Urban Sensing Technologies

The seamless integration of indoor and outdoor positioning has gained considerable attention due to its practical implications in various fields. This paper presents an innovative approach aimed at detecting and delineating outdoor, indoor, and transition areas using a time series analysis of Global Navigation Satellite System (GNSS) error statistics. By leveraging this contextual understanding, the decision-making process between GNSS-based and Visual-Inertial Odometry (VIO) for trajectory estimation is refined, enabling a more robust and accurate positioning. The methodology involves three key steps: proposing the division of our context environment into a set of areas (indoor, outdoor, and transition), exploring two methodologies for the classification of space based on a time series of GNSS error statistics, and refining the trajectory estimation strategy based on contextual knowledge. Real data across diverse scenarios validate the approach, yielding trajectory estimations with accuracy consistently below 10 m. Full article

The vital role of civil engineering is to enable the development of modern cities and establish foundations for smart and sustainable urban environments of the future. Advanced sensing technologies are among the instrumental methods used to enhance the performance of civil engineering infrastructures and address the multifaceted challenges of future cities. Through this study, we discussed the shortcomings of traditional sensors in four primary civil engineering domains: construction, energy, water, and transportation. Then, we investigated and summarized the potential of quantum sensors to contribute to and revolutionize the management of civil engineering infrastructures. For the water sector, advancements are expected in monitoring water quality and pressure in water and sewage infrastructures. In the energy sector, quantum sensors may facilitate renewables integration and improve grid stability and buildings’ energy efficiency. The most promising progress in the construction field is the ability to identify subsurface density and underground structures. In transportation, these sensors create many fresh avenues for real-time traffic management and smart mobility solutions. As one of the first-in-the-field studies offering the adoption of quantum sensors across four primary domains of civil engineering, this research establishes the basis for the discourse about the scope and timeline for deploying quantum sensors to real-world applications towards the quantum transformation of civil engineering. Full article

Accurately mapping urban built-up areas is critical for monitoring urbanization and development. Previous studies have shown that Night light (NTL) data is effective in characterizing the extent of human activity. But its inherently low spatial resolution and saturation effect limit its application in the construction of urban built-up extraction. In this study, we developed a new index called VNRT (Vegetation, Nighttime Light, Road, and Temperature) to address these challenges and improve the accuracy of built-up area extraction. The VNRT index is the first to fuse the Normalized Difference Vegetation Index (NDVI), NPP-VIIRS Nighttime NTL data, road density data, and land surface temperature (LST) through factor multiplication. To verify the good performance of VNRT in extracting built-up areas, the built-up area ranges of four national central cities in China (Chengdu, Wuhan, Xi’an, and Zhengzhou) in 2019 are extracted by the local optimum thresholding method and compared with the actual validation points. The results show that the spatial distribution of VNRT is highly consistent with the actual built-up area. THE VNRT increases the variability between urban built-up areas and non-built-up areas, and can effectively distinguish some types of land cover that are easily ignored in previous urban indices, such as urban parks and water bodies. The VNRT index had the highest Accuracy (0.97), F1-score (0.94), Kappa coefficient (0.80), and overall accuracy (92%) compared to the two proposed urban indices. Therefore, the VNRT index could improve the identification of urban built-up areas and be an effective tool for long-term monitoring of regional-scale urbanization. Full article

Old residential communities are integral parts of urban areas, with their environmental quality affecting residents’ well-being. Spontaneous informal spaces (SIS) often emerge within these communities. These are predominantly crafted by the elderly using discarded materials and negatively impact the environmental quality of communities. Understanding SIS emergence patterns is vital for enhancing the environmental quality of old communities; however, methodologies fall short in terms of the quantification of these emergence patterns. This study introduces a groundbreaking approach, merging drone oblique photography technology with space syntax theory, to thoroughly analyze SIS types, functions, and determinants in five Tianjin communities. Utilizing drones and the Depthmap space syntax tool, we captured SIS characteristics and constructed topological models of residences and traffic patterns. We further explored the intrinsic relationships between architectural layout, road traffic, and SIS characteristics via clustering algorithms and multivariate correlation analysis. Our results reveal that architectural layout and road traffic play decisive roles in shaping SIS. Highly accessible regions predominantly feature social-type SIS, while secluded or less trafficked zones lean towards private-type SIS. Highlighting the elderly’s essential needs for greenery, interaction, and basic amenities, our findings offer valuable insights into the revitalization of outdoor spaces in aging communities, into the fostering of urban sustainability and into the nurturing of a balanced relationship between humans and their surroundings. Full article

