## 1 Introduction  
Over the past 40 years since China's reform and opening up, the pace of urban construction has dramatically increased [1]. Between 2000 and 2005 alone, the total area of China's provincial capitals increased by 90.15%. Between 2000 and 2010, China's urban area grew by 101.04% [2, 3]. At the same time, the volume of unauthorized construction has also increased in direct proportion, which has seriously affected urban management and increased the workloads of urban supervision departments. Those engaging in unauthorized construction have not obtained the required permission for land use, structural planning, and construction. Since the promulgation of the urban planning law, unauthorized construction is regarded as illegal building [4]. Unauthorized structures such as corrugated iron houses in coastal cities are disastrous when a typhoon strikes, as the high winds cause the iron sheeting to fly through the air, destroying vehicles, shattering glass, and leading to other adverse consequences, including injuries and deaths. Quickly and effectively identifying unauthorized construction has become a key problem for urban planning departments. To identify illegal changes to buildings in urban areas, high-resolution aerial images can be useful for clearly showing the basic external characteristics of structures. In the past, many methods had been investigated to detect changes in buildings. [PERSON] and [PERSON] presented a method to automatically detect alterations to buildings using airborne laser scanner and digital aerial image data [5]. In a different approach, [PERSON] extracted data on buildings in old images and put the information into a database created using Site Model; new images were then compared with the models in the database to identify changes [6]. [PERSON] applied an algorithm to a geographic information system (GIS) database to detect changes in buildings. The important core of the algorithm was measurement of the minimum square distance between original building boundaries and a vector polygon from the GIS in new images [7]. [PERSON] used aerial images from different shooting angles and at different times. By eliminating the influence of parallax and building shadow caused by different shooting angles, the corresponding roof of a building in the two images was scanned to detect variation [8]. The buildings' change detection can also be used in different areas. For example, [PERSON] used the specificity of LIDAR data to detect building changes in earthquake-prone areas [9]. Recently, [PERSON] proposed a new method for employing multitemporal very high resolution (VHR) synthetic aperture radar (SAR) images to detect changes, using the regular back-scattering characteristics of buildings to detect new or completely demolished buildings [10]. [PERSON] used existing geographic databases and prior knowledge to automatically detect building changes in urban environments from high-resolution images, thereby identifying illegal construction and monitoring urban growth [11]. [PERSON] proposed a solution to monitor changes in buildings based on height variations in stereo imagery and digital surface models (DSMs). The change was detected via a stereo-matching methodology that used the joints of height changes and Kullback-Leibler divergence similarity measurements between original and later images [12]. [PERSON] used a multilevel random forest (RF) classifier to classify VHR images and compared the changes in two datasets to detect building alterations, using GIS data [13]. Although the above-mentioned studies have achieved good results in the identification of unauthorized construction, they cannot precisely prevent unauthorized construction. Thus, it is necessary to find a method of effectively detecting and managing unauthorized construction. In this study, we took the streets in the Siming District of Xiamen City as a research target, and we used multiperiod data and images from an unmanned aerial vehicle (UAV) with remote sensing to generate a database.