Buildings are the predominant objects that characterize the urban structure. For many cities, local governments establish building databases for administration as well as urban planning and monitoring. However, newly constructed buildings are often only included with a considerable time delay in the official digital cadastral maps due to processes in the acquisition of data, so-called undocumented buildings. In this regard, detecting undocumented buildings using remote sensing techniques would support the construction of update-to-date building databases with complementary information. In-depth studies on undocumented buildings and their number and location, however, are scarce. Therefore, we exploit a deep learning-based framework to detect undocumented buildings in remote sensing data and propose to derive 2D and 3D morphological parameters as well as landscape metrics., which are capable of depicting the physical forms and spatial structures of undocumented buildings. Furthermore, we exemplify the variabilities of undocumented buildings across space by the differences in morphology and landscape metrics between high and low building density regions. Upon analysis of undocumented buildings in 15 cities in the state of Bavaria, Germany, both state- and city-scale results reveal that most undocumented buildings are located in lower dense regions. This reveals that fragmentation of the landscape by building structures in the state of Bavaria is probably greater than official geospatial data currently documented.

建筑物是表征城市结构的主要对象。对于许多城市，地方政府建立了用于管理以及城市规划和监测的数据库。然而，由于数据采集过程的原因，新建建筑物通常仅在相当长的时间延迟后才包含在官方数字地籍地图中，即所谓的无证建筑物。在这方面，使用遥感技术检测无证建筑物将支持构建具有补充信息的最新建筑物数据库。然而，对无证建筑物及其数量和位置的深入研究很少。所以，我们利用基于深度学习的框架来检测遥感数据中的无证建筑物，并建议导出 2D 和 3D 形态参数以及景观指标，这些参数能够描绘无证建筑物的物理形态和空间结构。此外，我们通过高和低建筑密度区域之间形态和景观指标的差异来举例说明无证建筑在空间上的变化。在对德国巴伐利亚州 15 个城市的无证建筑进行分析后，州和城市规模的结果表明，大多数无证建筑位于人口密度较低的地区。这表明巴伐利亚州建筑结构造成的景观碎片化可能比目前记录的官方地理空间数据更大。