A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique
Abstract
1. Introduction
2. Research Aims
3. Airborne LiDAR Technology in Archaeological Feature Detection
3.1. Introduction to Airborne LiDAR
3.2. Advantages of Airborne LiDAR in Archaeology
3.3. Challenges and Limitations of Airborne LiDAR in Archaeology
4. Machine Learning in Archaeological Feature Detection
4.1. Overview
4.2. Application of Machine Learning in Archaeological Feature Detection
4.3. Deep Learning
4.4. Application of Deep Learning in Archaeological Feature Detection
4.5. Transfer Learning
5. Past Research Applying Machine Learning on Airborne LiDAR Derivatives for Archaeological Feature Detection
6. Discussion
6.1. The Value of LiDAR and Machine Learning Integration
6.2. Current Applications and Achievements
6.3. Technical and Practical Challenges and Generalization Issues
6.4. Practical Considerations and Implementation Needs
6.5. Opportunities and Future Research Directions
6.5.1. Improving Precision and Reducing False Positives
6.5.2. Multi-Modal Data Fusion and Complementary Data Integration
6.5.3. Advanced DL Architectures and Techniques
6.5.4. Enhancing Training Data and Labeling Methodologies
6.5.5. Integration into Archaeological Workflows and Decision Support Systems
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ALS | Airborne Laser Scanning |
ANNs | Artificial Neural Networks |
BIM | Building Information Model |
CNN | Convolutional Neural Network |
DEMs | Digital Elevation Models |
DL | Deep Learning |
DNNs | Deep Neural Networks |
DTMs | Digital Terrain Models |
DSMs | Digital Surface Models |
FN | False Negative |
FP | False Positive |
GIS | Geographic Information Systems |
GNSS | Global Navigation Satellite System |
IMU | Inertial Measurement Unit |
LBR | Location-Based Ranking |
LiDAR | Light Detection and Ranging |
LRMs | Local Relief Models |
ML | Machine Learning |
NIR | Near-Infrared Range |
OBIA | Object-Based Image Analysis |
R-CNN | Mask Region-based Convolutional Neural Network |
ReLU | Rectified Linear Units |
RF | Random Forest |
RNN | Recurrent Neural Network |
RS | Remote Sensing |
SAR | Synthetic Aperture Radar |
SS | Semantic Segmentation |
SVM | Support Vector Machine |
TL | Transfer Learning |
TN | True Negative |
TP | True Positive |
UAV | Unmanned Aerial Vehicle |
Appendix A. Glossary
Metric | Definition | Importance and Use Cases |
Accuracy (Overall Accuracy) | Represents the ratio of correctly predicted instances to the total number of instances in a dataset. It is calculated as the number of overall correctly classified items (TP + TN) compared to the entire number of samples [26,42,53,60]. |
|
Recall (Completeness, Detection Rate, Sensitivity) | Measures how many relevant objects are selected by the model. It is calculated as the number of TP (TP) divided by the sum of TP and FN [15,20]. |
|
Precision (Correctness) | Measures how many of the selected items are relevant. It is calculated as the number of TP (TP) divided by the sum of TP and FP [15,20,53]. | |
F1-score (Dice–Sorensen coefficient) | The harmonic average of precision and recall. It is calculated as 2 × (Recall × Precision)/(Recall + Precision) [15,20,53,68]. |
|
Reliability | Refers to the consistency and trustworthiness of a measurement, method, or system [32,37], and contributes directly to the accuracy of results [87]. |
|
Intersection over Union (IoU, Jaccard index) | Quantifies the overlap between a predicted bounding box or segmentation mask and the ground truth (actual object) [72]. It is calculated as the area of intersection divided by the area of union between the predicted and ground truth regions [8,24,80]. |
|
Mean Average Precision | It is typically defined as the average of the Average Precision (AP) values calculated for each object class, often at a specific IoU threshold (e.g., mAP@0.5 IoU). AP itself represents the area under the precision-recall curve for a given class [72,75]. |
|
Matthews Correlation Coefficient (MCC) | A balanced measure of the quality of binary classifications that can be used even if the classes are of very different sizes [24]. It takes into account all four values in a confusion matrix: TP, TN, FP, and FN [53]. |
