## 1 Introduction
However, owing to the complexity of geospatial landscapes and non-uniform image quality, the automatic extraction of change parcels for large-scale, practical business applications is hindered by high false and missed detection rates and irregular parcel morphology. This results in a significant workload for subsequent manual verification, which makes it difficult to meet the need for high-precision and high-frequency monitoring. Current research on feature extraction for the remote sensing of newly constructed land mainly focuses on the design and improvement of change detection models, rather than the post-processing of parcels that have been extracted [14]. Taking new bare land as a case in point, the original parcels extracted using deep learning models are often segmented, small, or have jagged edges or holes within them, which directly affect their practical application. Post-processing is an operation performed after the change parcels of remote sensing images have been automatically extracted, which can further reduce the errors in the parcels. Commonly used post-processing operations include parcel aggregation, morphological processing, filtering, and region growing algorithms. Of these, parcel aggregation is an important aspect of post-processing. Most current studies focus on modeling the aggregation of multi-category land use parcels [15; 16], which takes into account the spatial topology and semantic information of the land type. However, the calculation of the semantic proximity is generally closely related to the multi-level category attributes of the land type and its tenure information. In terms of single-category parcel aggregation, there are relatively more studies on the aggregation of building outlines [17], with no relevant post-processing studies on the parcels of newly constructed bare land. Therefore, building on the foundations of a deep CNN change detection model framework, this study focuses on adaptive post-processing techniques on the change detection results--in particular, the automatic extraction of new bare land parcels, which can further improve the performance and accuracy of change detection from the initial prediction. The major contributions of this study can be summarized as follows:  
1. A generalized deep CNN change detection model framework is constructed by integrating Siamese neural networks, atrous convolution, encoder-decoder, and other advanced algorithms and network structures to perform the large-scale automatic extraction of new bare land parcels with accuracy and efficiency. 2. To tackle the issue of large data volume, false extraction, and the difficulty of practical application in the automatic feature extraction of new bare land parcels, a complete parcel optimization process from the \"pixel-object\" pre-processing to comprehensively post-process the vector parcels is proposed by utilizing the probability distribution of pixel-level change. 3. A multi-criteria proximity evaluation model is proposed by integrating the spatial distance between parcels, their overlapping area, and confidence difference to aggregate adjacent parcels. The rest of this paper is organized as follows. Section 2 introduces the methodology of this paper. Section 3 presents the study areas and setup of the experiments. The experimental results are shown in Section 4 and the performance of the proposed method is discussed in Section 5. Finally, the conclusions are given in Section 6.