# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 1 Introduction  
Remote sensing change image detection [1] is a technique that analyzes the process of feature changes in remote sensing images of the same geographic area at different times. Through change detection, researchers are able to identify whether the region of interest of two remote sensing images has changed or not and obtain an accurate change map for subsequent research [2]. Building change detection is a branch of remote sensing image change detection that monitors building changes by comparing and analyzing remote sensing images from multiple time phases. Currently, remote sensing building detection has a wide range of applications in the fields of land resource planning [3; 4], smart city construction [5], and humanitarian remote sensing. Notably, change detection plays a pivotal role in humanitarian remote sensing by aiding in natural disaster assessments and landmine and unexploded ordnance detection. In the aftermath of natural disasters, remote sensing technology can swiftly provide essential information that is critical for orchestrating emergency responses and effectively distributing resources [6; 7; 8]. Furthermore, it can detect subtle terrain changes, thereby enhancing the safety and efficiency of surveys in areas potentially littered with landmines and unexploded ordnance [9; 10]. In recent years, the deep learning model [11; 12; 13; 14; 15; 16; 17; 18] with powerful feature representation capability has gradually taken its place in the field of change detection. Deep learning-based change detection methods feed input image pairs into neural networks to extractfeatures. The change detection task is then treated as a pixel-by-pixel binary classification or multi-classification task, generating binary difference maps and obtaining the final change maps through operations such as threshold segmentation. Based on the different stages of multi-temporal image fusion, deep learning-based change detection methods can be categorized into early fusion methods and late fusion methods. Early fusion refers to stacking the multi-temporal images before the encoding stage. [PERSON] et al. [19] sliced the two multi-spectral images into small blocks and stacked the corresponding positional blocks of the bi-temporal images as an input sequence, which was fed into a recurrent neural network to extract the features. [PERSON] et al. [20] constructed a change detection neural network structure based on Unet++ [21]. This network splices the bi-temporal data and feeds it as a whole into the neural network, extracting multi-scale change feature maps through dense connections. Late fusion uses multiple branches with shared weights to extract features from different temporal phases, which are then fused during the decoding stage. [PERSON] et al. [22] input the bi-temporal images into a five-layer Siamese neural network trained with weighted contrast loss to extract spatial features and then used the k-nearest neighbor technique to generate the final change maps. [PERSON] et al. [23] chose Resnet50 as the baseline network and used spatial attention and channel attention modules to improve the discrimination of change features. [PERSON] et al. [24] introduced a Transformer module to extract image context information to obtain excellent change detection results. However, the above methods focus on the fusion of different simultaneous image features and ignore the fusion of shallow and deep information, resulting in rough-edge results. Since change detection of buildings requires high edge clarity, it is difficult to achieve high-performance results in many scenarios if the above methods are directly used for building change detection. Some recent works have started to explore approaches to improve the accuracy of building edge change detection, such as by introducing additional branches to detect the edges of building change areas [25; 26; 27].