Shadow marks

Short description: Shadows on the ground that indicate buried archaeological structures.

Shadow marks

Aerial view showing shadow marks over Maiden Castle hillfort

Type| Archaeological surface feature
Visibility conditions| Low sun angle (morning or afternoon), clear skies
Detection methods| Aerial photography, multispectral imaging, SAR, LiDAR
Related techniques| Crop marks, soil marks, NDVI, GPR

Shadow marks are surface patterns formed when low-angle sunlight casts elongated shadows across slight variations in ground elevation, revealing buried or eroded features otherwise invisible at ground level. [1] Commonly observed through aerial photography or satellite imagery, shadow marks assist archaeologists in identifying ancient structures, earthworks, and landscape modifications.[2] Their visibility depends on lighting angle, surface reflectance (albedo), and environmental conditions such as vegetation or cloud cover.[3] Shadow marks differ from crop or soil marks in that they rely on topographic contrast rather than biological or chemical changes.[4][5] Modern remote sensing techniques—such as LiDAR, NDVI, and Synthetic Aperture Radar (SAR)—are often integrated with shadow mark analysis to improve accuracy and overcome environmental limitations.[6][7][8] Recent developments also include AI-assisted image classification and virtual light simulations to enhance detection.[9] Beyond archaeology, shadow marks are applied in geomorphology, heritage conservation, and battlefield studies, and continue to be a key proxy in multi-sensor approaches to landscape interpretation.[1]

## Contents

* 1 Description
* 2 Light and shadow
* 3 Remote sensing applications
* 3.1 Case studies
* 3.2 Limitations and solutions
* 4 AI applications
* 5 See also
* 6 References

## Description

The formation mechanism of shadow marks demonstrates that the appearance of shadows on the ground is closely related to the angle of the sun.

Shadow marks are surface-visible patterns that emerge due to minor variations in terrain elevation when sunlight strikes at a low angle.[1] These differences in elevation cast elongated shadows, allowing subtle topographic irregularities—such as ancient walls, ditches, or mounds—to be visualized from above.[2] Unlike crop marks, which result from vegetation stress, or soil marks, which emerge from changes in soil texture or color, shadow marks rely primarily on physical relief and light geometry.[4][1] As such, they serve as a valuable indicator of buried or eroded archaeological features that are not detectable at ground level.[1]

The visibility of shadow marks is highly sensitive to lighting conditions.[1] Early morning and late afternoon are ideal times for observation, as the sun is low on the horizon and shadows are longer, enhancing the contrast between elevations.[1] Even slight undulations in terrain can become apparent under such lighting, particularly in winter months when vegetation is sparse and light angles are optimal.[1] Archaeologists frequently capture aerial photographs during these times to take advantage of the enhanced shadow definition. In flat landscapes, where surface features are often minimal, the interplay between light and surface texture can exaggerate minor topographic variations, making shadow marks particularly effective.[7]

However, the clarity of shadow marks can be influenced by various surface conditions.[5] Vegetation cover, ploughed fields, soil moisture, and cloud shadows all affect how light interacts with the ground and, consequently, the visibility of these marks.[5][6][7] For this reason, shadow mark detection often requires repeated aerial surveys under differing environmental and seasonal conditions. In lowland or arid zones, temporary surface features or infrastructure may complicate interpretation, requiring careful visual analysis.[5]

Despite their sensitivity to environmental factors, shadow marks remain a crucial tool for archaeological prospection. Their topographic basis complements other remote sensing techniques, enabling the detection of features that may not produce biological or chemical contrasts.[7] When integrated with additional methods, shadow marks help form a composite view of subsurface landscapes and have proven especially useful in revealing long-buried cultural features and terrain modifications.[1]

## Light and shadow

The formation of shadow marks relies on the basic principles of physics and optics, especially how light interacts with differences in the earth's surface topography.[1] When sunlight strikes an uneven surface at a low angle, it casts long shadows that contrast high and low areas more.[1] This optical effect is integral to observing slight archaeological features since it permits a visualization of the ground’s irregularities that otherwise would not be apparent under direct, overhead illumination conditions.[7]

This aerial picture shows Maiden Castle, a prehistoric site located in Dorset, which was taken from the west in 1937. The low angle of the light creates long shadows across the earthworks, and the long shadows highlight the fort's intricate ramparts and ditches. This photo demonstrates shadow marks in aerial archaeology, where slight variations are made visible through the effects of shadow and light on the landscape.

