Investigations of non-visible features in archaeological sites: testing aerial remote sensing with UAV in Pompeii
Submitted: 2025-06-18
|Accepted: 2025-09-01
|Published: 2025-09-22
Copyright (c) 2025 Virtual Archaeology Review

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Downloads
Keywords:
Multispectral data, Aerial Remote Sensing, unmanned aerial vehicle (UAV), Spectral indices, Index maps, Archaeological investigations
Supporting agencies:
Abstract:
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.
References:
Abate, N., & Lasaponara, R. (2019). Preventive archaeology based on open remote sensing data and tools: the cases of Sant’Arsenio (SA) and Foggia (FG), Italy. Sustainability, 11(15), 4145. https://doi.org/10.3390/su11154145
Abderrazak, B., Kadhem, G., El Battay, A., Mohamed, N., & Rouai, M. (2016). Assessment of land erosion and sediment accumulation caused by runoff after a flash-flooding storm using topographic profiles and spectral indices. Advances in Remote Sensing, 5, 315–354. https://doi.org/10.4236/ars.2016.54024
Agapiou, A., Alexakis, D. D., Sarris, A., & Hadjimitsis, D. G. (2013). Orthogonal equations of multi-spectral satellite imagery for the identification of un-excavated archaeological sites. Remote Sensing, 5(12), 6560–6586. https://doi.org/10.3390/rs5126560
Agapiou, A., & Hadjimitsis, D. G. (2011). Vegetation indices and field spectroradiometric measurements for validation of buried architectural remains: Verification under area surveyed with geophysical campaigns. Journal of Applied Remote Sensing, 5(1), 053554. https://doi.org/10.1117/1.3645590
Agudo, P. U., Pajas, J. A., Pérez-Cabello, F., Redón, J. V., & Lebrón, B. E. (2018). The potential of drones and sensors to enhance detection of archaeological cropmarks: a comparative study between multi-spectral and thermal imagery. Drones, 2(3), 29. https://doi.org/10.3390/drones2030029
Aicardi, I., Chiabrando, F., Grasso, N., LINGUA, A., Noardo, F., & Spano, A. (2016). UAV PHOTOGRAMMETRY WITH OBLIQUE IMAGES: FIRST ANALYSIS ON DATA ACQUISITION AND PROCESSING. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B1, 835–842. https://doi.org/10.5194/isprsarchives-XLI-B1-835-2016
Änäkkälä, M., Lajunen, A., Hakojärvi, M., & Alakukku, L. (2022). Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices. Remote Sensing, 14(19), 4792. https://doi.org/10.3390/rs14194792
Andrés-Anaya, P., Molada-Tebar, A., Hernández-López, D., Moreno, M. Á., González-Aguilera, D., & Herrero-Huerta, M. (2024). Radiometric improvement of spectral indices using multispectral lightweight sensors onboard UAVs. Drones, 8(2), 36. https://doi.org/10.3390/drones8020036
Anguissola, A., & Olivito, R. (2022a). I Praedia di Iulia Felix alla luce delle nuove acquisizioni: Una sintesi. In Edizione degli scavi nei Praedia di Iulia Felix e studi sulla Regio II di Pompei (pp. 77–120). Pisa, Italy: Pisa University Press. https://www.academia.edu/95674889/A_Anguissola_R_Olivito_PRAEDIA_I_Edizione_degli_scavi_nei_Praedia_
di_Iulia_Felix_e_studi_sulla_Regio_II_di_Pompei_Pisa_University_Press_Pisa_2022
Anguissola, A., & Olivito, R. (2022b). I saggi di scavo 2019-2020 nell’hortus dei Praedia di Iulia Felix. In Edizione degli scavi nei Praedia di Iulia Felix e studi sulla Regio II di Pompei (pp. 137–176). Pisa, Italy: Pisa University Press. https://www.academia.edu/95674889/A_Anguissola_R_Olivito_PRAEDIA_I_Edizione_degli_scavi_nei_Praedia_
di_Iulia_Felix_e_studi_sulla_Regio_II_di_Pompei_Pisa_University_Press_Pisa_2022
Anguissola, A., & Olivito, R. (2022c). Introduzione. In Edizione degli scavi nei Praedia di Iulia Felix e studi sulla Regio II di Pompei (pp. 9–14). Pisa, Italy: Pisa University Press. https://www.academia.edu/95674889/A_Anguissola_R_Olivito_PRAEDIA_I_Edizione_degli_scavi_nei_Praedia
_di_Iulia_Felix_e_studi_sulla_Regio_II_di_Pompei_Pisa_University_Press_Pisa_2022
Belcore, E., Pittarello, M., Lingua, A. M., & Lonati, M. (2021). Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data. Remote Sensing, 13(9), 1756. https://doi.org/10.3390/rs13091756
