ABSTRACT This editorial marks the transition of editorial leadership at Archaeological Prospection and outlines priorities for the journal's next phase. We review the journal's 30‐year history and assess opportunities created by the convergence of machine learning, miniaturized sensors and cloud computing. We identify two primary objectives: establishing rigorous standards for reproducibility and open science, and prioritizing submissions that demonstrate how non‐invasive methods address substantive archaeological research questions. We introduce revised publication formats and address the need to expand representation beyond the journal's historically European and North American base. These changes position Archaeological Prospection as a knowledge transfer platform for 21st‐century archaeological practice.

ABSTRACT Burial mounds are key elements of Mediterranean funerary landscapes, but in intensively cultivated coastal plains their low‐relief expression is easily obscured by ploughing, levelling and rapidly changing surface conditions, making single‐date observations unreliable. This study develops a multiepoch Sentinel‐1 TOPS InSAR approach for repeatable mound‐domain recognition and disturbance‐hotspot screening at the Manicalunga–Timpone Nero necropolis near Selinunte, south‐western Sicily, Italy. Four C‐band interferometric epochs acquired between 2014 and 2025, spanning Sentinel‐1A, Sentinel‐1B and Sentinel‐1C, were analysed within DEM‐based visibility masks so that interpretation was restricted to coherence‐supported areas, while decorrelated sectors and layover/shadow zones were retained as explicit observational limits. Wrapped‐phase variability, coherence and terrain context were integrated into a covariance‐aware proxy, re‐expressed as an anchored ordinal ranking and fused across epochs to emphasise persistent rather than one‐off anomalies. The resulting multiepoch mean proxy, integrated with supervised machine‐learning‐based pixel classification (ilastik) to generate probability maps, delineates recurrent mound‐domain signatures and identifies deformation/disturbance hotspots both inside and outside the legally protected archaeological perimeter. The results show that a conservative, coherence‐gated InSAR workflow can separate persistent high‐rank anomaly structures from condition‐dependent noise in cultivated heritage landscapes. The approach also provides a transferable screening framework for archaeological prospection and conservation management, supporting targeted field verification, explicit reporting of non‐observable sectors and more robust prioritisation of monitoring actions.

Optimizing UAS thermal imaging for archaeological prospection: Effects of time of day, season, and surface conditions at a historic African American cemetery

Microgravimetry has been used in near surface investigations for detecting cavities. It has already proven its success in revealing unknown crypts and tombs in archaeological prospection. It was involved in searching for new cave spaces in karst. It was also employed in detecting void spaces in shallow-mining areas to mitigate sinkhole hazard. In our study we focus on the applicability, benefits and limitations of using the 3D Growth inversion approach for inverting the high-resolution high-precision micro-gravity data observed in undermined areas with the purpose of detecting shallow void space that could lead to sinkhole development, slow surface subsidence or collapses. Growth inversion has several free, user-specified inversion parameters that shape the Growth solution. Our case study presented here is related to sinkhole hazard due to abandoned shallow brown-coal mining under fields with agricultural activities. We pay attention to tuning these parameters for specific needs of cavity detection in terms of long and narrow shallow mining shafts.

Identifying spectral anomalies such as cropmarks is a valuable approach to detecting concealed archaeological sites. In this study, we explore a physically based modeling strategy, interpreting cropmarks as stress-induced changes in vegetation, governed by radiative transfer dynamics. Measurements were obtained from a controlled test field during two campaigns conducted 13 years apart. Using PROSAIL in both forward and inverse modes, combined with statistical modeling, we construct synthetic spectral datasets to augment limited observations, thus facilitating robust training of classification models. An ensemble of machine learning algorithms is applied, with classifiers trained on synthetic data and refined via majority voting. The models achieve detection rates exceeding 90% on earlier observations, demonstrating effective retrospective application. Results highlight the influence of plant growth phase, with peak greenness data yielding stronger performance. Interestingly, injected noise improves robustness, albeit modestly. This study establishes a reproducible pipeline for archaeological prospection, merging physical simulation with machine learning. The integration of synthetic data offers a promising solution to data scarcity, and opens pathways for applying predictive models to archive aerial and satellite imagery, thereby potentially transforming remote sensing strategies in heritage research.

