Framework for constructing multimodal transport networks and routing using a graph database: A case study in London

Most prior multimodal transport networks have been organized as relational databases with multilayer structures to support transport management and routing; however, database expandability and update efficiency in new networks and timetables are low due to the strict database schemas. This study aimed to develop multimodal transport networks using a graph database that can accommodate efficient updates and extensions, high relation-based query performance, and flexible integration in multimodal routing. As a case study, a database was constructed for London transport networks, and routing tests were performed under various conditions. The constructed multimodal graph database showed stable performance in processing iterative queries, and efficient multi-stop routing was particularly enhanced. By applying the proposed framework, databases for multimodal routing can be readily constructed for other regions, while enabling responses to diversified routings, such as personalized routing through integration with various unstructured information, due to the flexible schema of the graph database

Automatic Floor Matching for 3D Indoor Spatial Modeling

With the advent of a variety of indoor location-based services, the necessity of 3D indoor model construction has become a significant issue worth noting and following. The aim of this study is to propose an algorithm of floor matching to construct a multi-floor building model. In this case, the characteristics of shape and position of lift features are used to search matched pairs in the algorithm. In addition, the vertical connectivity information also can be generated through the process. The proposed algorithm was applied to the Seoul National University Library to verify its suitability. In the case of a high-rise building, it is expected that a multi-floor building model can be constructed efficiently by automatically aligning the data generated per each floor through the method developed in this study

Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea

The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by field-based surveys, it is time-consuming and only a limited area can be updated at a time. Although land categories can be updated by remote sensing techniques, the update is typically performed through manual analysis, namely through a visually interpreted comparison between the newly generated land information and the existing cadastral maps. A cost-effective, fast alternative to the current surveying methods would improve the efficiency of land management. For this purpose, the present study analyzes the discrepancy between the existing cadastral map and the actual land use. Our proposed method operates in two steps. First, an up-to-date land cover map is generated from hyperspectral unmanned aerial vehicle (UAV) images. These images are effectively classified by a hybrid two- and three-dimensional convolutional neural network. Second, a discrepancy map, which contains the ratio of the area that is being used differently from the registered land use in each parcel, is constructed through a three-stage inconsistency comparison. As a case study, the proposed method was evaluated using hyperspectral UAV images acquired at two sites of Jeonju in South Korea. The overall classification accuracies of six land classes at Sites 1 and 2 were 99.93% and 99.75% and those at Sites 1 and 2 are 39.4% and 34.4%, respectively, which had discrepancy ratios of 50% or higher. Finally, discrepancy maps between the land cover maps and existing cadastral maps were generated and visualized. The method automatically reveals the inconsistent parcels requiring updates of their land category. Although the performance of the proposed method depends on the classification results obtained from UAV imagery, the method allows a flexible modification of the matching criteria between the land categories and land coverage. Therefore, it is generalizable to various cadastral systems and the discrepancy ratios will provide practical information and significantly reduce the time and effort for land monitoring and field surveying

Hybrid approach using deep learning and graph comparison for building change detection

Existing methods of detecting building changes from very-high-resolution (VHR) images are limited by positional displacement. Although various change detection (CD) methods including deep learning methods have been proposed, they are incapable of overcoming the aforementioned limitation. Therefore, this study proposes a two-step hybrid approach using deep learning and graph comparison to detect building changes in VHR temporal images. First, the building objects were detected using mask regional-convolutional neural networks (Mask R-CNN), wherein the centroid of the bounding box was extracted as the building node. Second, for each image, graphs were generated using the extracted building nodes. Accordingly, the changed nodes were identified based on iterative graph comparison, which could be voluntarily halted without setting thresholds by examining the changes in the proposed index while sequentially eliminating the building changes. To demonstrate the effectiveness of the proposed method, we experimentally tested the simulated images with synthetic changes and positional displacements. The results verified that the proposed method effectively reduced the false detections originating from positional inconsistencies. Consequently, the proposed method could overcome the limitations of conventional CD methods by employing a graph model based on the connectivity between adjacent buildings

Application of Style Transfer in the Vectorization Process of Floorplans (Short Paper)

As the market for indoor spatial information burgeons, the construction of indoor spatial databases consequently gain attention. Since floorplans are portable records of buildings, they are an indispensable source for the efficient construction of indoor environments. However, as previous research on floorplan information retrieval usually targeted specific formats, a system for constructing spatial information must include heuristic refinement steps. This study aims to convert diverse floorplans into an integrated format using the style transfer by deep networks. Our deep networks mimic a robust perception of human that recognize the cell structure of floorplans under various formats. The integrated format ensures that unified post-processing steps are required to the vectorization of floorplans. Through this process, indoor spatial information is constructed in a pragmatic way, using a plethora of architectural floorplans

Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities

The increasing complexity of modern buildings has challenged the mobility of people with disabilities (PWD) in the indoor environment. To help overcome this problem, this paper proposes a data model that can be easily applied to indoor spatial information services for people with disabilities. In the proposed model, features are defined based on relevant regulations that stipulate significant mobility factors for people with disabilities. To validate the model’s capability to describe the indoor spaces in terms that are relevant to people with mobility disabilities, the model was used to generate data in a path planning application, considering two different cases in a shopping mall. The application confirmed that routes for people with mobility disabilities are significantly different from those of ordinary pedestrians, in a way that reflects features and attributes defined in the proposed data model. The latter can be inserted as an IndoorGML extension, and is thus expected to facilitate relevant data generation for the design of various services for people with disabilities

Techniques for Updating Pedestrian Network Data Including Facilities and Obstructions Information for Transportation of Vulnerable People

Demand for a Pedestrian Navigation Service (PNS) is on the rise. To provide a PNS for the transportation of vulnerable people, more detailed information of pedestrian facilities and obstructions should be included in Pedestrian Network Data (PND) used for PNS. Such data can be constructed efficiently by collecting GPS trajectories and integrating them with the existing PND. However, these two kinds of data have geometric differences and topological inconsistencies that need to be addressed. In this paper, we provide a methodology for integrating pedestrian facilities and obstructions information with an existing PND. At first we extracted the significant points from user-collected GPS trajectory by identifying the geometric difference index and attributes of each point. Then the extracted points were used to make an initial solution of the matching between the trajectory and the PND. Two geometrical algorithms were proposed and applied to reduce two kinds of errors in the matching: on dual lines and on intersections. Using the final solution for the matching, we reconstructed the node/link structure of PND including the facilities and obstructions information. Finally, performance was assessed with a test site and 79.2% of the collected data were correctly integrated with the PND

Upregulation of autophagy by Ginsenoside Rg2 in MCF-7 cells

Autophagy is a major intracellular degradation process that plays an important role in cell survival, stress responses, nutrient sensing and development. Our previous studies have shown that Rg2, a triterpenoid saponin contained in ginseng, protects cells against UVB-induced genotoxicity by increasing DNA repair, in possible association with modulation of protein levels involved in p53 pathway. In this study, we determined an upregulation of autophagy by Rg2. Rg2 treatment for 24 h in MCF-7, a breast cancer cell, did not show cytotoxicity up to 200 mu M. Rg2 also upregulated the level of p-p53, p-AMPK, p-ACC, Atg-7 and LC3-II and decreased the level of p62 in concentration-dependent manners. We also determined the level of p53, AMPK, p62, Atg-7 and LC3 after UVB exposure and subsequent incubation in growth medium for 24 h. UVB increased the level of p-p53, p-AMPK, p-ACC and decreased the levels of p62, Atg-7 and LC3-II. Interestingly, Rg2 treatment for 24 h after UVB exposure increased the levels of p-p53, p-AMPK, p-ACC, Atg-7 and LC3-II and decreased the level of cyclobutane pyrimidine dimer, a UVB-induced DNA damage in concentration-dependent manners. All these results suggest that Rg2 increased autophagy and decreased UVB-induced DNA damage, in possible association with the modulation of protein levels in p53- and autophagic pathways

The relationship between exposure to environmental noise and risk of atopic dermatitis, asthma, and allergic rhinitis

Background: Noise is defined as unwanted sound. It may induce negative emotions and mental health problems and may even lead to increased suicide risk. However, the impact of noise exposure on environmental diseases and disease severity is not well understood. This study aimed to elucidate the association between night-time noise exposure and the prevalence of environmental diseases in South Korea. Methods: We conducted an analysis of the Environmental Disease Database provide by the National Health Insurance Service (NHIS) from 2013 to 2017. After spatially interpolating the noise data provided by the National Noise Information System (NNIS), night-time noise values in the district level were obtained by calculating the mean noise values at the administrative district level. The linear regression analyses were performed to test the association between the age-standardized prevalence ratio (SPR) and the night-time noise exposure in the district level. Results: In areas with high night-time noise exposure (≥55 dB), the SPR for atopic dermatitis and allergic rhinitis were 1.0515 (95 % confidence interval [CI]:1.0508–1.0521) and 1.0202 (95 % CI:1.0201–1.0204), respectively, which were higher than those in the general population. The SPR for environmental diseases, including atopic dermatitis, asthma, and allergic rhinitis, was 1.0104 (95 % CI:1.0103–1.0105). Additionally, a significant linear association was observed between the level of nocturnal noise exposure and the total hospitalization period for atopic dermatitis (β = 399.3, p < 0.01). Conclusion: We provide evidence of a significant association between night-time environmental noise and environmental diseases, particularly atopic dermatitis and allergic rhinitis. Furthermore, we observed a significant linear association between night-time noise exposure and the severity of atopic dermatitis