Geometric-semantic coherent building models are demanding in many geoscience applications. Conventional building modeling methods often rely on successive roof plane segmentation and fitting. The subsequent reconstruction procedure is difficult to assure topologic consistency and geometric accuracy. This paper starts with a library of predefined building models or primitives, including pyramid, gable, hip, etc. We propose an optimal model fitting approach that holistically determines all of its parameters from segmented point cloud data. The approach is formulated as an optimization problem that minimizes the point-to-mesh distance between the point cloud and the meshed primitive model. Necessary constraints in the form of inequality equations are introduced to assure correct and reliable solution. For complex roofs consisting of several predefined primitive models, a hierarchical procedure is presented to reconstruct the major roof model and its superstructures sequentially. The CityGML LoD2 model is created from the parameterized primitives. The quality and performance of this approach are evaluated with airborne lidar and photogrammetric point clouds. Based on the experiments with 910 buildings, the primitive fitting accuracy is 7.8cm and the corner uncertainty is 0.36m or 0.78 times the ground point spacing; the building boundary consistency is 89.6%. The study demonstrates a piecewise continuous polyhedral building model can be determined through a holistic parameter optimization process. The resultant building models intrinsically best fit to the input point cloud with topologic integrity. The approach not only qualitatively generates semantic building models, but also exhibits the potential for building reconstruction over large areas.

User-generated content (UGC) is a relatively young field of research; however, it has been proven useful in disciplines such as hospitality and tourism, to elicit public opinions of place usage. In landscape architecture and urban planning, UGC has been used to understand people’s emotions and movement in a space, while other areas and additional functions are yet to be discovered. This paper explores the capability of UGC in revealing city-scale park management problems and the applicability of social media as a future tool in bridging visitor feedback to city parks and recreation department staff. This research analyzed the spatial characteristics and patterns of Google Maps review quantity, rating score, and review comments. The results of this pilot study indicate the spatial and structural features of the Chicago parks and demonstrate distribution problems, financial investment priority concerns, park usage characteristics, and user preferences of the park attributes. Findings affirm that user-generated online reviews can be used as an alternative and self-reporting data source to effectively assess the natural performance and users’ experience of city parks and can potentially serve as an evaluative tool for public park management.

Photogrammetry is experiencing an era of democratization mostly due to the popularity and availability of many commercial off-the-shelf devices, such as drones and smartphones. They are used as the most convenient and effective tools for high-resolution image acquisition for a wide range of applications in science, engineering, management, and cultural heritage. However, the quality, particularly the geometric accuracy, of the outcomes from such consumer sensors is still unclear. Furthermore, the expected quality under different control schemes has yet to be thoroughly investigated. This paper intends to answer those questions with a comprehensive comparative evaluation. Photogrammetry, in particular, structure from motion, has been used to reconstruct a 3D building model from smartphone and consumer drone images, as well as from professional drone images, all under various ground control schemes. Results from this study show that the positioning accuracy of smartphone images under direct geo-referencing is 165.4 cm, however, this could be improved to 43.3 cm and 14.5 cm when introducing aerial lidar data and total station surveys as ground control, respectively. Similar results are found for consumer drone images as well. For comparison, this study shows the use of the professional drone is able to achieve a positioning accuracy of 3.7 cm. Furthermore, we demonstrate that through the combined use of drone and smartphone images we are able to obtain full coverage of the entire target with a 2.3 cm positioning accuracy. Our study concludes that smartphone images can achieve an accuracy equivalent to consumer drone images and can be used as the primary data source for building facade data collection.

Indoor structures are composed of ceilings, walls and floors that need to be modeled for a variety of applications. This paper proposes an approach to reconstructing models of indoor structures in complex environments. First, semantic pre-processing, including segmentation and occlusion construction, is applied to segment the input point clouds to generate semantic patches of structural primitives with uniform density. Then, a primitives extraction method with detected boundary is introduced to approximate both the mathematical surface and the boundary of the patches. Finally, a constraint-based model reconstruction is applied to achieve the final topologically consistent structural model. Under this framework, both the geometric and structural constraints are considered in a holistic manner to assure topologic regularity. Experiments were carried out with both synthetic and real-world datasets. The accuracy of the proposed method achieved an overall reconstruction quality of approximately 4.60 cm of root mean square error (RMSE) and 94.10% Intersection over Union (IoU) of the input point cloud. The development can be applied for structural reconstruction of various complex indoor environments.

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.