ABSTRACT High‐resolution and accurate synoptic images of terrestrial topography, even in densely forested areas, have proven valuable for archaeology by enabling the identification and characterization of relief patterns associated with ancient human activities. This study presents a novel approach that integrates digital terrain models (DTMs) obtained through airborne laser scanning (ALS) from a drone, along with advanced visualization techniques (VTs) based on computer vision algorithms, evaluated using objective performance metrics. The research was conducted at the archaeological sites of Kuélap and Cambolín, belonging to the Chachapoyas culture in the Amazonas region, north‐western Peru. Seventeen VTs were applied to a DTM derived from ALS with a resolution of 0.5 m. Additionally, the mask region‐convolutional neural network (Mask R‐CNN) model in ArcGIS Pro was used for the automatic detection and segmentation of architectural features. The results indicate that the colour relief image map (CRIM) VT achieved the highest average precision score, reaching 71.89% in Kuélap and 43.54% in Cambolín. The model detected a total of 137 out of 185 reference structures in Kuélap and 53 out of 73 in Cambolín. The combination of VTs and deep learning supports archaeological prospection in areas with dense vegetation and complex topography, serving as a complementary tool to manual interpretation in the study of Chachapoya settlements.

Archaeological prospecting is pivotal to understanding human civilization’s development and past human–environment interactions (PHEIs). Synthetic aperture radar (SAR), a unique active remote sensing (RS) technology capable of penetrating vegetation and dry soil, has become an indispensable tool for large-scale archaeological surveys. This review, encompassing 45 years of progress (1980–2024), systematically chronicles SAR’s evolution—a field we term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SARchaeology</i>—from pioneering L-band discoveries to sophisticated modern techniques like high-resolution SAR (HiSAR), interferometric SAR (InSAR), polarimetric SAR (PolSAR), and multitemporal SAR analysis (MtSAR). These methods have enabled unprecedented discoveries of hidden landscapes, particularly in areas obscured by dense cover or aridity. However, SARchaeology grapples with inherent limitations we define as the “SAR gap,” driven by technical complexity and, critically, the difficulty in achieving adequate signal-to-noise ratios (SNR) for small low-backscatter archaeological targets. Traditional challenges like speckle noise, cost, and interpretation ambiguity are compounded by this fundamental issue. To bridge this gap, we propose an integrated framework of five strategic frontiers: 1) leveraging innovative long-wavelength SAR technologies (e.g., the P band) for deep penetration; 2) establishing collaborative space–air–ground networks; 3) integrating SAR with complementary RS techniques; 4) implementing artificial intelligence (AI) and machine learning (ML) for noise suppression and weak signal feature extraction; and 5) developing explainable SARchaeology (X-SARchaeology). This framework provides a definitive road map for the systematic exploration of buried civilizations in hyper-arid regions (e.g., the Silk Road and ancient Mesopotamia). By focusing on these solutions, SARchaeology is poised to transcend conventional limitations and illuminate much more of human history before its Golden Jubilee.

Archaeological Predictive Model is crucial for efficient site identification but faces challenges like high uncertainty in negative samples, limited accuracy, and simplistic analyses of site-environment interactions. This study, based on 189 Kushan-period sites in the Surkhandarya Region of Uzbekistan, introduces a kernel density estimation (KDE)-based strategy to improve negative sample selection. Five machine learning models, incorporating geomorphological, climatic, and terrain variables, were assessed for predicting site locations. SHAP (SHapley Additive exPlanations) analysis was used to investigate the relationship between environmental factors and settlement patterns. Results show that the proposed strategy significantly enhances predictive performance, with AUC and accuracy increasing by 12.1% and 14%, respectively. Random Forest outperformed other models in robustness across various conditions. Land cover, slope, and precipitation emerged as key factors influencing site distribution. This research offers a novel framework for archaeological prospection, combining machine learning with environmental analysis.

This paper presents multi-disciplinary research undertaken between 2022 and 2024 at El Riscal, the only rock art station known in Seville (Andalusia, Spain), a territory otherwise very rich in Late Prehistoric archaeological remains. Discovered in the 1980s and first published in the early 1990s, El Riscal presents engraved motifs not documented in other parts of southern Spain, where schematic-style painted rock art is prevalent. The set of methods employed in this research includes petrology, chemical characterisation (XRD, SEM-EDS), digital photogrammetry and digital image analysis, archaeological prospection and archaeoastronomy. The results throw new light on the characteristics and temporality of the site, with a pervasive significance through time. This, in turn, invites a new approach to the geographical distribution of the various traditions and styles present in Iberian rock art. This research also suggests that re-examining previously published rock art sites could lead to a better understanding of styles, chronologies and traditions.