The angle of solar illumination is the primary agent for creating shadow marks. In the morning and the evening (late afternoon), the sun is low in the sky, and shadows are longer, meaning even small ground undulations are apparent.[1] Because of this, archaeologists will conduct aerial surveys during this time to best use shadow marks. However, the effectiveness of shadow marks depends upon the latitude, season, and atmosphere, each of which can affect the scattering of light and shadow clarity.[1]

Albedo, or surface reflectance, is also a key factor in photographs or in other records of shadow marks. Different materials have different albedos with regard to both absorption and reflection of incoming light and thus vary in their ability to create contrast, which may either increase or obscure the shadows.[3] For example, when a buried stone structure or wall has changed the physical or chemical properties of the surrounding soil—whether that is by compacting it or influencing moisture retention—the area above is likely to reflect sunlight differently than surrounding, unaltered soils.[10] The variations in surface reflectance, or albedo, sometimes will be observable in aerial photographs as differences in tonal contrast or shadow intensity.[10] A ploughed field is an example of an exposure that can reveal faint outlines of a buried foundation in the soil due to slight differences in shadow or color even if the foundation remains hidden underground.[10] Furthermore, the moisture content may reflect light differently, thus making shadow marks more or less detectable depending on prior weather conditions.[7]

## Remote sensing applications

Shadow marks, as elevation-dependent surface indicators, play a central role in archaeological surveys by visually exposing buried or eroded structures.[11] While their visibility largely depends on lighting—previously discussed—they are especially effective in identifying fortifications and earthworks, particularly in pre-modern agricultural landscapes where terrain alterations are preserved.[1][4][10]

Kite aerial photo of the site of Ogilface Castle, Woodend, West Lothian in low sunlight

Aerial photography has been the primary method of recording shadow marks, particularly in early 20th-century archaeological explorations.[11][12] Early archaeologists recorded shadow marks via images captured manually in the early morning or evening to highlight the contrast of landscape changes using a traditional camera or aerial photography.[11] Technology in remote sensing has improved this process by developing high-resolution satellite imagery, surveying with drones, and complimentary surveys using airborne Light Detection and Ranging (LiDAR).[6] LiDAR has been a significant advancement in the detection of shadow marks, as its main contribution is the ability to generate a Digital Terrain Model (DTM) that highlights slight variations in the topography that were previously hidden because of dense vegetation.[11]

### Case studies

Real-world case studies demonstrate the practical value of shadow marks in archaeological detection. For instance, aerial surveys over Maiden Castle in England revealed complex fortification structures through shadow-enhanced topography.[13] Similarly, in China, SAR imagery successfully detected shadow patterns of buried city walls beneath agricultural fields.[8]

These examples highlight how shadow marks often provide the first visual cue of subsurface features—especially when crop and soil marks offer limited information.[14][7] Shadow marks are the most informative when they are used along with other remote sensing methodologies.[14][15] When combined with other forms of remote sensing, shadow marks help triangulate archaeological interpretations through complementary visual, biological, and physical indicators.[14]

Archaeologists today are therefore placing an emphasis on a multiproxy approach. As researchers use shadow mark analysis paired with LiDAR (topography), GPR (subsurface readings), and multispectral band analyses (landscape development); they categorize between anthropogenic and natural features.[16] As more data goes into Geographic Information Systems (GIS), but shadow marks could also be placed in GIS, the spatial analysis of archaeological sites can improve predictive modeling and our ability to reconstruct cultural landscapes.[17] Shadow marks will remain important - even if they are a relatively low technology - in the expanding toolbox of remote sensing archaeology.[11]

