural heritage and the environment if not carefully managed. Analysis that combines Historical Cartography and Remote Sensing sources to identify potential archaeological sites and river palaeochannels is an important first step towards the reconstruction of settlement patterns in different historical periods and their relationship to the history of hydrological networks. We are able to use different computational methods to great effect, including algorithms to enhance the visualization of different features of the landscape; and for processing large quantity of data using Machine-Learning based methods. Here we integrate those methods for the first time in a single study case: a section of the Indus River basin. Using a combined approach, it has been possible to map the historical hydrological network in a detail never achieved before and identify hundreds of potential archaeological sites previously unknown. Discussing these datasets together, we address the interpretation of the archaeological record, and highlight how Remote Sensing approaches can inform future research, heritage documentation, management, and preservation. The paper concludes with a targeted analysis of our datasets in the light of previous field-based research in order to provide preliminary insights on how long-term processes might have re-worked historical landscapes and their potential implications for the study of settlement patterns in different Historical periods in this region, thereby highlighting the potential for such integrated approaches.

This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km 2 , the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.

Agricultural production and the palatial redistribution of staples have played a key role in the debate concerning the emergence of social complexity in Minoan Crete. However, much of the focus has fallen on major settlements where such products were consumed, rather than on the landscape where agricultural surplus was produced. While there is no shortage of landscape surveys on Crete, their emphasis has typically been on the distribution of rural settlements instead of on identifying landscape structures and arrangements—such as terraces, enclosures, and field systems—that might provide data about a territory's economic focus. A key aim of the new survey at Palaikastro has been to address this bias. By combining extensive archaeological survey with differential GPS (DGPS) measurements, high-resolution aerial photography, and microrelief generation and analysis, the project has identified hundreds of structures, forming an almost continuous fossilized landscape and providing important clues on landscape management practices. The results highlight the importance of pastoral practices, to which a large part of the landscape was dedicated. Agricultural arrangements were also documented in the form of terraced areas adapted for dryland agriculture and reflecting concerns for soil retention. We argue that a highly structured landscape, indicative of pressures in land use, was established during the Middle and Late Minoan periods across Palaikastro's territory. This article is also available as open access on AJA Online.

This article presents AgriExp, a remote-based workflow for the rapid mapping and monitoring of archaeological and cultural heritage locations endangered by new agricultural expansion and encroachment. Our approach is powered by the cloud-computing data cataloguing and processing capabilities of Google Earth Engine and it uses all the available scenes from the Sentinel-2 image collection to map index-based multi-aggregate yearly vegetation changes. A user-defined index threshold maps the first per-pixel occurrence of an abrupt vegetation change and returns an updated and classified multi-temporal image aggregate in almost-real-time. The algorithm requires an input vector table such as data gazetteers or heritage inventories, and it performs buffer zonal statistics for each site to return a series of spatial indicators of potential site disturbance. It also returns time series charts for the evaluation and validation of the local to regional vegetation trends and the seasonal phenology. Additionally, we used multi-temporal MODIS, Sentinel-2 and high-resolution Planet imagery for further photo-interpretation of critically endangered sites. AgriExp was first tested in the arid region of the Cholistan Desert in eastern Pakistan. Here, hundreds of archaeological mound surfaces are threatened by the accelerated transformation of barren lands into new irrigated agricultural lands. We have provided the algorithm code with the article to ensure that AgriExp can be exported and implemented with little computational cost by academics and heritage practitioners alike to monitor critically endangered archaeological and cultural landscapes elsewhere.

Animal mobility is a common strategy to overcome scarcity of food and the related over-grazing of pastures. It is also essential to reduce the inbreeding rate of animal populations, which is known to have a negative impact on fertility and productivity. The present paper shows the geographic range of sheep provisioning in different phases of occupation at the Iron Age site of Turó de la Font de la Canya (7th to 3rd centuries BC). Strontium isotope ratios from 34 archaeological sheep and goat enamel, two archaeological bones and 14 modern tree leaves are presented. The isotopic results suggest that sheep and goats consumed at the site were reared locally (within a few kilometres radius) during the whole period of occupation. The paper discusses the isotopic results in light of the socio-political structure of this period, as complex, strongly territorial societies developed during the Iron Age in the north-east Iberian Peninsula.

Incomplete datasets curtail the ability of archaeologists to investigate ancient landscapes, and there are archaeological sites whose locations remain unknown in many parts of the world. To address this problem, we need additional sources of site location data. While remote sensing data can often be used to address this challenge, it is enhanced when integrated with the spatial data found in old and sometimes forgotten sources. The Survey of India 1” to 1-mile maps from the early twentieth century are one such dataset. These maps documented the location of many cultural heritage sites throughout South Asia, including the locations of numerous mound features. An initial study georeferenced a sample of these maps covering northwest India and extracted the location of many potential archaeological sites—historical map mound features. Although numerous historical map mound features were recorded, it was unknown whether these locations corresponded to extant archaeological sites. This article presents the results of archaeological surveys that visited the locations of a sample of these historical map mound features. These surveys revealed which features are associated with extant archaeological sites, which were other kinds of landscape features, and which may represent archaeological mounds that have been destroyed since the maps were completed nearly a century ago. Their results suggest that there remain many unreported cultural heritage sites on the plains of northwest India and the mound features recorded on these maps best correlate with older archaeological sites. They also highlight other possible changes in the large-scale and long-term distribution of settlements in the region. The article concludes that northwest India has witnessed profound changes in its ancient settlement landscapes, creating in a long-term sequence of landscapes that link the past to the present and create a foundation for future research and preservation initiatives.

This paper discusses the evolution of human settlement in ancient Macedonia from the Neolithic to the Late Roman periods, based on the results of a new multi-disciplinary and multi-scale archaeological survey in northern Grevena (NW Greece). Building upon an unpublished (legacy) survey, we developed a GIS-structured workflow that integrates site-revisiting and surveying strategies (material collection and test pits) with multi-temporal remote-sensing analyses, offering analytical information about site distribution, characterisation, dating, and taphonomy. Notably, the new study led to a 64% increase in the number of known sites. The combined results indicate that prehistory is less represented in the surface record than historical periods, likely due to the impact of soil erosion episodes. The Late Bronze Age and Early Iron Age saw increased site numbers and the emergence of a settlement structure that characterised the area until the Hellenistic period. During the Roman period, the pattern shifted from a seemingly limited use of the landscape towards a model of more extensive habitation. This was driven by the appearance of new rural sites that introduced a land-use regime designed to support agricultural intensification by implementing anti-erosion measures, such as field terraces.