## 4 Identification of Unauthorized Construction
### Preliminary Automatic Screening of Unauthorized Construction  
In this paper, the program was coded in several steps for preliminary identification of illegal buildings, and these main steps are shown in Figure 2. First, the projection transformation of preprocessed DSM model data was required in order to make DSM data with different phases have accurate superposition. Second, different DSM data with different phases were calculated by using ArcGIS built-in grid calculator. Third, if the difference value was smaller than the set threshold one, it was judged as a non-illegal building, otherwise it was an illegal building. And fourth, the result data of the building area was extracted by superimposing the masking data with the data of the building area. The term \"building changes\" refers to new construction, capping, or demolition of buildings within a certain period of time in a specific area. These changes are mainly reflected in building height, as the results in Figure 3a,b show. By using the DSM model of a long time series in the observation area and combining this with a spatial analysis algorithm, we extracted a change distribution diagram of regional building height, as shown in Figure 4a. Further analysis of this diagram enabled us to set a reasonable number and interval of height difference classification values. Finally, we obtained a reclassification diagram of changes in buildings' heights. In combination with actual observation, as the buildings were constructed at different time differences and their heights were less than 1.5 m, they could be recognized as non-illegal areas, so such data were filtered out. The final result is shown in Figure 4b by selecting different symbol styles for the appropriate layers. The results in Figure 4 show that we can accurately and intuitively present dynamic changes in buildings' heights within a project area for a specific time period. By rapidly recognizing and filtering changes in buildings in a monitored region, including new buildings, illegal construction, and demolition, we can effectively reduce the effort required for early artificial visual discrimination of objects that can provide clues for further artificial visual discrimination. Figure 2: Automatic extraction flow chart of building elevation change detection. First, we pretreated the multiphase DSM model data for the same research area, including data exploration, projection transformation, and so forth, to ensure all the pretreated time-phase data was founded on the same accurate coordinate system for subsequent spatial analysis. Next, we used a grid computing tool to subtract the pre- and postprocessed phase data [17]. The formula is as follows:  
\[\text{Result = current-image ProjectRaste - early-image ProjectRaste} \tag{1}\]  
where current-image ProjectRaste is the post-temporal DSM model (representing the current state), and early-image ProjectRaste is the antecedent phase DSM model (representing the past state). The difference between the early-and current-phase DSM model can be displayed for the same region. A positive result indicates an increase in height, which may be due to new construction or illegal buildings. A negative result indicates a lower elevation, which may be the result of demolition. We can thereby obtain a building height distribution map. Through the above process, we achieved automatic extraction of all elevation changes in a certain region during a certain time period. As the main objective of this project was to examine built areas, potential disturbance factors such as woodland, shrubs, and mountainous areas were eliminated, and we applied masking tools to the built area. A difference in phase height less than \(\pm\)1.5 m was considered a nonbuilding change interval and was filtered out. The test data were then reclassified at intervals of 1 and 1.5 m. A distribution map of building height change was thus obtained and a data processing model was created to generate the appropriate data processing tools. The tools can be shared for further development and improvement.