### Limitations and solutions

While shadow marks can enhance the visibility of ancient features under ideal lighting conditions, they are also highly susceptible to environmental interference.[5] Cloud shadows, uneven terrain, vegetation, and surface modifications—such as roads or ploughing—can all distort or obscure shadow patterns, making interpretation less reliable.[5][7] These limitations necessitate the use of advanced remote sensing techniques and multi-sensor methodologies.[7] Passive optical imaging via aerial technology has also improved shadow mark interpretation.[6] Multispectral and hyper-spectral imaging makes it possible to filter some atmospheric interferences, such as cloud shadows or remove them and preserve archaeological patterns, as it does compensate for shadow marks on the earth’s surface when recording sunlight and manipulating shadows.[18] A multispectral light dome also allows archaeologists to simulate sun angles for interpreting shadow marks in digital reconstructions.[3] In addition, synthetic lighting simulations can be developed to create shadow conditions artificially, which gives archaeologists the potential capabilities to manipulate digital terrain features that may not be possible in situ.[19]

The environment and seasonal factors can still influence shadow marks, as archaeologists work in areas with frequent cloud cover or shifts in shadow angle of periodical archaeological features.[1][11] Likewise, although perfect air and vegetation conditions may be present, modern infrastructure, roads, and contemporary urban development may also distort or compact the shadow marks footprints, making interpretation increasingly difficult.[20] To address these, archaeologists now routinely use an integrated methodology, utilizing shadow-marked analysis alongside ground-penetrating radar (GPR) and geomorphological survey applications to verify their past interpretations.[11] These much-relied-upon interdisciplinary techniques and methodologies provide higher accuracy in the census of archaeological sites and ultimately verify the power of shadow marks in remote sensing applications for archaeology.[11]

In recent applications, spectral indices (such as band ratios and NDVI) have been used to diminish contrived impacts of cloud shadows, which obscure archaeological features including both crop marks and moist marks.[7] These indices help counteract not only atmospheric effects, such as cloud interference, but also transient natural shadows that can originate from vegetation growth or passing weather systems.[7] Using these approaches can improve the visibility of less visible circular marks, including those that result from buried ditches.

In addition, synthetic aperture radar (SAR) technology—particularly using COSMO-SkyMed X-band data—has demonstrated the potential to improve the identification of shadow marks under challenging environmental conditions.[21] When SAR data is layered with optical imagery, the combined approach significantly enhances detection reliability—particularly in arid or densely vegetated regions where optical methods alone are insufficient.[5] Using both multi-temporal averaging and single-date enhancements (including speckle filtering and morphological processing), they sought to improve detection of microrelief marks and structures below the surface.[21] Two advantages included that:

1. Noise can (and was) suppressed while effectively preserving weak signals associated with archeological features or treatments;
2. Radar imaging for sub-surface analysis can complement existing optical methods (and in some situations, exceed optical methods) when visibility is constrained.[21][15]

Future studies of shadow optics will likely include imaging in real-time adaptive conditions, where agents (e.g., sensors) are tuned by an AI to adjust quickly to the conditions of light.[22] Hyperspectral imaging and improvements in LiDAR will increase the accurate classification of shadows and reduce false positives and errors. Merging physics outcomes and imaging methods will continue to the limits and interpreting shadow marks effectively as a critical framework of remote sensing and archaeologically detecting earth-based sites and features.[2]

## AI applications

The collaboration of shadow mark analysis with artificial intelligence (AI) has paved the way for new opportunities in archaeological remote sensing.[9] Shadow mark analysis had traditionally relied on the interpretation of aerial photographs to identify archaeological sites. Still, recent technological advances in machine learning and computer vision have led to automated shadow mark analysis with greater efficiency and accuracy. AI-based approaches can allow researchers to analyze countless datasets of aerial and satellite images to identify shadow marks with minimal human involvement.[9]

An evident and prominent development in this area is the deployment of convolutional neural networks (CNN) in detecting and classifying shadow marks.[23] CNNs can distinguish between genuine archaeological shadow features and other artifacts caused by clouds, vegetation, or urban features.[23] Moreover, researchers have recently demonstrated improved accuracy in the automated detection of shadow marks by training AI models with datasets containing known archaeological sites.[9] Researchers have recently used unsupervised learning methods such as clustering algorithms to segment aerial imagery and extract shadow features of possible underground features.[9][23]

