## 1 Introduction  
As urbanization gains traction, illegal land occupation is becoming increasingly prominent. Consequently, newly constructed land has become a key subject of supervision and regulation in land development. To effectively improve the monitoring capacity of natural resources and achieve the \"early detection and early prevention\" goal of natural resource management against illegal land occupation, remote sensing image change detection technology is urgently needed to extract the features pertaining to changes in the development of different types of constructed land. According to the classification system in the land use change surveys, new bare land is among the most significant forms of change in constructed land, where a certain land type is converted from its vegetated or natural state into cleared land for construction. Therefore, obtaining automatic and accurate regional and even national data on land development changes is crucial for natural resource management. Remote sensing change detection is the process of determining changes in land coverage based on multiple satellite images at different points in time to obtain accurate real-time information. Change detection methods for remote sensing images are mainly divided into two major categories: traditional and deep learning methods. Traditional methods can be divided into image differencing and feature-based and target-driven change detection. These traditional change detection methods have limitations such as the inability to eliminate manual intervention and low levels of automation, and they are easily affected by changes in imaging conditions, image acquisition time, image matching quality, and noise, which render the change detection results unsatisfactory. With the gradual expansion of deep learning in the field of remote sensing, the power of neural networks in feature selection and fitting has generated new ideas for image change detection tasks. Since the introduction of AlexNet [1] in 2012, convolutional neural networks (CNN) with enhanced structure and performance have been launched every year, such as Visual Geometry Group (VGG) [2], GoogLeNet [3], fully convolutional networks (FCNs) [4], U-Net [5], Residual Network (ResNet) [6], and Efficientnet [7]. Many deep learning methods for change detection have been derived based on these networks. As deep learning-based image change detection can autonomously identify high-dimensional features reflected in the changed regions of the image without human intervention, it is the mainstream method today. Currently, most deep learning methods treat remote sensing image change detection as a semantic segmentation task by evaluating the changes in each pixel in the input image pair. Most models are based on the classic U-Net's encoder-decoder network architecture design and have built on it, including DeepLab V3+ [8] and Semantic FPN [9]. Change detection models, such as SNUNet-CD [10] and STANet [11], have been used to study remote sensing change detection in land use based on deep learning networks, with a significant focus on changes in buildings [12; 13]. Presently, relatively few relevant studies have used deep learning algorithms for high-resolution remote sensing change detection on land use in large areas (such as city- and national-level) with practical business applications. Furthermore, several studies are limited to a few test images or small areas. In this study, we address the business demand for the automatic feature extraction of new bare land at a national scale. This fully integrates the Siamese neural networks, atrous convolution, encoder-decoder, and other advanced algorithms and network structures to build a generalized deep CNN change detection model framework for automatic extraction on large-scale newly constructed bare land with accuracy and efficiency. The accuracy of deep learning in the automatic feature extraction of land cover change has considerably improved compared with that of traditional algorithms.