The Potential of Drones and Sensors to Enhance Detection of Archaeological Cropmarks: A Comparative Study Between Multi-Spectral and Thermal Imagery

Agudo, Paula Uribe; Pajas, Jorge Angás; Pérez‐Cabello, Fernando; Redón, Jaime Vicente; Lebrón, Beatriz Ezquerra

doi:10.3390/drones2030029

Cited by 59 publications

(39 citation statements)

References 25 publications

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“…The key finding of the two survey methodologies is that no single technique is sufficient to attempt to reveal the maximum amount of potential information [119] and that for archaeological survey work a minimum of RGB, multispectral, and thermal imaging should be employed whenever possible. The addition of hyperspectral imagery and UAV acquired LiDAR, which were not available for this study, have considerable potential based on the findings presented here.…”

Section: Discussionmentioning

confidence: 99%

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Brooke

Clutterbuck

2019

Remote Sensing

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There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information.

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Section: Discussionmentioning

confidence: 99%

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Brooke

Clutterbuck

2019

Remote Sensing

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“…This technology is still developing and is being used for an increasing number of applications [12]. UAV-based field surveys have been assisted by improvements in the global navigation satellite system (GNSS) [13]. Enhanced data production workflows allow emergency response teams to obtain the required images in a timely manner, in order to respond to emergency situations.…”

Section: Introductionmentioning

confidence: 99%

“…As well as the possibility of using UAVs to obtain very high resolution (VHR) imagery, software such as Agisoft PhotoScan or Pix4D Processes has also helped in the compilation of mosaics, as well as with image georeferencing and post-processing, allowing users to obtain, for example, digital elevation models (DEMs) [13]. UAVs have in the past been used on a number of occasions to collect data for slope failure inventories [12,17,18], but analysis and classification of remotely sensed (UAV-derived) imagery in order to extract slope failures along roads is a new area of research.…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks

et al. 2019

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Slope failures occur when parts of a slope collapse abruptly under the influence of gravity, often triggered by a rainfall event or earthquake. The resulting slope failures often cause problems in mountainous or hilly regions, and the detection of slope failure is therefore an important topic for research. Most of the methods currently used for mapping and modelling slope failures rely on classification algorithms or feature extraction, but the spatial complexity of slope failures, the uncertainties inherent in expert knowledge, and problems in transferability, all combine to inhibit slope failure detection. In an attempt to overcome some of these problems we have analyzed the potential of deep learning convolutional neural networks (CNNs) for slope failure detection, in an area along a road section in the northern Himalayas, India. We used optical data from unmanned aerial vehicles (UAVs) over two separate study areas. Different CNN designs were used to produce eight different slope failure distribution maps, which were then compared with manually extracted slope failure polygons using different accuracy assessment metrics such as the precision, F-score, and mean intersection-over-union (mIOU). A slope failure inventory data set was produced for each of the study areas using a frequency-area distribution (FAD). The CNN approach that was found to perform best (precision accuracy assessment of almost 90% precision, F-score 85%, mIOU 74%) was one that used a window size of 64 × 64 pixels for the sample patches, and included slope data as an additional input layer. The additional information from the slope data helped to discriminate between slope failure areas and roads, which had similar spectral characteristics in the optical imagery. We concluded that the effectiveness of CNNs for slope failure detection was strongly dependent on their design (i.e., the window size selected for the sample patch, the data used, and the training strategies), but that CNNs are currently only designed by trial and error. While CNNs can be powerful tools, such trial and error strategies make it difficult to explain why a particular pooling or layer numbering works better than any other.

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“…Precision agriculture techniques are creating software and workflows that use remote sensing principles to asses crops health. All these elements are drastically changing the panorama of aerial archaeology, whereby aerial images obtained from UAVs are now widely used for survey, recording and publication [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

NDVI Identification and Survey of a Roman Road in the Northern Spanish Province of Álava

González

Hernández

2019

Remote Sensing

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The Iter 34 (Antonine Itinerary XXXIV) is the name of the Roman road that crosses the province of Álava from west to east. Since no specific path was officially recognized before our study, the remains of the road did not benefit from heritage protection. In 2017, we made a project to determine the course of the road through rural Álava. In addition to traditional archaeological excavation and prospecting techniques, we used UAVs (unmanned aerial vehicle) to produce NDVI (normalized difference vegetation index) orthomosaic plans of ten cultivated areas through which the road is conjectured to pass. NDVI orthomosaics let us see crop marks better than with conventional photography, allowing us to detect the crop marks during times of the year and in places where conventional photography would fail to show them. Thanks to the NDVI orthomosaics, remains of the road were identified not only in places where we knew it existed, but also in previously unknown locations. Furthermore, other archaeological features were identified close to the roadway. This technique heralds a great advance in non-invasive methods of archaeological surveying. By using precision farming techniques we have identified the course of the Roman road Iter 34 in several locations in a short period of time and with few resources.

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The Potential of Drones and Sensors to Enhance Detection of Archaeological Cropmarks: A Comparative Study Between Multi-Spectral and Thermal Imagery

Cited by 59 publications

References 25 publications

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks

NDVI Identification and Survey of a Roman Road in the Northern Spanish Province of Álava

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