Another significant development involves applying 3D photogrammetry and light simulation to improve the visibility of shadow marks. Virtual reconstructions of lighting conditions allow researchers to simulate and recreate both sun angles and the corresponding shadows, allowing them to visualize how an archaeological site would appear under different lighting circumstances. This technique has been beneficial in instances when real-world shadow marks cannot be detected due to seasonal or weather conditions.[19]

AI-driven remote sensing has also begun to employ multi-spectral data processing concurrently with shadow analysis.[9] By applying spectral indices like the Normalized Difference Vegetation Index (NDVI), researchers can explore patterns in an object’s shadow mark, establishing a means to differentiate between shadows associated with burial remains from those generated by vegetation differences.[9] The separation allows researchers to filter out false positive occasions, particularly in dense forest areas where the distinction between tree and archaeological shadows can be problematic.[23]

These AI methodologies for shadow mark detection are still limited by their reliance on high-quality training datasets that are not always available throughout the world.[9] Another concern is that variable landscapes can also complicate and further diversify the types of shadows produced over time, meaning that AI processes of shadow recognition will require continuous development and improvement.[23] Regardless, as more datasets become available, it is anticipated that AI methodologies will become a routine part of chronological remote sensing and shadow analysis for archaeological inquiry.[9][23]

## See also

* Aerial archaeology
* Archaeological field survey
* Remote sensing
* Cropmark
* Soilmark

