Large-scale, multi-temporal remote sensing of palaeo-river networks: A case study from Northwest India and its implications for the indus civilisation

© 2018 by the authors. 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.ER

Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia

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

Hybrid MSRM-based deep learning and multitemporal Sentinel 2-based machine learning algorithm detects near 10k archaeological tumuli in north-western Iberia

This is the final version. Available on open access from MDPI via the DOI in this record.Data Availability Statement: All relevant material has been made available as Supplementary MaterialsThis 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.European Union Horizon 2020Spanish Ministry of Science, Innovation and UniversitiesFundación BBV

Mapping archaeology while mapping an empire: Using historical maps to reconstruct ancient settlement landscapes in modern India and Pakistan

A range of data sources are now used to support the process of archaeological prospection, including remote sensed imagery, spy satellite photographs and aerial photographs. This paper advocates the value and importance of a hitherto under-utilised historical mapping resource—the Survey of India 1” to 1-mile map series, which was based on surveys started in the mid–late nineteenth century, and published progressively from the early twentieth century AD. These maps present a systematic documentation of the topography of the British dominions in the South Asian Subcontinent. Incidentally, they also documented the locations, the height and area of thousands of elevated mounds that were visible in the landscape at the time that the surveys were carried out, but have typically since been either damaged or destroyed by the expansion of irrigation agriculture and urbanism. Subsequent reanalysis has revealed that many of these mounds were actually the remains of ancient settlements. The digitisation and analysis of these historic maps thus creates a unique opportunity for gaining insight into the landscape archaeology of South Asia. This paper reviews the context within which these historical maps were created, presents a method for georeferencing them, and reviews the symbology that was used to represent elevated mound features that have the potential to be archaeological sites. This paper should be read in conjunction with the paper by Arnau Garcia et al. in the same issue of Geosciences, which implements a research programme combining historical maps and a range of remote sensing approaches to reconstruct historical landscape dynamics in the Indus River Basin.ERC Consolidator Gran

Alpha helices are more robust to mutations than beta strands

The rapidly increasing amount of data on human genetic variation has resulted in a growing demand to identify pathogenic mutations computationally, as their experimental validation is currently beyond reach. Here we show that alpha helices and beta strands differ significantly in their ability to tolerate mutations: helices can accumulate more mutations than strands without change, due to the higher numbers of inter-residue contacts in helices. This results in two patterns: a) the same number of mutations causes less structural change in helices than in strands; b) helices diverge more rapidly in sequence than strands within the same domains. Additionally, both helices and strands are significantly more robust than coils. Based on this observation we show that human missense mutations that change secondary structure are more likely to be pathogenic than those that do not. Moreover, inclusion of predicted secondary structure changes shows significant utility for improving upon state-of-the-art pathogenicity predictions

Reconstructing long-term settlement histories on complex alluvial floodplains by integrating historical map analysis and remote-sensing: an archaeological analysis of the landscape of the Indus River Basin

AbstractAlluvial 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.European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no 648609). Global Challenges Research Fund’s TIGR2ESS project (BB/P027970/1)

Survey Archaeology in the Mediterranean World: Regional Traditions and Contributions to Long-Term History

In this paper, we describe the development and state of archaeological surface survey in the Mediterranean. We focus especially on surface survey as a means of documenting long-term settlement patterns at various scales, as an approach to the archaeology of regions, and as a pathway to the interpretation of past landscapes. Over the last decades, the literature on Mediterranean survey has increasingly emphasized a distinct set of practices, viewed both favorably and critically by regional archaeologists in the Mediterranean and elsewhere. We show that Mediterranean survey in fact comprises several discrete regional traditions. In general, these traditions have much to offer to wider dialogs in world archaeology, particularly concerning sampling and research design, the interpretation of surface assemblages, and the integration of complex, multidisciplinary datasets. More specifically, survey investigations of Mediterranean landscapes provide comparative data and potential research strategies of relevance to many issues of global significance, including human ecology, demography, urban–rural dynamics, and various types of polity formation, colonialism, and imperialism