Figure 1
Categories of dry-stone fortification walls considered in this work. Color patches indicate the scheme used to visualize the categories in segmentation maps.
Figure 2
Overview of the proposed pipeline. From a photogrammetric 3D reconstruction of the wall, 2D appearance and geometry layers are extracted and fed into a machine-learning model that predicts a multi-class semantic segmentation (colors denote individual classes, see Figure 1). The greyed-out elements represent manual annotations used during training, while the non-greyed components form the inference pipeline.
Figure 3
Area of interest with geographical context and ancient sites of the region. Transparent blue marks the ancient region of Caria.
Figure 4
Depth maps (left) provide absolute distance information, which complicates standardization. Our pipeline instead uses normal maps (right), which encode local surface orientation. For reference, the color coding of surface normals is illustrated using a sphere in the top right.
Figure 5
Illustration of training data generation: image patches are cropped at quasi-random positions across the wall, shown as red boxes.
Table 1
Properties of our test walls.
| NAME | SITE | CLASSES | SURFACE AREA | PURPOSE |
|---|---|---|---|---|
| Wall 1 | Halicarnassus | All classes | 121 m2 | Balanced evaluation |
| Wall 2 | Cedreae | Ashlar (dominant) & Quarry | 53 m2 | Class imbalance test |
| Wall 3 | Cedreae | Polygonal (dominant) & Quarry | 66 m2 | Class imbalance test |
| Wall 4 | Kaunos | All classes (Ashlar-dominant) | 70 m2 | Cross-site generalization |
Figure 6
Stone detections outside the annotated masonry bond on Wall 1, excluded from evaluation by the region of interest.
Figure 7
Qualitative results for our test walls, showing inputs (left), semantic segmentation from our full model, and manual annotations (right). Color coding of block types follows the scheme in Figure 1; black denotes background. Best viewed digitally and at high magnification.
Table 2
Quantitative results across models, test walls, and stone classes. Higher values indicate better performance. Bold numbers mark the best-performing model, while dashes denote classes absent from the test wall. The full model, which incorporates both appearance and geometry, generally achieves the best performance (numbers are rounded).
| MODEL METRIC | APPEARANCE ONLY | GEOMETRY ONLY | FULL MODEL | ||||
|---|---|---|---|---|---|---|---|
| loU | F1 | loU | F1 | loU | F1 | ||
| Wall 1 | Ashlar | .92 | .96 | .86 | .93 | .90 | .95 |
| Polygonal | .73 | .84 | .60 | .75 | .69 | .81 | |
| Quarry | .34 | .51 | .57 | .73 | .60 | .75 | |
| All Classes | .66 | .77 | .68 | .80 | .73 | .84 | |
| Wall 2 | Ashlar | .96 | .98 | .96 | .98 | .96 | .98 |
| Polygonal | – | – | – | – | – | – | |
| Quarry | .00 | .01 | .00 | .00 | .03 | .07 | |
| All Classes | .48 | .49 | .48 | .49 | .50 | .52 | |
| Wall 3 | Ashlar | – | – | – | – | – | – |
| Polygonal | .80 | .89 | .85 | .92 | .87 | .93 | |
| Quarry | .28 | .44 | .28 | .43 | .38 | .55 | |
| All Classes | .54 | .66 | .56 | .68 | .63 | .74 | |
| Wall 4 | Ashlar | .95 | .97 | .82 | .90 | .92 | .96 |
| Polygonal | .18 | .31 | .02 | .03 | .09 | .17 | |
| Quarry | .07 | .13 | .05 | .09 | .07 | .13 | |
| All Classes | .40 | .47 | .29 | .34 | .36 | .42 | |
| All Walls | Ashlar | .94 | .97 | .88 | .94 | .93 | .96 |
| Polygonal | .57 | .68 | .49 | .57 | .55 | .64 | |
| Quarry | .17 | .27 | .22 | .31 | .27 | .37 | |
| All Classes | .56 | .64 | .53 | .60 | .58 | .66 | |
Figure 8
Boundary precision between individual stones on Wall 1.
Figure 9
Polygonal-dominant Wall 3.
Figure 10
Small-sample effects on Wall 4.
Figure 11
Per-pixel uncertainty of the full model on Wall 1. Bar charts show the full class-probability distributions at four example pixels (black circles), ranging from a near-certain ashlar assignment to ambiguous pixels at block boundaries and class transitions.