With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level’s ontology to the data layer. Then, using the D2RQ tool, a SPARQL protocol and resource description framework query language (SPARQL) endpoint service is used to describe functions such as query and reasoning of the knowledge graph. The graph database is then used to display the knowledge graph. Finally, the knowledge graph is used to describe the correlation between the fields of surveying and remote sensing.

Panoramic mosaicking is a key step in the process of aerial photogrammetry, which combines multiple images to form a single image with a wide field of view. In this article, a refined global seam-line network generation method based on bounded Voronoi diagrams and watershed segmentation algorithm is proposed for mosaicking unmanned aerial vehicle (UAV) orthoimages. First of all, valid range polygons and image centres are extracted from orthoimages. Second, an initial seam-line network is generated by a bounded Voronoi diagrams and polygon Boolean operations. Third, the seam-line network is refined by marker-controlled watershed segmentation algorithm. Finally, a photomosaic is produced by multiband blending mosaic. The panoramic mosaicking approach is comprehensively discussed in terms of data volume and degree of overlapping. Experimental studies performed on several groups of UAV orthoimages show that the proposed approach provides a feasible and promising solution to large-scale UAV photomosaic generation.

Multi-view stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this paper describes a new benchmark dataset called the LuoJia-MVS dataset, as well as a new deep neural network known as HDC-MVSNet. The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of three-dimensional (3D) convolutional blocks, and “true 3D” deformable 3D convolutional layers are specifically designed by considering the afore-mentioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods, and confirmed that the newly built dataset can provide an effective benchmark.

Human activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of human activity hotspots from their scaling pattern in a reliable way. Specifically, a large number of stopping locations are extracted from trajectory data, which are then aggregated into activity hotspots. Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land use. Importantly, this scaling pattern remains stable over time. This finding inspires us to devise an allometric ruler to identify the activity hotspots, whose functionality could be reliably estimated using the stopping locations. Thereafter, a novel Bayesian inference model is proposed to infer their urban functionality, which examines the spatial and temporal information of stopping locations covering 75 days. Experimental results suggest that the functionality of identified activity hotspots are reliably inferred by stopping locations, such as the railway station.

Although spatial and spectral resolutions of remotely sensed data have been improved, the usage of multispectral imagery is not sufficient for urban feature classification. This article addresses the problem of automated classification by integrating airborne lidar range data and aerial imagery. In this study, the classification procedure is divided into three phases. We first use the lidar range data to obtain the coarse lidar-based classification results, by which a lidar-driven labelled image and a lidar-driven high-rise object mask are acquired in this phase. Then, at the image-based classification level, we train samples based on the lidar-driven labelled image and conduct maximum likelihood classification experience with the lidar-driven normalized digital surface model as a high-rise object mask. Finally, we propose a knowledge-based cross-validation (KBCV) for misclassification between the lidar-based classification results and the image-based classification results. Experimental results are presented to demonstrate the benefits of the training sample selection of the lidar-driven labelled image, using the lidar-driven high-rise object mask, and the greater classification accuracy of the KBCV.

3-D digital city vividly presents a real-world city and has been widely needed for many application domains. Numerous pole-like objects (PLOs), including trees, street lamps, and traffic signs, are an indispensable part of 3-D digital city. The point cloud data of mobile laser scanning (MLS) systems can capture both the geometric shape and geospatial coordinates of the PLOs while moving along the roads. This article is motivated to accurately extract and efficiently model PLOs from the point cloud data. The main contributions of this article are as follows: 1) a divergence-incorporated clustering algorithm is proposed to extract trunks accurately from the pole-like 3-D distribution perspective of point cloud; 2) an adaptive growing strategy of alternately extending and updating 3-D neighbors is proposed to get the complete canopy points of various shapes and density; and 3) the part-based modeling is proposed to synthesize the point cloud of PLOs with meaningful 3-D shapes, providing a way to model objects for the 3-D digital city vividly and efficiently. The proposed method is tested on three data sets with different interference, shape of the canopy, and point density. Experimental results demonstrate that the proposed method can extract and model the PLOs effectively and efficiently for 3-D digital city. The precision of trunk extraction is 98.45%, 98.08%, and 92.39%, the completeness of canopy extraction is 80.54%, 89.84%, and 89.29%, and the modeling time for a PLO is 0.011, 0.038, and 0.063 s in three data sets.