## 2 Materials and Methods
### Building Footprint Extraction Method Based on U-Net Model  
The building footprint represents the geographical location and size of the building and is a carrier of other building information, so the building footprint must be determined first. Deep-learning models can achieve good results in the field of image semantic recognition,  
Figure 1: Technical route of this study.  
such as building extraction. Fully convolutional network (FCN) is a common structure of artificial neural network, which mainly consists of encoder and decoder. The encoder extracts features by convolutional cloud, completes feature downsampling using pooling operation, and repeats the operation to complete the feature encoding. The decoder is to perform upsampling work on the feature encoding by interpolation and convolutional merging of the deconvolution operation, repeat the operation output, and, finally, complete the semantic segmentation work.  
The U-net model architecture is a continuation of Ronneberger's optimization based on FCN [17]. It has a symmetrical structure, with the encoder on the left and the decoder on the right, as shown in Figure 2. The main advantage of the structure is the use of jump connections to combine the shallow features extracted by downsampling in the encoder with the deeper features obtained by upsampling, which improves the accuracy of the output results. In this study, the existing image data and building outline vector data are used to make a sample data set, and the sample data are partitioned into a training set, a validation set, and a test set, obtaining the optimal model and parameters after training with the training and validation sets. Finally, the effectiveness of the model is evaluated using a test set. The specific process is as follows:  
1. Processing sample data sets. The open-source Google Images and Tianditu Images are used as the original remote sensing images, and the footprint data of the buildings are obtained as the annotated dataset after using human-assisted correction;
2. Making a model training set. The targeted selection of labeled data covering multiple types of buildings is used as the training dataset. A part of the annotated dataset is also selected as the validation set. The remaining sample set is the test dataset. The division ratio of the three datasets is 5:3:2;
3. Model training. The model runs mainly using the U-net model platform built by GPU GTX1080 Ti 8G video memory hardware and Python3.6 and ArcGIS Pro2.8 deep-learning tools. The training and validation sets are input into the model, and parameters such as learning rate, number of training samples, and number of iterations are set. As the number of training iterations increases, the training and validation conclusions of the U-net network model tend to stabilize. Finally, the parameters are solidified, and the model training is completed;
4. Model testing. The test set is fed into the model, the model is run, and the test sample data are recorded. After outputting the conclusion, the model prediction accuracy is evaluated using metrics such as intersection over union (IoU) and accuracy rate (Acc), as shown in Equations (1) and (2), where \(P\) denotes the prediction sample, \(T\) denotes the true sample, and count() denotes the area of the building footprint. \[\text{IoU}=\frac{\text{area}(P\cap T)}{\text{area}(P\cup T)}\] (1) \[\text{Acc}=\frac{\text{area}(P\cap T)}{\text{area}(T)}\] (2)