|
Receiver Operating Characteristic (ROC) Curve | It plots the True Positive Rate (recall) against the False Positive Rate for different classification thresholds [42]. | |
Precision–Recall Curve | A graph that plots precision values against corresponding recall values for different classification thresholds [42]. |
|
Kappa Coefficient (Cohen’s Kappa) | Measures the agreement between the predicted classification and the true values, accounting for the possibility of agreement occurring by chance [60,62]. |
|
Loss Functions (e.g., Mean Squared Error (MSE), Binary Cross-Entropy, Jaccard Loss, Categorical Focal Loss) | A loss function is a mathematical function that quantifies the error or discrepancy between the predicted output of a model and the true (ground truth) labels [8,22,80]. During the training process, the neural network parameters are iteratively adjusted to minimize this loss function [8,13,84]. |
|
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Authors | Archaeological Sites/Objects | Study’s Location (Extent) | LiDAR Derivative and Resolution | Detection Method (Architecture/ Algorithm) | Quality Evaluation |
---|---|---|---|---|---|
(a) | |||||
[8] | Ancient City Walls | Jinancheng, China (16 km2) | 0.5 m DEM | CNN (U-Net segmentation) | Precision 94.12% |
[61] | Ancient Agricultural Water Harvesting Systems (Terrace and Sidewall) | Central Negev Desert, Israel (1800 km2) | 0.125 m DTM; 2 points/m2 | CNN (modified U-Net) | IoU 53% |
[32] | Pitfall Systems | Suomenselka, Finland (6778.9 km2) | 0.25 m DEM; 5 points/m2 | CNN (-) | Reliability 80% |
[5] | Tar Production Kilns | Kuivaniemi (2760 km2), Hossa (2004 km2), and Näljänkä (2304 km2), Finland | 0.25 m DEM; 5 points/m2 | CNN (U-Net) | Accuracy 93–95% Precision 82–97% Recall 72–99% F1-score 77–97% |
[60] | Precolonial Stone-Walled Structures (Circular Homestead, Agricultural Terrace, and Road) | Thaba-Chweu, South Africa (31.25 km2) | 0.2 m DTM | ML (Support Vector Machine) | Accuracy 95% |
[25] | Ancient Canals (Maya Wetland) | Rio Bravo, Belize (~5 km2) | 0.5 m DEM | ML (Random Forest) | Accuracy 66% |
[19] | Linear Structures (Embankment, Ditch, Hollow Path, etc.) | Blois, France (270 km2) | 0.5 m DTM | ML (Support Vector Machine) | - |
[82] | Barrows and Celtic Fields | Gelderland, The Netherlands (2200 km2) | 0.5 m DTM; 6–10 points/m2 | Faster Region-based CNN | - |
[17] | Historic Stone Walls | Aro, Denmark (88 km2) | 0.4 m DTM | CNN (U-Net segmentation) | Accuracy 93% |
[41] | Celtic Fields and Burial Mounds | The Białowieza Forest, Poland (697.8 km2) | 0.5 m DTM; 11 points/m2 | CNN (U-Net) | F1-score 58% IoU 50% |
[72] | Topographic Anomalies | Brittany, France (200 km2) | 0.5 m DTM; 14 points/m2 | TL Mask Region-based CNN (ResNet-101) | Accuracy <77% |
[13] | Grave mound, Pitfall trap, Charcoal Kiln | Norway (937 km2) | 0.5 m DTM; 5 points/m2 | Faster Regional based-CNN | Accuracy ~70% |
[53] | Trace Hollow Roads | Veluwe, The Netherlands (93.75 km2) | 0.5 m DTM | CNN (CarcassonNet) | Accuracy 89%, F1-score 42% |
[22] | Relict Charcoal Hearth Sites | Germany (3.4 km2) | 0.5 m DEM | Modified Mask Region-based CNN | Recall 83%, Precision 87% |
[80] | Ancient Agricultural Terraces and Walls | Negev, Israel (-) | 0.1 m DTM | CNN (U-Net segmentation) | Precision (Terrace 87%, Wall 60%) |
[15] | Barrow, Celtic Field, Charcoal kiln | Veluwe, The Netherlands (2200 km2) | 0.5 m DTM; 6–10 points/m2 | Faster Region-based CNN (WODAN 2.0) | F1-score 70% |
[85] | Maya Settlements (Aguada, Building, Platform) | Campeche, Mexico (230 km2) | 0.5 m DEM; 14.7 points/m2 (ground) | CNN (VGG-19) | Accuracy 95% |
[84] | Bomb Crater, Charcoal Kiln, Barrow | Harz Mountains, Germany (47,000 km2) | 0.5 m DTM | CNN (Deeplab v3+) | IoU 76.8% |
[67] | Burial Mounds | Romania (200 km2) | 0.5 m DEM; 2–6 points/m2 | ML (Random Forest) | Accuracy 96% |
[71] | House, Wall, Pyramid, etc. | Mexico (-) | 0.3 m DEM | CNN (VGG) | Precision 97% |
