Recently, LiDAR (Light Detection and Ranging)-based place recognition, has been widely concerned because of its robustness to light conditions, seasonal changes, and viewpoint variations. Unlike most of existing methods which represent the whole point cloud scenes with global descriptors, we treat the LiDAR-based place recognition problem as a scene overlap prediction task and propose an end-to-end overlap prediction network, which consists of a feature learning backbone, a feature enhancement module, and an overlap prediction module. Based on the prediction result for each point, the overlapping ratios between two point clouds are computed and used to predict whether these two point clouds are at the same place. In addition, to promote the computational efficiency and reduce the model complexity, a lightweight feature learning backbone is also adopted. The experiments conducted on the KITTI Odometry dataset demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods. The lightweight method also obtains 2x inference speed with little performance degradation compared with the vanilla method.

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

Current point cloud registration methods are mainly based on local geometric information and usually ignore the semantic information contained in the scenes. In this article, we treat the point cloud registration problem as a semantic instance matching and registration task and propose a deep semantic graph matching method (DeepSGM) for large-scale outdoor point cloud registration. First, the semantic categorical labels of 3D points are obtained using a semantic segmentation network. The adjacent points with the same category labels are then clustered together using the Euclidean clustering algorithm to obtain the semantic instances, which are represented by three kinds of attributes including spatial location information, semantic categorical information, and global geometric shape information. Second, the semantic adjacency graph is constructed based on the spatial adjacency relations of semantic instances. To fully explore the topological structures between semantic instances in the same scene and across different scenes, the spatial distribution features and the semantic categorical features are learned with graph convolutional networks (GCNs), and the global geometric shape features are learned with a PointNet-like network. These three kinds of features are further enhanced with the self-attention and cross-attention mechanisms. Third, the semantic instance matching is formulated as an optimal transport problem, and solved through an optimal matching layer. Finally, the geometric transformation matrix between two point clouds is first estimated by the singular value decomposition (SVD) algorithm and then refined by the ICP algorithm. Experimental results conducted on the KITTI Odometry dataset demonstrate that the proposed method improves the registration performance and outperforms various state-of-the-art methods.

As we all know, the combined block adjustment of very high-resolution satellite stereo images (VHRSI) and spaceborne laser altimetry data (ICESat-2 data) can provide support for 1:10,000 topographic mapping without the use of ground control points (GCPs). However, there are still many challenges in the combined adjustment processing, such as the selection of elevation control points and the registration of two types of data. To address these issues in combined adjustment, a preliminary method for selecting elevation control points is proposed based on various laser attribute information, building utilizing an analysis of ATL08 product characteristics. Furthermore, virtual planar control points and elevation checkpoints are introduced, and a combined block adjustment strategy is designed to refine the application of laser elevation control points and resolve registration issues between the stereo image data and laser altimetry data during the adjustment process. The proposed strategy was implemented and evaluated across three representative experimental areas. In the experiments, the elevation root-mean-square errors of processed stereo images in all three areas were found to be below 1 m, while the planimetric accuracy remained consistent with the results obtained from the free network adjustment. The findings suggest that the developed strategy effectively addresses the challenges associated with elevation control point selection and data registration in the combined adjustment process, significantly enhancing elevation accuracy while preserving the planar accuracy of stereo images. Therefore, the proposed combined block adjustment strategy supports the endeavor of global 1:10,000 mapping without the need for GCPs and promotes the application of VHRSI and ICESat-2 data in land surface surveying and mapping.

ICESat-2 can obtain high-precision three-dimensional measurement information of targets and has a unique advantage in determining global elevation control points. However, due to the influence of the atmospheric environment, target characteristics, hardware equipment, and other factors, its elevation accuracy is not highly reliable, and not all data points can be used as control points. To obtain high-precision elevation control points from ICESat-2 data products, an extraction strategy that combines the accuracy and location requirements for control points was developed based on the ATL08 product. Multiple attribute parameters were incorporated into the extraction procedure, including terrain factor information, segment elevation information, cloud confidence flag, surface coverage data, topographic photon quantity, and photon height difference information. The extraction approach was then performed and analyzed using experimental data from the Hanzhong area in Shanxi province and the Songshan area in Henan province. In the experiments, the root-mean-square error (RMSE) of the extracted data points in two areas were both about 0.5 m and could reach 0.3 m after eliminating gross error points caused by surface changes and misclassification. The results suggest that the developed strategy takes into account the point location requirements of control points, can overcome the influence of complex terrain and ground objects and significantly improve the overall accuracy of obtained control points. Therefore, the proposed extraction strategy can support the establishment of the global elevation control point database and promote the application of ICESat-2 data in land surface surveying and mapping.