Prospecting of Architectural Features Using LiDAR‐UAV Technology, Deep Neural Networks and Visualization Techniques
A Case Study in Kuélap and Cambolín (NW Peru)
Bibliographic Data
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
Artificial neural network · Deep learning · Digital elevation model · Identification (biology · Prospecting · Prospection · Segmentation · Terrain · Visualization · 3D Surveying and Cultural Heritage · Archaeological Research and Protection · Archaeology and ancient environmental studies
Deep Learning in Remote Sensing
Why Not a Single Image? Combining Visualizations to Facilitate Fieldwork and On-Screen Mapping
Sky-View Factor as a Relief Visualization Technique
Deep learning
Aerial Bombing Crater Identification
Combined Detection and Segmentation of Archeological Structures from LiDAR Data Using a Deep Learning Approach
Unfolding WWII Heritages with Airborne and Ground-Based Laser Scanning
Adapting LiDAR data for regional variation in the tropics
Objective comparison of relief visualization techniques with deep CNN for archaeology
Evaluating Mask R‐CNN models to extract terracing across oceanic high islands
Using deep neural networks on airborne laser scanning data
LiDAR‐derived Local Relief Models – a new tool for archaeological prospection
A Comparison of Visualization Techniques for Models Created from Airborne Laser Scanned Data
Light detection and ranging (lidar) in the Witham Valley, Lincolnshire
A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland
Lidar visualization techniques for the construction of geoarchaeological deposit models
Broadscale deep learning model for archaeological feature detection across the Maya area
Deep learning reveals extent of Archaic Native American shell-ring building practices
Visualization of lidar-derived relief models for detection of archaeological features
Lasers Without Lost Cities
From earth to sky
Archaeological site segmentation of ancient city walls based on deep learning and LiDAR remote sensing
Visualisation of LiDAR terrain models for archaeological feature detection
Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models
| Citation velocity | historical |
|---|---|
| Highly cited | No |