Calleja, J. F., Requejo Pagés, O., Díaz-Álvarez, N., Peón, J., Gutiérrez, N., Martín-Hernández, E., … Fernández Álvarez, P. (2018). Detection of buried archaeological remains with the combined use of satellite multispectral data and UAV data. International Journal of Applied Earth Observation and Geoinformation, 73, 555–573. https://doi.org/10.1016/j.jag.2018.07.023
Cappellazzo, M., Patrucco, G., Sammartano, G., Baldo, M., & Spanò, A. (2024). Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data. Remote Sensing, 16(19), 3572. https://doi.org/10.3390/rs16193572
Casana, J., & Ferwerda, C. (2024). Drone-Acquired Short-Wave Infrared (SWIR) Imagery in Landscape Archaeology: An Experimental Approach. Remote Sensing, 16(10), 1671. https://doi.org/10.3390/rs16101671
Cebrián, R., Hortelano, I., Ortiz, I., & Vallés, J. (2024). Detection of funerary monuments in the northern necropolis of Segobriga using multispectral and georadar imaging. Virtual Archaeology Review. https://doi.org/10.4995/var.2024.22738
Chao, H., Cao, Y., & Chen, Y. (2010). Autopilots for small unmanned aerial vehicles: A survey. International Journal of Control, Automation and Systems, 8(1), 36–44. https://doi.org/10.1007/s12555-010-0105-z
Character, L., Beach, T., Inomata, T., Garrison, T. G., Luzzadder-Beach, S., Baldwin, J. D., … Ranchos, J. L. (2024). Broadscale deep learning model for archaeological feature detection across the Maya area. Journal of Archaeological Science, 169, 106022. https://doi.org/10.1016/j.jas.2024.106022
Cigna, F., & Tapete, D. (2021). Satellite Technologies for Monitoring Archaeological Sites at Risk. In A. Traviglia, L. Milano, C. Tonghini, & R. Giovanelli, Stolen Heritage Multidisciplinary Perspectives on Illicit Trafficking of Cultural Heritage in the EU and the MENA Region (p. Chapter_5510). Venice: Fondazione Università Ca’ Foscari. https://doi.org/10.30687/978-88-6969-517-9/007
Costa, H., Benevides, P., Marcelino, F., & Caetano, M. (2020). Introducing automatic satellite image processing into land cover mapping by photo-interpretation of airborne data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3-W11, 29–34. https://doi.org/10.5194/isprs-archives-XLII-3-W11-29-2020
Council of Europe. (2005). The Faro Convention. Retrieved from https://rm.coe.int/1680083746
da Silva, T. v. d. W., Gomes Pereira, L., & Oliveira, B. R. F. (2024). Assessing Geometric and Radiometric Accuracy of DJI P4 MS Imagery Processed with Agisoft Metashape for Shrubland Mapping. Remote Sensing, 16(24), 4633. https://doi.org/10.3390/rs16244633
Davis, D. (2021). Theoretical repositioning of automated remote sensing archaeology: shifting from features to ephemeral landscapes. Journal of Computer Applications in Archaeology, 4(1). https://doi.org/10.5334/jcaa.72
de Souza, R., Buchhart, C., Heil, K., Plass, J., Padilla, F. M., & Schmidhalter, U. (2021). Effect of time of day and sky conditions on different vegetation indices calculated from active and passive sensors and images taken from UAV. Remote Sensing, 13(9), 1691. https://doi.org/10.3390/rs13091691
Delegido, J., Verrelst, J., Meza, C. M., Rivera, J. P., Alonso, L., & Moreno, J. (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, 46, 42–52. https://doi.org/10.1016/j.eja.2012.12.001
Fabbri, C., Delgado, A., Guerrini, L., & Napoli, M. (2025). Precision nitrogen fertilization strategies for durum wheat: A sustainability evaluation of NNI and NDVI map-based approaches. European Journal of Agronomy, 164, 127502. https://doi.org/10.1016/j.eja.2024.127502
Fassbinder, J., Lippolis, C., Messina, V., Patrucco, G., Santoro, V., & Spano, A. (2025). Preliminary report on the campaigns 2022-2023 of the Italian Archaeological Expedition at Seleucia on the Tigris (IAES). Mesopotamia: rivista di archeologia, epigrafia e storia orientale antica: LIX, 2024, 55-82. https://iris.polito.it/handle/11583/2997480
