# Crack Segmentation on Earthen Heritage Site Surfaces
The preceding studies demonstrated its superiority over the methods based on traditional machine learning algorithms. SegNet [32] is made up of an encoder network and a corresponding decoder network. The encoder network architecture is topologically identical to the VGG16 [43] network with 13 convolutional layers. [PERSON] et al. [44] deployed SegNet architecture to segment structure surface cracks. By using SegNet as a reference, [PERSON] et al. [45] recommended an encoder-decoder structural model for inspecting concrete pavement, asphalt pavement, and bridge deck cracks. [PERSON] et al. [46] proposed the DeepCrack network based on the encoder-decoder architecture of SegNet and pairwisely fused the convolutional features that are generated in the encoder network and decoder networks. LinkNet employs an improved codec network architecture, and the input of each encoder layer is also bypassed by the output of its corresponding decoder [34]. This method can recover the spatial information lost by downsampling operations in the encoder and reduces the processing time. Training deeper neural networks is more challenging. [PERSON] et al. presented a residual learning framework to make it simpler to train networks [35]. ResNet, which has a depth of up to 152 layers, is less complex than VGG. Combining the Residual connections and Inception architecture [47], [PERSON] et al. [36] proposed that Inception-ResNet improves training speed with low total parameter counts and computational costs. [PERSON] et al. [48] evaluated the effectiveness of several deep learning networks (U-Net, DeepLabV3+, U-Net (SM), LinkNet (SM), and FPN (SM)) in brick segmentation and crack detection with masonry walls. The analysis results showed that machine learning is superior to image-processing techniques for segmentation. Deep learning demonstrates enormous potential in crack detection. However, there is not much research being done on the application of convolutional neural network models for the detection of cracks in earthen heritage sites because deep learning models require huge amounts of training data to make them work, and the adoption of deep learning techniques relies significantly on the volume of data available. The complexity of building a deep learning model stems from the difficulty in obtaining a significant number of cracked photographs of earthen heritage sites. This study focused on contributing to this growing area of research by exploring an effective and efficient method for crack detection in earthen heritage sites. By evaluating different network performances, pixel-level crack segmentation models were recommended for the study of earthen heritage site surfaces. It also aimed to establish a hybrid dataset. Different open-source image datasets were mixed, and then, a hybrid dataset was constructed. The hybrid dataset combines the images of cracks on the surface of cement pavement, asphalt pavement, and earthen heritage sites. In this paper, a method for crack detection in earthen heritage sites is proposed. Mixed datasets were used to evaluate different network performances by using key metrics and manually labeled ground truth for evaluation purposes. The rest of the paper is structured as follows. Section 2 briefly presents the mixed datasets and proposed network. Section 3 presents experimental results. The results are analyzed and discussed in Section 4, and Section 5 draws some conclusions and presents some hints for future work.