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This study presents preliminary results from recent bathymetric LiDAR-guided surveys of submerged archaeological landscapes in the Apalachee Bay off the coast of Florida. We show how bathymetric LiDAR can re-identify previously recorded archaeological sites and identify new cultural deposits at shallow depths and help aid SCUBA surveys of submerged environments. While most prior archaeological applications of bathymetric LiDAR have focused on shipwrecks and historic era sites, our case study demonstrates that bathymetric LiDAR is capable of detecting Holocene and Pleistocene era archaeological sites as well. Detecting and eventually characterizing these ancient deposits will greatly expand our understanding of settlement trends when sea levels were lower and may provide insights into how some of the earliest coastal populations adapted to this novel and changing environment. Our SCUBA surveys also elucidate the impact of local environmental conditions of the applicability of deploying bathymetric LiDAR; specifically, eel grass cover does not hinder LiDAR capabilities, while high rates of sedimentation greatly reduce success in identifying archaeological deposits. Overall, our results show promise in the future of applying remote sensing to study shallow submerged archaeological landscapes, which can help improve our understanding of human–environment dynamics prior to and during periods of sea level change. [ABSTRACT FROM AUTHOR]

Over the past several centuries, the iron industry played a central role in the economy of Sweden and much of northern Europe. A crucial component of iron manufacturing was the production of charcoal, which was often created in charcoal piles. These features are visible in LiDAR (light detection and ranging) datasets. These charcoal piles vary in their morphology by region, and training data for some feature types are severely lacking. Here, we investigate the potential for machine automation to aid archaeologists in recording charcoal piles with limited training data availability in a forested region of Jönköping County, Sweden. We first use hydrological depression algorithms to conduct a preliminary assessment of the study region and compile suitable training data for charcoal production sites. Then, we use these datasets to train a series of RetinaNet deep learning models, which are less computationally expensive than many popular deep learning architectures (e.g., R-CNNs), allowing for greater usability. Together, our results demonstrate how charcoal piles can be automatically extracted from LiDAR datasets, which has great implications for improving our understanding of the long-term environmental impact of the iron industry across Northern Europe. Furthermore, our workflow for developing and implementing deep learning models for archaeological research can expand the use of such methods to regions that lack suitable training data. [ABSTRACT FROM AUTHOR]

Landscape archaeology has a long history of using predictive models to improve our knowledge of extant archaeological features around the world. Important advancements in spatial statistics, however, have been slow to enter archaeological predictive modeling. Point process models (PPMs), in particular, offer a powerful solution to explicitly model both first- and second-order properties of a point pattern. Here, we use PPMs to refine a recently developed remote sensing-based predictive algorithm applied to the archaeological record of Madagascar's southwestern coast. This initial remote sensing model resulted in an 80% true positive rate, rapidly expanding our understanding of the archaeological record of this region. Despite the model's success rate, it yielded a substantial number (~20%) of false positive results. In this paper, we develop a series of PPMs to improve the accuracy of this model in predicting the location of archaeological deposits in southwest Madagascar. We illustrate how PPMs, traditional ecological knowledge, remote sensing, and fieldwork can be used iteratively to improve the accuracy of predictive models and enhance interpretations of the archaeological record. We use an explicit behavioral ecology theoretical framework to formulate and test hypotheses utilizing spatial modeling methods. Our modeling process can be replicated by archaeologists around the world to assist in fieldwork logistics and planning. [ABSTRACT FROM AUTHOR]

A vast majority of the archaeological record, globally, is understudied and increasingly threatened by climate change, economic and political instability, and violent conflict. Archaeological data are crucial for understanding the past, and as such, documentation of this information is imperative. The development of machine intelligence approaches (including machine learning, artificial intelligence, and other automated processes) has resulted in massive gains in archaeological knowledge, as such computational methods have expedited the rate of archaeological survey and discovery via remote sensing instruments. Nevertheless, the progression of automated computational approaches is limited by distinct geographic imbalances in where these techniques are developed and applied. Here, I investigate the degree of this disparity and some potential reasons for this imbalance. Analyses from Web of Science and Microsoft Academic searches reveal that there is a substantial difference between the Global North and South in the output of machine intelligence remote sensing archaeology literature. There are also regional imbalances. I argue that one solution is to increase collaborations between research institutions in addition to data sharing efforts. [ABSTRACT FROM AUTHOR]