Fiz, J. I., Martín, P. M., Cuesta, R., Subías, E., Codina, D., & Cartes, A. (2022). Examples and results of aerial photogrammetry in archeology with UAV: Geometric documentation, high resolution multispectral analysis, models and 3D printing. Drones, 6(3), 59. https://doi.org/10.3390/drones6030059
Gojda, M., & Hejcman, M. (2012). Cropmarks in main field crops enable the identification of a wide spectrum of buried features on archaeological sites in Central Europe. Journal of Archaeological Science, 39(6), 1655–1664. https://doi.org/10.1016/j.jas.2012.01.023
Gupta, S. G., Ghonge, D. M., & Jawandhiya, P. M. (2013). Review of unmanned aircraft system (UAS). International Journal of Advanced Research in Computer Engineering & Technology, 2(4). https://doi.org/10.2139/ssrn.3451039
Haryuatmanto, G. (2023). Analysis of airborne LiDAR data for archaeology study case: Sriwijaya Muaro Jambi Site. IOP Conference Series: Earth and Environmental Science, 1127(1), 012012. https://doi.org/10.1088/1755-1315/1127/1/012012
Hill, A. C., Laugier, E. J., & Casana, J. (2020). Archaeological remote sensing using multi-temporal, drone-acquired thermal and near infrared (NIR) imagery: A case study at the Enfield Shaker Village, New Hampshire. Remote Sensing, 12(4), 690. https://doi.org/10.3390/rs12040690
Hollesen, J., Jepsen, M. S., & Harmsen, H. (2023). The application of RGB, multispectral, and thermal imagery to document and monitor archaeological sites in the Arctic: a case study from South Greenland. Drones, 7(2), 115. https://doi.org/10.3390/drones7020115
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
Huang, T., Olsoy, P. J., Glenn, N. F., Cattau, M. E., Roser, A. V., Boehm, A., & Clark, P. E. (2024). Quantifying rangeland fractional cover in the Northern Great Basin sagebrush steppe communities using high-resolution unoccupied aerial systems (UAS) imagery. Landscape Ecology, 39(11), 196. https://doi.org/10.1007/s10980-024-01983-0
Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3), 185–200. https://doi.org/10.1016/S0167-5877(05)80004-2
Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845. https://doi.org/10.1016/j.rse.2008.06.006
Johnson, K., Nissen, E., Saripalli, S., Arrowsmith, J. R., McGarey, P., Scharer, K., … Blisniuk, K. (2014). Rapid mapping of ultrafine fault zone topography with structure from motion. Geosphere, 10(5), 969–986. https://doi.org/10.1130/GES01017.1
Kapari, M., Sibanda, M., Magidi, J., Mabhaudhi, T., Mpandeli, S., & Nhamo, L. (2025). Assessment of the maize crop water stress index (CWSI) using drone-acquired data across different phenological stages. Drones, 9(3), 192. https://doi.org/10.3390/drones9030192
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305–319. https://doi.org/10.1016/j.isprsjprs.2018.09.018
Luo, L., Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., … Yao, Y. (2019). Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment, 232, 111280. https://doi.org/10.1016/j.rse.2019.111280
Machala, M., Honzova, M., & Klimánek, M. (2015). Generating land-cover maps from remotely sensed data: Manual vectorization versus object-oriented automation. Monash University. Journal contribution, 11, 1–30. https://doi.org/10.4225/03/57D7990BEA4AC
Maes, W. H. (2025). Practical guidelines for performing UAV mapping flights with snapshot sensors. Remote Sensing, 17(4), 606. https://doi.org/10.3390/rs17040606
Marchetti, M., Materni, V., Sapia, V., & Urbini, S. (2022). Indagini geofisiche nell’hortus dei Praedia di Iulia Felix. In Edizione degli scavi nei Praedia di Iulia Felix e studi sulla Regio II di Pompei (pp. 9–14). Pisa, Italy: Pisa University Press. Retrieved from https://www.academia.edu/95674889/A_Anguissola_R_Olivito_PRAEDIA_I_Edizione_degli_scavi_nei_Praedia_
di_Iulia_Felix_e_studi_sulla_Regio_II_di_Pompei_Pisa_University_Press_Pisa_2022
Marques, M. J., Alvarez, A., Carral, P., Sastre, B., & Bienes, R. (2020). The use of remote sensing to detect the consequences of erosion in gypsiferous soils. International Soil and Water Conservation Research, 8(4), 383–392. https://doi.org/10.1016/j.iswcr.2020.10.001
