## 4 Results
### Results of the Parcel Optimization Process  
Based on the automatic extraction results of the two experimental areas, the confidence scores of all parcels were calculated according to the pre-processing methodology in Section 2.2. Next, the post-processing of the change parcels was performed according to the steps in Section 2.3. In the experiments, the edge simplification tolerance parameter was set to one pixel. When calculating the spatial proximity, \(d_{thresh}\) was set to a width of two pixels. When calculating the buffer overlap area ratio, the buffer radius was set to five pixels in width. In the parcel aggregation proximity model, \(\delta_{1}=0.5\), \(\delta_{2}=0.3\), and \(\delta_{3}=0.2\), were used to calculate the P\({}_{com}\) value for the proximity of two neighboring parcels. The results of each process of the parcel optimization are shown in Table 2, namely pre-processed parcels, simplified parcels, aggregated results, and final results after hole-filling, removal of small parcels, and confidence filtering. In the table above, the (a) column shows examples of the pre-processed parcel with three to four adjacent parcels in each example, and each of their confidence values (P1, P2, P3, and P4) are calculated. As shown in Table 3 below, the proximity (P\({}_{com}\)) was calculated only for pairs of adjacent parcels with intersecting buffers, and the aggregation threshold (P\({}_{thresh}\)) was set to 0.65 in this experiment. The aggregated results are shown in column (c) of Table 2. The final results of the optimization process are shown in Table 2d after area filtering, hole-filling, and confidence filtering, where the area filter threshold (A\({}_{thresh}\)) was set to 1200 m\({}^{2}\), and the confidence score filtering range \(p\) of the aggregated parcels was 165-255. Based on the processing results, the overall post-processing process proposed in this paper is reasonable and can effectively solve the problems of automatically extracted parcels being segmented, having holes or redundancy. To verify the effectiveness of the aggregation method proposed in this paper, the last column in Table 2 shows the results processed using the cartographic generalization tool in ArcGIS software. The AggregatePolygons function in ArcGIS was used for aggregation. In the experiment, the aggregation distance parameter _aggr_dis_ was set to a five-pixel width, and the parcel filtering area and size of the holes that were retained were consistent with the method in this paper. According to the post-processing examples in Table 2 and several processed results in Figure 11, the comparative analysis is as follows:  
The ArcGIS processing results of Examples 1, 2, and 4 in Table 2 are slightly different from the final results obtained using the method proposed in this paper in column (d). For the case of the uneven distance between two parcels, the aggregation probability of the method proposed in this study is high and the parcels can be easily aggregated. This is due to the proposed aggregation method incorporating semantic confidence within a certain buffer range (five pixel width), and the spatial proximity only considers the nearest distance between two parcels, which is relatively loose. The ArcGIS aggregation method does not consider the semantic information of the parcels and is restrictive for the distance evaluation, resulting in some fragmentation of the final new bare land parcels. As the connectivity of bare land types tends to be wide rather than narrow, the overall aggregation method proposed in this paper is targeted and effective. Additionally, in Example 2, the confidence score of the top-right parcel is low and does not satisfy the aggregation condition. The method proposed in this paper can effectively eliminate such discrete distribution parcels with high error rates through confidence-based filtering.