## References

1. ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 Stefano, Campana (2016). "Archaeology, remote sensing". Encyclopedia of Geoarchaeology. Dordrecht/Heidelberg/New York/London: Springer (2016): 5–6. https://d1wqtxts1xzle7.cloudfront.net/49462117/Enciclopedia_GeoArchaeoloy_Springer_MENABO_low_res-libre.pdf?1475970604=&response-content-disposition=inline;+filename=Archaeology_Remote_Sensing_Encyclopedia.pdf&Expires=1746686598&Signature=Wyw0vxrekpWa0xHtrM0iaZZNo0ShI~rVMiqytKzurQfMhO1n1nxD7e1ZbUgMRQsjncwoiGFyTJlzaytCJ9u9ab~MhJGkazldEdaoFQDQy-XsFVwU5J3~YwLEDSK5DwngKbFRmQBZbVUEAnNCAAIoYlgyU-rZkxr3zgtI-VNKnKrQ8VddbcbhpcYDMF8Pnwhee-VyofgsI4hu8oxXVM4eJp-p0KG9k7XoyGLf1cH8tJdrxD0Yg1juN1P0gWM9VN70QyRGqR7qBHVzIjiugXlyX~Nrqu9lRbo2fn5ejqRprO1rQpX21u8V2T4wHx4ArQ9dD7xKt0opYS~Edcer5NEJdQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA.
2. ↑ 2.0 2.1 2.2 Chen, Fulong; Masini, Nicola; Yang, Ruixia; Milillo, Pietro; Feng, Dexian; Lasaponara, Rosa (2014-12-23). "A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data" (in en). Remote Sensing 7 (1): 27. doi:10.3390/rs70100024. ISSN 2072-4292. Bibcode: 2014RemS....7...24C.
3. ↑ 3.0 3.1 3.2 Watteeuw, Lieve; Hameeuw, Hendrik; Vandermeulen, Bruno; Van der Perre, Athena; Boschloos, Vanessa; Delvaux, Luc; Proesmans, Marc; Van Bos, Marina et al. (November 2016). "Light, shadows and surface characteristics: the multispectral Portable Light Dome" (in en). Applied Physics A 122 (11): 976. doi:10.1007/s00339-016-0499-4. ISSN 0947-8396. Bibcode: 2016ApPhA.122..976W. http://link.springer.com/10.1007/s00339-016-0499-4.
4. ↑ 4.0 4.1 4.2 "Crop Marks and Soil Marks". https://www.metaldetectingworld.com/metaldetecting_research_p61.shtml.
5. ↑ 5.0 5.1 5.2 5.3 5.4 5.5 5.6 Chen, Fulong; Masini, Nicola; Yang, Ruixia; Milillo, Pietro; Feng, Dexian; Lasaponara, Rosa (2014-12-23). "A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data" (in en). Remote Sensing 7 (1): 30–37. doi:10.3390/rs70100024. ISSN 2072-4292. Bibcode: 2014RemS....7...24C.
6. ↑ 6.0 6.1 6.2 6.3 Stefano, Campana (2016). "Archaeology, remote sensing". Encyclopedia of Geoarchaeology. Dordrecht/Heidelberg/New York/London: Springer (2016): 6–19. https://d1wqtxts1xzle7.cloudfront.net/49462117/Enciclopedia_GeoArchaeoloy_Springer_MENABO_low_res-libre.pdf?1475970604=&response-content-disposition=inline;+filename=Archaeology_Remote_Sensing_Encyclopedia.pdf&Expires=1746686598&Signature=Wyw0vxrekpWa0xHtrM0iaZZNo0ShI~rVMiqytKzurQfMhO1n1nxD7e1ZbUgMRQsjncwoiGFyTJlzaytCJ9u9ab~MhJGkazldEdaoFQDQy-XsFVwU5J3~YwLEDSK5DwngKbFRmQBZbVUEAnNCAAIoYlgyU-rZkxr3zgtI-VNKnKrQ8VddbcbhpcYDMF8Pnwhee-VyofgsI4hu8oxXVM4eJp-p0KG9k7XoyGLf1cH8tJdrxD0Yg1juN1P0gWM9VN70QyRGqR7qBHVzIjiugXlyX~Nrqu9lRbo2fn5ejqRprO1rQpX21u8V2T4wHx4ArQ9dD7xKt0opYS~Edcer5NEJdQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA.
7. ↑ 7.00 7.01 7.02 7.03 7.04 7.05 7.06 7.07 7.08 7.09 7.10 Masini, Nicola; Lasaponara, Rosa (2017), Masini, Nicola; Soldovieri, Francesco, eds., "Sensing the Past from Space: Approaches to Site Detection" (in en), Sensing the Past: From artifact to historical site (Cham: Springer International Publishing): pp. 23–60, doi:10.1007/978-3-319-50518-3_2, ISBN 978-3-319-50518-3, https://link.springer.com/chapter/10.1007/978-3-319-50518-3_2, retrieved 2025-05-08
8. ↑ 8.0 8.1 Chen, Fulong; Masini, Nicola; Yang, Ruixia; Milillo, Pietro; Feng, Dexian; Lasaponara, Rosa (2014-12-23). "A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data" (in en). Remote Sensing 7 (1): 24–50. doi:10.3390/rs70100024. ISSN 2072-4292. Bibcode: 2014RemS....7...24C.
9. ↑ 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 Luo, Lei; Wang, Xinyuan; Guo, Huadong (2024-07-01). "Transitioning from remote sensing archaeology to space archaeology: Towards a paradigm shift". Remote Sensing of Environment 308: 4–15. doi:10.1016/j.rse.2024.114200. ISSN 0034-4257. Bibcode: 2024RSEnv.30814200L. https://www.sciencedirect.com/science/article/abs/pii/S0034425724002189.
