Lithology mapping of stone heritage via state-of-the-art computer vision
Lithology mapping of stone heritage via state-of-the-art computer vision
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DOI：
10.1016/j.jobe.2020.101921
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发表时间：
2021-01-06
影响因子：
6.4
通讯作者：
Ince, Ismail
中科院分区：
文献类型：
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作者：
Hatir, Mehmet Ergun;Ince, Ismail
在建造不可移动文化遗产时广泛使用的建筑石材中，由于各种风化过程，必须进行保护工作。修复工作的准确性和有效性，特别是对文化遗产而言，是重要的因素。这些工作的最基本阶段是确定岩石类型和编制地质岩性图。然而，耗时的过程和困难的实地工作可能会导致人为错误。在这项研究中，基于Mask R-CNN开发了建筑石材的岩相测定和映射模型，以防止人为错误。为此，教科文组织临时遗产名录上的土耳其科尼亚市被选定采用拟议的方法。该模型使用从9座历史建筑中收集的1800张图像进行训练，这些建筑具有不同的装饰和建筑石材，构成了城市的文化纹理。该模型的测试是在Matbah-1 Serif纪念碑的主立面上进行的，该纪念碑由363块具有7种不同岩性的建筑石材组成，适用于不同的情况（分辨率，拍摄距离和角度）。在该模型中训练的岩石类型的平均精度值在89.10%和100%之间，并且为每种情况获得准确的岩性确定和地图。结果表明，该模型具有快速、自动成图的能力和可靠、高精度的岩性判别能力，可为恢复工作提供重要依据。
In building stones that are widely used in the construction of immovable cultural heritage, preservation efforts are necessary due to various weathering processes. The accuracy and effectiveness of the restoration works, specifically for cultural heritage, are important factors. The most basic phase of these works is determining the stone types and preparing geo-lithological maps. However, the time-consuming process and difficult field work may lead to human-induced errors. In this study, a petrographic determination of building stone and a mapping model were developed based on Mask R-CNN in order to prevent human errors. To this end, the city of Konya, Turkey, which is on the UNESCO temporary heritage list, was selected for applying the proposed method. The model was trained with a total of 1800 images collected from nine historic buildings with different ornamental and building stones that constitute the cultural texture of the city. Testing of the model was conducted on the main facade of the Matbah-1 Serif monument, consisting of 363 building stones with seven different lithologies, for different situations (resolution, shooting distance, and angles). The average precision values for the stone types trained in the model were between 89.10% and 100%, and an accurate lithology determination and map were obtained for each case. These results indicate that the proposed model can provide important bases for restoration works, with its fast and automatic mapping capability as well as its reliable and highly precise lithology determination.