ABSTRACT Archaeological remnants rarely exhibit distinctive structural or spectral characteristics, which makes their detection via remote sensing in tropical environments challenging due to significant fluctuations in soil composition, moisture levels, vegetation and land use. This study analyses multi‐temporal SPOT‐5/7 satellite data (2006–2017) and DJI P4‐RTK multispectral UAV imagery (2024) to (i) characterize the spectral behaviour of established archaeological proxies at the Sungai Batu (Batu River) complex in Bujang Valley, Kedah, Malaysia, and (ii) identify an optimal spectral band (OSB) for archaeological prospection in humid tropical conditions. Twenty‐five proxies encompassing ritual structures ( Candi ), ancient jetties, Syahbandar (administrative) locations and iron‐smelting remnants ( Tuyères ) were identified from excavation records and plotted as regions of interest (ROIs) on pre‐excavation SPOT‐5 imagery (2006). We derived top‐of‐atmosphere reflectance values for each band, calculated mean–variance statistics and modelled proxy reflectance relationships using linear regressions. Additionally, we propose a straightforward band‐sum modification (per‐proxy linear model) to mitigate spectral heterogeneity caused by non‐target background effects. Among the 25 proxies, the near‐infrared (NIR) had the highest raw reflectance; nevertheless, the Green band (≈0.50–0.59 μm) consistently yielded the most robust proxy‐reflectance correlations ( R &gt; 0.95 post‐adjustment), outperforming the Blue, Red and NIR bands. The most significant improvements following the adjustment were observed in the Syahbandar and iron‐smelting proxies ( R 2 reaching 0.98–0.99), whereas the Candi and ancient jetties proxies maintained stable, monotonic reflectance curves consistent with moist soil vegetation backgrounds. The research attributes the Green‐band's advantage to vegetation structure, soil moisture and the surface contrast derived from brick materials within the context of monsoon tropical phenology. The findings endorse the Green band as the most practical operational spectral band for preliminary assessment in Sungai Batu and similar low‐relief, forested tropical locations when only three to four multispectral bands are accessible. The work ultimately suggests implications for conducting low‐cost surveys and advocates for future testing with hyperspectral sensors and machine learning classifiers.

ABSTRACT Advances in low‐altitude remote sensing are needed to improve the effectiveness of archaeological prospection in the Netherlands. The geomorphological situation and land use history make applying various remote sensing and geophysical technologies particularly challenging. Technologies developed for precision agriculture have improved the field. However, the technology has yet to deliver reliable results when used in archaeological prospection in the Netherlands. The Parrot Sequoia multispectral camera was used to study a known archaeological site near Raalte, the Netherlands, to investigate the particular influence of land use practices on archaeological vegetation mark visibility. The site was beneficial for this purpose because the archaeological structures beneath the surface are well known. Also, the hydrological situation is relatively constant throughout the year. This site, therefore, is the perfect test site because certain variables are known constants. The four missions resulted in 24 ortho‐mosaics, of which the RGB, the NDVI and the SR datasets were selected for further analysis. In the RGB datasets, vegetation marks were only visible in the August dataset. Vegetation marks were recognized in the June and August NDVI and SR datasets. All of the detected vegetation marks in the NDVI and SR datasets were visible at times shortly after the grass had been mown, and none were visible when the grass was at the peak of its growth. This study has made a contribution to better understanding the visibility of vegetation marks regarding their timing in the land use cycle of meadows and pastures.

ABSTRACT Drone‐based magnetometry has demonstrated high effectiveness in verifying satellite observations and detecting new archaeological features. In this study, we evaluate its application for archaeological prospection in the Northern Black Sea region, focusing on the previously documented Roman fort Kamianka V and one newly identified fort. DJI Agras T30 drones equipped with SENSYS MagDrone R3 magnetometers were used to survey areas of 14.0 and 1.6 ha on two sites. The magnetometer was suspended on a 2.0‐m extension boom to minimize interference from the drone's electronics and to maintain the sensors' altitude of 1.0 m above the ground. The achieved survey accuracy, estimated as ±1.05 nT, enabled reliable detection of ditch structures—key elements of Roman fortifications—based on anomalies ranging from 1 to 12 nT. Comparison with the ground‐based magnetometer survey demonstrates a close correspondence in the observed magnetic patterns. The layout of the Kamianka V fort, which is atypical for Roman military architecture, has been confirmed—featuring two internal forts enclosed within a single outer defensive system. The New fort was identified as a square‐shaped positive magnetic anomaly consistent with a Roman‐period enclosure. The study demonstrates the strong potential of drone‐based magnetometry for large‐area, high‐resolution archaeological mapping in the steppe environment, including the detection of subtle earthen structures often obscured by intensive agricultural land use. These findings contribute to ongoing research on Roman military presence in the Northern Black Sea region and illustrate how drone‐based geophysical solutions can expand the spatial and historical understanding of frontier systems.