[14] | Historic Mining Pits | Dartmoor National Park, UK (-) | 0.25 m and 0.5 m DSM | TL CNN (DeepMoon) | Recall 80% (0.5 m DSM) and 83% (0.25 m DSM) |
[81] | Prehistoric Roundhouses, Shieling Huts, Clearance Cairns | Arran, Scotland (432 km2) | 0.25 m DTM; 2.75 points/m2 (ground) | TL CNN (ResNet-18) | Accuracy (Roundhouse 73%, Huts 26%, Cairns 20%) |
[7] | Burial Mounds | Brittany, France (246.7 km2) | 0.25 m DTM; 14 points/m2 | ML (Random Forest) | - |
(b) | |||||
[29] | Hillforts | England (130,000 km2), Alto Minho, Portugal (2220 km2), Galicia, Spain (30,000 km2) | 1 m DTM; 0.5 and 2 points/m2 | CMX (Semantic Segmentation) | F1-score 66% |
[26] | Maya Structures | Tabasco, Mexico (885 km2), Petén, Guatemala (615 km2) | 1 m DEM; 2.07 points/m2 (ground) | CNN (YOLOv3) | F1-score 80% |
[18] | Burial Mounds | Alto Minho, Portugal (2220 km2) | 1 m DTM | Region-based CNN (YOLOv3) | Detection Rate 72.53% |
[79] | Stone Walls | Northeastern CT, USA (-) | 1 m DEM | CNN (U-Net) | Recall 89% Precision 93% F1-score 91% |
[42] | Shipwreck | Alaska, and Puerto Rico, USA (-) | 1 m DEM | TL CNN (YOLOv3) | F1-score 92% |
[62] | Stone Wall, Pottery | Chun Castle, UK (-) | 1 m DSM | ML (Support Vector Machine) | Accuracy >70% |
[78] | Burial Mounds | Galicia, Spain (29,574 km2) | 1 m DTM | Region-based CNN (YOLOv3) | Detection Rate 89.5%, Precision 66.75% |
[10] | Shell Rings | South Carolina, USA (6712 km2) | 1.5 m DEM | Mask Region-based CNN | Accuracy ~75% |
[54] | Field Systems (Medieval Terraced Slopes, and Ridges and Furrows) | Southern Vosges, France (1462 km2) | 1 m DEM; 5 points/m2 | ML (Random Forest) and DL (Fully Connected Networks) | F-score 64–91% (ML) and 55–77% (DL) |
[68] | Relict Charcoal Hearths | New England, USA (493 km2) | 1 m DEM; 2 points/m2 | CNN (U-Net) | F1-score 86% |
[24] | Maya Structures | Petén, Guatemala (2144 km2) | 1 m DEM | Mask Region-based CNN (U-Net) | Accuracy 95% |
[11] | Viking Age Fortress | Bornholm, Denmark (42,036 km2) | 1.6 m DTM | ML (Random Forest) | - |
[74] | Hollow Way, Stream, Pathway, Lake, Street, Ditch, etc. | Lower Saxony, Germany (-) | 1 m DTM | Hierarchical CNN | Accuracy 91% |
[24] | Maori Storage Pits | New Zealand (-) | 1 m DEM | ML (Template Matching) | - |
(c) | |||||
[12] | Historical Terrain Anomalies | Eifel Region, Germany (0.01 km2) | DTM; 200–300 points/m2 | ML (Support Vector Machine) | Recall 76–80% Precision 55–72% F1-score 57–81% |
[2] | Clearance Cairns | Söderåsen, Sweden (-) | DTM; 0.5–1 points/m2 | CNN (U-Net segmentation) | Dice coefficient 84% |
[64] | Archaeological Topography | Perticara, Italy (106.45 km2) | DEM; 142 points/m2 | ML (Unsupervised ISODATA) | - |
[6] | Earthwork Sites (Pit, Terrace, Sod Wall, Ditch) | Northland, New Zealand (-) | Low-quality DEM | Faster Region-based CNN (ResNet-101) | - |
[75] | Barrow, Celtic Field, Charcoal Kiln | Veluwe, The Netherlands (2200 km2) | DTM; 6–10 points/m2 | CNN (YOLOv4) | Precision 64%, F1-score 76% |
[20] | Barrows and Celtic Fields | Veluwe, The Netherlands (440 km2) | LiDAR images; 6–10 points/m2 | Region-based CNN (WODAN) | F1-score~70% |
[4] | Barrow, Celtic Field, Charcoal Kiln | Veluwe, The Netherlands (437.5 km2) | LiDAR images; 6–10 points/m2 | CNN (WODAN) | - |
[69] | Grave, Mound, Pitfall Trap, Charcoal Burning Pit, Charcoal Kiln | Oppland, Norway (29 km2) | - | ML (Template Matching) | - |
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Zeynali, R.; Mandanici, E.; Bitelli, G. A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique. Remote Sens. 2025, 17, 2733. https://doi.org/10.3390/rs17152733
Zeynali R, Mandanici E, Bitelli G. A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique. Remote Sensing. 2025; 17(15):2733. https://doi.org/10.3390/rs17152733
Chicago/Turabian StyleZeynali, Reyhaneh, Emanuele Mandanici, and Gabriele Bitelli. 2025. "A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique" Remote Sensing 17, no. 15: 2733. https://doi.org/10.3390/rs17152733
APA StyleZeynali, R., Mandanici, E., & Bitelli, G. (2025). A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique. Remote Sensing, 17(15), 2733. https://doi.org/10.3390/rs17152733