Marx, A., Clasen, A., May, J., König, S., Kleinschmit, B., & Förster, M. (2024). Imaging spectroscopy for bark beetle detection in Norway spruce and the relevance of the red-edge spectral range. International Journal of Applied Earth Observation and Geoinformation, 133, 104100. https://doi.org/10.1016/j.jag.2024.104100
Masini, N., & Lasaponara, R. (2006). Satellite-based recognition of landscape archaeological features related to ancient human transformation. Journal of Geophysics and Engineering, 3(3), 230–235. https://doi.org/10.1088/1742-2132/3/3/004
Masini, N., Romano, G., Sieczkowska, D., Capozzoli, L., Spizzichino, D., Gabellone, F., Lasaponara, R. (2023). Non invasive subsurface imaging to investigate the site evolution of Machu Picchu. Scientific Reports, 13(1), 16035. https://doi.org/10.1038/s41598-023-43361-x
Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density Cypress Forest. Sensors, 7(11), 2636–2651. https://doi.org/10.3390/s7112636
Moriarty, C., Cowley, D. C., Wade, T., & Nichol, C. J. (2019). Deploying multispectral remote sensing for multi-temporal analysis of archaeological crop stress at Ravenshall, Fife, Scotland. Archaeological Prospection, 26(1), 33–46. https://doi.org/10.1002/arp.1721
Nikolakopoulos, K. G., Soura, K., Koukouvelas, I. K., & Argyropoulos, N. G. (2017). UAV vs classical aerial photogrammetry for archaeological studies. Journal of Archaeological Science: Reports, 14, 758–773. https://doi.org/10.1016/j.jasrep.2016.09.004
Nilsson, H. (1995). Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology, 33, 489–528. https://doi.org/10.1146/annurev.py.33.090195.002421
Ortega-Terol, D., Hernandez-Lopez, D., Ballesteros, R., & Gonzalez-Aguilera, D. (2017). Automatic hotspot and sun glint detection in UAV multispectral images. Sensors, 17(10), 2352. https://doi.org/10.3390/s17102352
Paone, R., & Rispoli, P. (2011). P. Rispoli e R. Paone, Pompei, Canale Conte Sarno,Lavori di sistemazione e rifunzionalizzazione. Rivista Di Studi Pompeiani XXII. Retrieved from https://www.academia.edu/12155319/P_Rispoli_e_R_Paone_Pompei_Canale_Conte_Sarno_Lavori
_di_sistemazione_e_rifunzionalizzazione_in_Rivista_di_Studi_Pompeiani_XXII_2011
Parslow, C. (1988). Documents illustrating the excavations of the Praedia of Julia Felix in Pompeii. Rivista Di Studi Pompeiani : II, 1988, 37–48. https://doi.org/10.1400/258585
Patrucco, G., Cortese, G., Giulio Tonolo, F., & Spanò, A. (2020). Thermal and optical data fusion supporting built heritage analyses. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020, 619–626. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-619-2020
Peña-Villasenín, S., Gil-Docampo, M., & Ortiz-Sanz, J. (2024). Hidden Archaeological remains in heterogeneous vegetation: a crop marks study in fortified settlements of Northwestern Iberian Peninsula. Remote Sensing, 16(21), 3923. https://doi.org/10.3390/rs16213923
Pepe, M., Fregonese, L., & Scaioni, M. (2018). Planning airborne photogrammetry and remote-sensing missions with modern platforms and sensors. European Journal of Remote Sensing, 51(1), 412–436. https://doi.org/10.1080/22797254.2018.1444945
Ronchi, D., Limongiello, M., & Barba, S. (2020). Correlation among earthwork and cropmark anomalies within archaeological landscape investigation by using LiDAR and multispectral technologies from UAV. Drones, 4(4), 72. https://doi.org/10.3390/drones4040072
Salgado Carmona, J. Á., Quirós, E., Mayoral, V., & Charro, C. (2020). Assessing the potential of multispectral and thermal UAV imagery from archaeological sites. A case study from the Iron Age hillfort of Villasviejas del Tamuja (Cáceres, Spain). Journal of Archaeological Science: Reports, 31, 102312. https://doi.org/10.1016/j.jasrep.2020.102312