This study presents a geophysical investigation aimed at identifying Ptolemaic Period tombs and associated archaeological structures at Al-Dyabat archaeological hill, near Akhmim City, Egypt. The site gained international attention following the 2018 discovery of the tomb of the Priest Tutu. Combined ground magnetic surveying and ground-penetrating radar (GPR) were used at the promising selected site. Integrated analysis revealed subsurface anomalies interpreted as tomb chambers, mudbrick walls, and possible limestone coffins, located at depths between 0.2 and 3 m. The combination of magnetic and GPR data significantly enhanced the detection capabilities and structural resolution, demonstrating the effectiveness of multimodal geophysics in archaeological contexts. Beyond its regional relevance, this work offers a transferable model for non-invasive archaeological prospection in sensitive heritage zones worldwide. It underscores the value of geophysical integration in cultural heritage preservation, offering insights into best practices for sustainable and non-destructive archaeological exploration.

One of the most significant bellicose events of the Viceroyal period in the Kingdom of New Galicia, which today is Western Mexico, was the Mixton War (1541-1542), fought by some groups of Indigenous people from the Northern part of New Galicia, among them the Caxcans, also known as Teules Chichimec. These were moments of tension and instability that brought about a great military mobilization, forming the largest Novo-Hispanic army headed by Viceroy Antonio de Mendoza, who personally went to the area in conflict, after the death of Pedro de Alvarado in one of the battles of this insurrection, an unfortunate event that took place. This war is documented in the historical sources with large gaps in their information, among the details are announced the different spaces where bloody battles took place, in some cases with detailed descriptions, but other narrations are extremely vague, which is why several questions arise respect to the location of the places of the clashes. On this occasion, the discussion on these spaces offers their location following the methodology of battlefield archaeology: revision of historical accounts and cartography, landscape analysis, and archaeological prospection.

We investigate the applicability of visual foundation models, a recent advancement in artificial intelligence, for archaeological remote sensing. In contrast to earlier approaches, we employ a strictly zero-shot methodology, testing the hypothesis that such models can perform archaeological feature detection without any fine-tuning or other adaptation for the remote sensing domain. Across five experiments using satellite imagery, aerial LiDAR, and drone video data, we assess the models’ ability to detect archaeological features. Our results demonstrate that such foundation models can achieve detection performance comparable to that of human experts and established automated methods. A key advantage lies in the substantial reduction of required human effort and the elimination of the need for training data. To support reproducibility and future experimentation, we provide open-source scripts and datasets and suggest a novel workflow for remote sensing projects. If current trends persist, foundation models may offer a scalable and accessible alternative to conventional archaeological prospection.

ABSTRACT This study investigates the effectiveness of drone‐based remote sensing and Google Earth satellite imagery for archaeological prospection in the Bayan Gol Valley, Central Mongolia. Utilizing a fixed‐wing unmanned aerial vehicle (UAV) equipped with RGB and multispectral sensors, we surveyed 655 ha to document Mongol‐period settlement structures dating to the 13th and 14th centuries ce . The resulting high‐resolution datasets, including digital surface models, RGB orthomosaics and vegetation index rasters, were compared with five image sets from 2007 to 2021 provided by Google Earth to assess their respective capabilities. The UAV‐derived data proved significantly more effective, identifying 16% more archaeological features than the satellite imagery. However, Google Earth imagery provided valuable cost‐efficient and multitemporal contextual information. The analysis also considers the impact of modern disturbances, such as agricultural activity and road construction, on feature visibility. These findings emphasize the complementary strengths of elevation and vegetation‐based anomaly detection and highlight the value of integrated remote sensing approaches for archaeological mapping in complex and dynamic landscapes. The study contributes to the refinement of UAV methodologies and the broader application of remote sensing in archaeological research.

Over the last three decades, archaeological research has made relevant advances in the study of cultural heritage related to the local societies of the Basque Country. Two aspects, in particular, have provided very interesting results for the study of rural contexts: industrial activity, namely ferrous metallurgy; and individual farmsteads (baserriak), which articulated different economic activities related to agriculture, and husbandry. This work proposes a revision of these works from a landscape perspective, in order to gain new insights into the relationship between social and environmental factors at the local scale. To this purpose, the case of the Oma valley is analysed (Kortezubi, Biscay – Basque Country) through a combination of different sources of information: written, oral, cartographic, archaeological prospection, and geoarchaeology. The results of this work show the potential of this approach for a deeper understanding of the multifunctional socio-environmental systems that shaped cultural landscapes, but also some methodological limits. In any case, the heritage values of these landscapes are evidenced, and new research would be useful to increase the evidence available.

