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Abstract Recent archaeological research in the Andes suggests that Indigenous herders carefully managed their environments through the modification of local hydrology and vegetation. However, the limited geographical scale of previous research makes it challenging to assess the range and prevalence of pastoralist land management in the Andes. In this article, the authors utilise large-scale, systematic imagery survey to examine the distribution and environmental contexts of corrals and pastoralist settlements in Huancavelica, Peru. Results indicate that corrals and pastoralist settlements cluster around colonial and present-day settlements and that a statistically significant relationship exists between pastoral infrastructure and perennial vegetation. This highlights the utility of remote survey for the identification of trans-regional patterns in herder-environment relationships that are otherwise difficult to detect. 

The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km2 of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand. 

The north coast of Peru is among the most extensively surveyed regions in the world, yet variation in research questions, sampling strategies and chronological and geospatial controls among survey projects makes comparison of disparate datasets difficult. To contextualise these issues, the authors present a systematic survey of satellite imagery focusing on hilltop fortifications in the Jequetepeque and Santa Valleys. This digital recontextualisation of pedestrian survey data demonstrates the potential of hybrid methodologies to substantially expand both the identification of archaeological sites within difficult terrain and, consequently, our understanding of the function of defensive sites. 

Archaeological surveys conducted through the inspection of high-resolution satellite imagery promise to transform how archaeologists conduct large-scale regional and supra-regional research. However, conducting manual surveys of satellite imagery is labour- and time-intensive, and low target prevalence substantially increases the likelihood of miss-errors (false negatives). In this article, the authors compare the results of an imagery survey conducted using artificial intelligence computer vision techniques (Convolutional Neural Networks) to a survey conducted manually by a team of experts through the Geo-PACHA platform (for further details of the project, see Wernkeet al. 2023). Results suggest that future surveys may benefit from a hybrid approach—combining manual and automated methods—to conduct an AI-assisted survey and improve data completeness and robustness. 

Fog oases (lomas) present pockets of verdant vegetation within the arid coastal desert of Andean South America and archaeological excavation within some of the oases has revealed a long history of human exploitation of these landscapes. Yet lomas settlements are under-represented in archaeological datasets due to their tendency to be located in remote inter-valley areas. Here, the authors employ satellite imagery survey to map the locations of anthropogenic surface features along the central Peruvian coast. They observe two categories of archaeological features, large corrals and clustered structures, and document a concentration of settlement features within lomas landscapes that suggests a pre-Hispanic preference for both short- and long-term occupation of these verdant oases. 

Imagery-based survey is capable of producing archaeological datasets that complement those collected through field-based survey methods, widening the scope of analysis beyond regions. The Geospatial Platform for Andean Culture, History and Archaeology (GeoPACHA) enables systematic registry of imagery survey data through a ‘federated’ approach. Using GeoPACHA, teams pursue problem-specific research questions through a common data schema and interface that allows for inter-project comparisons, analyses and syntheses. The authors present an overview of the platform's rationale and functionality, as well as a summary of results from the first survey campaign, which was carried out by six projects distributed across the central Andes, five of which are represented here. 

In the Andean highlands, hilltop fortifications known aspukarasare common. Dating predominantly to the Late Intermediate Period (AD 1000–1450), pukaras are important to archaeological characterisations of a political landscape shaped by conflict but the distribution of these key sites is not well understood. Here, the authors employ systematic satellite imagery survey to provide a contiguous picture of pukara distribution on an inter-regional scale covering 151 103km²in the south-central highlands of Peru. They highlight the effectiveness of such survey at identifying pukaras and capturing regional variability in size and residential occupation, and the results demonstrate that satellite surveys of high-visibility sites can tackle research questions at larger scales of analysis than have previously been possible.