10. ↑ 10.0 10.1 10.2 10.3 Verhoeven, Geert (2017-09-14). "Are We There Yet? A Review and Assessment of Archaeological Passive Airborne Optical Imaging Approaches in the Light of Landscape Archaeology" (in en). Geosciences 7 (3): 3–5. doi:10.3390/geosciences7030086. ISSN 2076-3263. Bibcode: 2017Geosc...7...86V.
11. ↑ 11.0 11.1 11.2 11.3 11.4 11.5 11.6 11.7 Neubauer, Wolfgang (January 2001). "Images of the invisible-prospection methods for the documentation of threatened archaeological sites" (in en). Naturwissenschaften 88 (1): 13–24. doi:10.1007/s001140000192. ISSN 0028-1042. PMID 11261352. Bibcode: 2001NW.....88...13N. http://link.springer.com/10.1007/s001140000192.
12. ↑ Verhoeven, Geert (2017-09-14). "Are We There Yet? A Review and Assessment of Archaeological Passive Airborne Optical Imaging Approaches in the Light of Landscape Archaeology" (in en). Geosciences 7 (3): 86. doi:10.3390/geosciences7030086. ISSN 2076-3263. Bibcode: 2017Geosc...7...86V.
13. ↑ Bewley, Robert H. (2003). "Aerial survey for archaeology" (in en). The Photogrammetric Record 18 (104): 273–292. doi:10.1046/j.0031-868X.2003.00023.x. ISSN 1477-9730. Bibcode: 2003PgRec..18..273B. https://onlinelibrary.wiley.com/doi/10.1046/j.0031-868X.2003.00023.x.
14. ↑ 14.0 14.1 14.2 Wilson, David Raoul (1982). Air photo interpretation for archaeologists. Internet Archive. New York : St. Martin's Press. pp. 27–69. ISBN 978-0-312-01527-5. https://archive.org/details/airphotointerpre0000wils/mode/2up.
15. ↑ 15.0 15.1 Del Pozo, S.; Rodríguez-Gonzálvez, P.; Sánchez-Aparicio, L. J.; Muñoz-Nieto, A.; Hernández-López, D.; Felipe-García, B.; González-Aguilera, D. (2017-08-18). "Multispectral Imaging in Cultural Heritage Conservation" (in English). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2-W5: 155–162. doi:10.5194/isprs-archives-XLII-2-W5-155-2017. ISSN 1682-1750. Bibcode: 2017ISPAr62W5..155D. https://isprs-archives.copernicus.org/articles/XLII-2-W5/155/2017/.
16. ↑ Bitelli, Gabriele (2012). Lasaponara, Rosa; Masini, Nicola. eds. "Satellite Remote Sensing" (in en). Remote Sensing and Digital Image Processing 16: 113–126. doi:10.1007/978-90-481-8801-7. ISBN 978-90-481-8800-0. ISSN 1567-3200. https://link.springer.com/book/10.1007/978-90-481-8801-7.
17. ↑ Cowley, Dave (2011-01-01). Remote Sensing for Archaeological Heritage Management. pp. 43–58. ISBN 978-963-9911-20-8. https://www.academia.edu/6730535.
18. ↑ Verhoeven, Geert (2017-09-14). "Are We There Yet? A Review and Assessment of Archaeological Passive Airborne Optical Imaging Approaches in the Light of Landscape Archaeology" (in en). Geosciences 7 (3): 12–17. doi:10.3390/geosciences7030086. ISSN 2076-3263. Bibcode: 2017Geosc...7...86V.
19. ↑ 19.0 19.1 Hubert, Mara; Krömker, Susanne (2017). "Visual Computing for Archaeological Artifacts with Integral Invariant Filters in 3D". GCH Conference Proceedings: 2–7. https://diglib.eg.org/server/api/core/bitstreams/25baea81-03f8-4a5a-a8b0-679a5ccc02f2/content.
20. ↑ Verhoeven, Geert (2017-09-14). "Are We There Yet? A Review and Assessment of Archaeological Passive Airborne Optical Imaging Approaches in the Light of Landscape Archaeology" (in en). Geosciences 7 (3): 9–12. doi:10.3390/geosciences7030086. ISSN 2076-3263. Bibcode: 2017Geosc...7...86V.
21. ↑ 21.0 21.1 21.2 Chen, Fulong; Masini, Nicola; Yang, Ruixia; Milillo, Pietro; Feng, Dexian; Lasaponara, Rosa (2014-12-23). "A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data" (in en). Remote Sensing 7 (1): 28–46. doi:10.3390/rs70100024. ISSN 2072-4292. Bibcode: 2014RemS....7...24C.
22. ↑ Huang, Xiang; Uffelman, Erich; Cossairt, Oliver; Walton, Marc; Katsaggelos, Aggelos K. (September 2016). "Computational Imaging for Cultural Heritage: Recent developments in spectral imaging, 3-D surface measurement, image relighting, and X-ray mapping". IEEE Signal Processing Magazine 33 (5): 130–138. doi:10.1109/MSP.2016.2581847. ISSN 1558-0792. Bibcode: 2016ISPM...33..130H.
23. ↑ 23.0 23.1 23.2 23.3 23.4 23.5 Argyrou, Argyro; Agapiou, Athos (2022-11-26). "A Review of Artificial Intelligence and Remote Sensing for Archaeological Research" (in en). Remote Sensing 14 (23): 3–19. doi:10.3390/rs14236000. ISSN 2072-4292. Bibcode: 2022RemS...14.6000A.

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