Sammartano, G., Chiabrando, F., & Spanò, A. (2020). Oblique images and direct photogrammetry with a fixed wing platform: first test and results in Hierapolis of Phrygia (TK). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020, 75–82. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-75-2020
Santoro, V., Patrucco, G., Lingua, A., & Spanò, A. (2023). Multispectral UAV data enhancing the knowledge of landscape heritage. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-M-2–2023, 1419–1426. https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1419-2023
Sauerbier, M., & Eisenbeiss, H. (2010). UAVs For the documentation of archaeological excavations. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII(5), 526–531. ISPRS. https://doi.org/10.3929/ethz-b-000026303
Schaepman, M. E., Ustin, S. L., Plaza, A. J., Painter, T. H., Verrelst, J., & Liang, S. (2009). Earth system science related imaging spectroscopy—An assessment. Remote Sensing of Environment, 113(Suppl.1), S123–S137. https://doi.org/10.1016/j.rse.2009.03.001
Sonnemann, T. F., Comer, D. C., Patsolic, J. L., Megarry, W. P., Herrera Malatesta, E., & Hofman, C. L. (2017). Semi-automatic detection of indigenous settlement features on hispaniola through remote sensing data. Geosciences, 7(4), 127. https://doi.org/10.3390/geosciences7040127
Spinosa, A. (2022). Restauri, fruizione e ars topiaria nella Regio II di Pompei. In Edizione degli scavi nei Praedia di Iulia Felix e studi sulla Regio II di Pompei (pp. 61–73). Pisa, Italy: Pisa University Press. Retrieved from https://www.academia.edu/95674889/A_Anguissola_R_Olivito_PRAEDIA_I_Edizione_degli_scavi_nei_Praedia
_di_Iulia_Felix_e_studi_sulla_Regio_II_di_Pompei_Pisa_University_Press_Pisa_2022
Takhtkeshha, N., Mandlburger, G., Remondino, F., & Hyyppä, J. (2024). Multispectral light detection and ranging technology and applications: a review. Sensors, 24(5), 1669. https://doi.org/10.3390/s24051669
Traviglia, A., & Torsello, A. (2017). Landscape pattern detection in archaeological remote sensing. Geosciences, 7(4), 128. https://doi.org/10.3390/geosciences7040128
Urbini, S., Sapia, V., Materni, V., Marchetti, M., Anguissola, A., Taccola, E., & Olivito, R. (2021). Indagini geofisiche nell’hortus dei Praedia Iuliae Felicis (Pompei, II, 4). Risultati preliminari e prospettive [PDF]. https://doi.org/10.13131/ARCHEOLOGICADATA-C4V3-E890
Uribe, P., Angás, J., Pérez-Cabello, F., de la Riva, J., Bea, M., Serreta, A., … Martín-Bueno, M. (2015). Aerial mapping and multi-sensors approaches from remote sensing applied to the Roman archaeological heritage. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5-W4, 461–467. https://doi.org/10.5194/isprsarchives-XL-5-W4-461-2015
Verhoeven, G., Smet, P., Poelman, D., & Vermeulen, F. (2009). Spectral characterization of a digital still camera’s NIR modification to enhance archaeological observation. IEEE Transactions on Geoscience and Remote Sensing, 47(10), 3456–3468. https://doi.org/10.1109/TGRS.2009.2021431
Watts, A. C., Ambrosia, V. G., & Hinkley, E. A. (2012). Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sensing, 4(6), 1671–1692. https://doi.org/10.3390/rs4061671
Yang, H., Wang, S., Wang, S., Zhao, P., Ai, M., & Hu, Q. (2024). Moated site object detection using time series satellite imagery and an improved deep learning model in northeast Thailand. Journal of Archaeological Science, 171, 106070. https://doi.org/10.1016/j.jas.2024.106070
Zanni, S., & De Rosa, A. (2019). Remote Sensing Analyses on Sentinel-2 Images: Looking for Roman Roads in Srem Region (Serbia). Geosciences, 9(1), 25. https://doi.org/10.3390/geosciences9010025
Zhang, D., Hou, L., Lv, L., Qi, H., Sun, H., Zhang, X., … Liao, Y. (2025). Precision agriculture: temporal and spatial modeling of wheat canopy spectral characteristics. Agriculture, 15(3), 326. https://doi.org/10.3390/agriculture15030326
Zhao, Q., & Qu, Y. (2024). The retrieval of ground NDVI (Normalized Difference Vegetation Index) data consistent with remote-sensing observations. Remote Sensing, 16(7), 1212. https://doi.org/10.3390/rs16071212