The accuracy of point cloud processing results is greatly dependent on the determination of the voxel size and shape during the point cloud voxelization process. Previous studies predominantly set voxel sizes based on point cloud density or the size of ground objects. Voxels are mostly considered square in shape by default. However, conventional square voxels are not applicable to all surfaces. This study proposes a method of using cuboid voxels to extract urban road boundary points using curb points as road boundary points. In comparison with conventional cubic voxels, cuboid voxels reduce the probability of mixed voxels at the road curb, highlight two geometric features of road curb voxels (i.e., normal vector and distribution dimension), and improve the accuracy of road curb point extraction. In this study, ground points were obtained using cloth simulation filtering. First, the cuboid-based voxelization of ground points was performed. Then, taking the voxel as a unit, two geometric features, namely, the normal vector of the voxel and the linear dimension of the point distribution in the voxel, were calculated. According to these geometric features, the voxels that met the conditions were regarded as candidate road curb voxels, and the points in them as candidate road curb points. Afterward, filtering was applied using the intensity value to eliminate the bottom points of fences, street trees, and other ground objects in the candidate road curb points. Finally, noise points were eliminated according to the clustering results of the density based spatial clustering of applications with noise (DBSCAN) algorithm. In this study, point cloud data obtained by the SSW vehicle-mounted mobile mapping system and three-point cloud datasets in the IQmulus & TerraMobilita competition dataset were used to experimentally extract road curbs. Results showed that this method could effectively extract road curb points as the precision of the four groups of data results was over 90% and the quality coefficient reached over 75%. Full article

With the proliferation and development of social media platforms, social media data have become an important source for acquiring spatiotemporal information on various urban events. Providing accurate spatiotemporal information for events contributes to enhancing the capabilities of urban management and emergency responses. However, existing research regarding mining spatiotemporal information of events often solely focuses on textual content and neglects data from other modalities such as images and videos. Therefore, this study proposes an innovative spatiotemporal information extraction method, which extracts the spatiotemporal information of events from multimodal data on Weibo at coarse- and fine-grained hierarchical levels and serves as a beneficial supplement to existing urban event monitoring methods. This paper utilizes the “20 July 2021 Zhengzhou Heavy Rainfall” incident as an example to evaluate and analyze the effectiveness of the proposed method. Results indicate that in coarse-grained spatial information extraction using only textual data, our method achieved a spatial precision of 87.54% within a 60 m range and reached 100% spatial precision for ranges beyond 200 m. For fine-grained spatial information extraction, the introduction of other modal data, such as images and videos, resulted in a significant improvement in spatial error. These results demonstrate the ability of the MIST-SMMD (Method of Identifying Spatiotemporal Information of Social Media Multimodal Data) to extract spatiotemporal information from urban events at both coarse and fine levels and confirm the significant advantages of multimodal data in enhancing the precision of spatial information extraction. Full article

The comprehensive and accurate assessment of the indoor air quality (IAQ) in large spaces, such as offices or multipurpose facilities, is essential for IAQ management. It is widely recognized that various IAQ factors affect the well-being, health, and productivity of indoor occupants. In indoor environments, it is important to assess the IAQ in places where it is difficult to install sensors due to space constraints. Spatial interpolation is a technique that uses sample values of known points to predict the values of other unknown points. Unlike in outdoor environments, spatial interpolation is difficult in large indoor spaces due to various constraints, such as being separated into rooms by walls or having facilities such as air conditioners or heaters installed. Therefore, it is necessary to identify independent or related regions in indoor spaces and to utilize them for spatial interpolation. In this paper, we propose a spatial interpolation technique that groups points with similar characteristics in indoor spaces and utilizes the characteristics of these groups for spatial interpolation. We integrated the IAQ data collected from multiple locations within an office space and subsequently conducted a comparative experiment to assess the accuracy of our proposed method in comparison to commonly used approaches, such as inverse distance weighting (IDW), kriging, natural neighbor interpolation, and the radial basis function (RBF). Additionally, we performed experiments using the publicly available Intel Lab dataset. The experimental results demonstrate that our proposed scheme outperformed the existing methods. The experimental results show that the proposed method was able to obtain better predictions by reflecting the characteristics of regions with similar characteristics within the indoor space. Full article

In this study, the operation of a final disposal site for municipal solid waste in the state of Chiapas, in Mexico, was evaluated. Several spatial analyses and Geographic Information Technology (GIT) tools were used. It was found that the site’s current operation and location are deficient, partially complying with regulations. The gaseous dispersion is not far-reaching (from 100 to 8725 µg/m³ for landfill gas, and from 0.01 to 0.35 µg/m³ for H₂S) but requires attention to avoid olfactory unpleasantness. Liquid emissions (conservative pollutants) move in the east direction of the final disposal site, which can damage the environmental infrastructure (water supply wells) in the long term. The highest and lowest concentrations were found in years 1 (12,270 mg/m³) and 20 (1080 mg/m³), respectively. Thermal emissions around the dumping site are important due to the formation of microclimatic zones. Temperature differences were found during the analysis period, ranging from 8.37 °C in summer to 2.49 °C in winter, which are due to waste decomposition processes and anthropogenic activities. Finally, the change in land use around the dumping site increased at a rate of 5.82% per year, mainly due to the growth of homes, communication routes, and shopping centers. Full article

Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling. Full article