ABSTRACT Preliminary geophysical investigations are a cost‐effective and efficient way to screen archaeological sites and locate buried structures. Ground‐penetrating radar (GPR) is one of the most widely used methods for archaeological prospection, but in some sites, it cannot be employed effectively due to the presence of clay or other electrically conductive materials, which strongly attenuate the electromagnetic signal, or due to bumped terrain, which demands rigorous signal analysis. Alternatively, electrical resistivity tomography (ERT) can be adopted in these situations. However, ERT is not as frequently adopted as GPR for archaeological purposes because it is more time and cost consuming and, generally, has worse resolution. In this study, we aim to test a full‐3D ERT approach to improve the imaging quality of ERT surveys for archaeological prospections. We develop specific survey strategies, including a custom open‐source quadrupole sequence generator, studied for achieving high sensitivity to archaeological remains within the first metres of subsoil. We performed a test survey on a well‐known archaeological site (the Roman town of Augusta Bagiennorum, NW Italy) and compared the results with a state‐of‐the‐art multichannel GPR acquisition. The results showed that both GPR and ERT equally located the outer and inner walls of a complex Roman residential building. Moreover, the ERT could locate two targets, barely visible in the GPR survey, with the antenna used. We also compared the results of our full‐3D ERT approach to a more common quasi‐3D approach. We found that the full‐3D approach overcomes the directional bias found in our quasi‐3D acquisitions and provides a more accurate subsurface resistivity model. This methodology is ready to be employed in other archaeological sites and, differently from GPR, can easily operate on bumped terrain, in the presence of clay, and potentially reach greater investigation depths.

We present the results of a Ground-Penetrating Radar (GPR) survey conducted at the archaeological site of Bisya and Salūt (Sultanate of Oman), aimed at assessing archaeological risk associated with the planned infrastructural development of the site. The survey employed a dual-frequency GPR system with a survey rugged cart to adapt to the varying conditions of the area. The survey was designed around a scale-adaptive grid strategy, across three sectors, combining medium- and low-definition acquisitions over broader areas to identify zones with low archaeological potential, with a high-density grid near previously excavated structures. Data interpretation was integrated with Geographic Information System (GIS)-based spatial mapping, allowing the definition of a parametric risk indicator for subsurface archaeological potential derived from radar facies characterisation and point-by-point anomaly analysis along GPR profiles. Within the area of higher density, the method successfully mapped buried alignments suggestive of anthropogenic features. The results confirmed the effectiveness of GPR as a predictive tool for archaeological prospection, particularly when combined with spatial analysis. Overall, this study highlights the feasibility of incorporating non-invasive methods into heritage protection strategies, contributing to the sustainable development and planning of archaeological landscapes.

The study described below aims to confirm the potential of UAV-based multispectral imagery as a flexible and cost-effective tool to detect possible buried archaeological structures, expanding upon previous approaches based on satellite or traditional airborne data. In parallel, the authors investigate the role of such imagery within a conjectured workflow that incorporates multispectral analysis as a preliminary, extensive, and non-invasive step in archaeological prospection strategies. The study evaluates the performance of a commercial sensor and analyses spectral signatures by generating index maps within the significant context of Iulia Felix Praedia in Pompeii (Italy). A significant opportunity was the possibility of acquiring multispectral data in the hortus area, previously investigated through non-invasive geophysical surveys and archaeological excavations. The UAV photogrammetric flight, as well as the subsequent analyses, focused on the visual interpretation and geolocated examination of vegetation and soil index maps, accurately selected among those available, considering the UAV-acquired band dataset. This approach enhanced the features of the complex hortus environment, where natural elements alternate with numerous man-made structures. These analyses led to the detection of anomalies consistent with those previously identified by the aforementioned investigations, alongside additional anomalies distributed across the study area. The detected anomalies were further analysed and synthesised; this involved generating a confidence map based on the frequency of anomaly occurrence across the analysed index maps. The consistency between detected anomalies and previous investigations’ results underlines the potential for continued research on processing multispectral data captured by UAVs. Thanks to such data, a valuable alternative to satellite imagery was provided due to their much higher spatial resolution, enabling rapid and cost-effective campaigns to plan more targeted geophysical and archaeological investigations. The findings also validate the hypothesised workflow involving the use of multispectral imagery as a preliminary, extensive, and non-invasive tool to define excavation areas’ perimeters and, subsequently, guide targeted analyses.