Recent developments in laser scanning systems have inspired substantial interest in indoor modeling. Semantically rich indoor models are required in many fields. Despite the rapid development of 3D indoor reconstruction methods for building interiors from point clouds, the indoor reconstruction of multi-room environments with curved walls is still not resolved. This study proposed a novel straight and curved line tracking method followed by a straight line test. Robust parameters are used, and a novel straight line regularization method is achieved using constrained least squares. The method constructs a cell complex with both straight lines and curved lines, and the indoor reconstruction is transformed into a labeling problem that is solved based on a novel Markov Random Field formulation. The optimal labeling is found by minimizing an energy function by applying a minimum graph cut approach. Detailed experiments were conducted, and the results indicate that the proposed method is well suited for 3D indoor modeling in multi-room indoor environments with curved walls.

The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method.

Ecological security patterns (ESPs) provide a solution for balancing the conflicts between ecological conservation and socio-economic development and maintaining sustainable regional development. Identifying sustainable ESPs should be a matter given attention to in order to meet future urban expansion needs. Based on multi-source eco-environmental datasets and remote sensing (RS) containing the history of land-use patterns, our study incorporated future land-use patterns into the ESP identification framework to reduce the potential impact of future urban expansion on the identified ESPs (i.e., ecological corridors and nodes) and to reserve space for future urban development from an urban development perspective. Considering the Changsha–Zhuzhou–Xiangtan (CZX) urban agglomeration, the results show that 57 corridors are extracted, within which 36 key corridors are mainly distributed in the Lukou and Wangcheng districts and Xiangtan and Changsha counties; an additional 21 potential corridors are mainly distributed in the urban built-up area and these corridors connect to Xiangjiang River or its tributary. In addition, 18 pinch points were identified, mainly located adjacent to the Xiangjiang River, Liuyang River, and Laodao River; 9 barrier points are distributed at the junctions of the main roads. A comparison of ESP identification with and without simulated land-use patterns showed the incorporation of future land-use patterns favoured sustainable ESP construction. Furthermore, we discuss the rationality and application of ESP identification from an urban development perspective and emphasize the trade-off between future land-use patterns and ecological conservation, better reflecting the forward-looking thinking of spatial planning.