The existing cultural heritage mapping in Norway is incomplete. Some selected areas are mapped well, while the majority of areas only contain chance discoveries, often with bad positional accuracy. The goal of this research was to develop automated tools for improving the cultural heritage mapping in Norway, thus enabling detailed mapping of large areas within realistic budgets and time frames. The focus was on three types of cultural heritage that occur frequently in many types of Norwegian landscape: grave mounds, pitfall traps in deer hunting systems and charcoal kilns.

A recent development in deep neural networks for object detection in natural images is the region-proposing convolutional neural network (R-CNN), which may also be used for cultural heritage detection in local relief model (LRM) visualizations of airborne laser scanning (ALS) data. Python code for ‘Simple Faster R-CNN’ was downloaded from Github.

On 737 test images (16.6 km²) not seen during training, 87 % of the true cultural heritage objects were correctly identified, while 24 % of the predicted cultural heritage locations were false. However, all test images were small (150 m × 150 m) and contained at least one cultural heritage object, meaning that the false positive rate may be higher for an entire landscape. In Larvik municipality, Vestfold and Telemark County, on a 67 km² area not seen during training, the R-CNN correctly identified 38 % of the true grave mounds, with 89 % false positives. On a 937 km² area covering Øvre Eiker municipality, Viken County, the R-CNN correctly identified 90 % of the charcoal kilns, with 38 % false positives.

In conclusion, we have demonstrated that Faster R-CNN is well suited for semi-automatic detection of cultural heritage objects such as charcoal kilns, grave mounds and pitfall traps in high resolution airborne lidar data. However, it is desirable to reduce the false positive rate in order to limit the amount of visual inspection needed when the method is applied to large areas for detailed archaeological mapping.

挪威现有的文化遗产地图不完整。某些选定区域的地图绘制得很好，而大多数区域仅包含偶然发现，通常位置精度较低。这项研究的目的是开发自动化工具，以改善挪威的文化遗产地图，从而在现实的预算和时间范围内对大面积区域进行详细的地图绘制。重点是在许多类型的挪威景观中经常出现的三种文化遗产：坟堆，鹿狩猎系统中的陷阱和木炭窑。

用于自然图像中目标检测的深层神经网络的最新发展是提出区域卷积神经网络（R-CNN），该网络也可用于机载激光扫描（ALS）局部浮雕模型（LRM）可视化中的文化遗产检测）数据。从Github下载了“ Simple Faster R-CNN”的Python代码。

在培训期间未看到的737张测试图像（16.6 km ²）上，正确识别了87％的真实文化遗产，而预测的文化遗产位置中有24％是错误的。但是，所有测试图像都很小（150 m×150 m），并且包含至少一个文化遗产物体，这意味着整个景观的假阳性率可能更高。在培训期间未见过的67 km ²区域的Larvik市，Vestfold和Telemark县，R-CNN正确地识别了38％的真实坟墓，其中89％的假阳性。在维肯县ØvreEiker市的937 km ²区域，R-CNN正确地识别了90％的木炭窑，其中38％的假阳性。

总之，我们证明了Faster R-CNN非常适合半自动检测文化遗产物体，例如高分辨率机载激光雷达数据中的木炭窑，坟冢和陷阱陷阱。但是，希望减少误报率，以限制在将该方法应用于大面积进行详细考古制图时所需的视觉检查量。