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- 저자 : Alexis Richard C. Claridades (검색결과 2 건)
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Alexis Richard C. Claridades,최현상,이지영 한국측량학회 2023 한국측량학회지 Vol.41 No.5
Spatial data is important for virtually representing the real world and is essential in developing applications for making informed decisions. With the growing interest in seamless indoor-outdoor environments, spatial data from different sources exists in various formats for use in LBS (Location-Based Services). Previous research has utilized deep learning for indoor omnidirectional images to generate NRS (Node-Relation Structure), a network-based topological data, for supporting spatial analysis for navigation while providing visualization. This study proposes an approach to detect building entrances in street view omnidirectional images through a deep learning-based object detection algorithm for supporting indoor-outdoor LBS. This paper focuses on formulating refinement conditions for constructing an image training dataset that combines both an open dataset and directly captured omnidirectional images to address the challenge of establishing a huge volume of images for training the object detection model. By applying the conditions, the mAP (mean Average Precision) of 61.20% obtained from training with open data increased to 85.72%, and applying image augmentation methods improved the mAP to 87.42%. These results show that the proposed conditions can be used as a framework for constructing generalized training data that results in accurate entrance detection in street view images, regardless of the study area.
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Developing a Strategy for Integrating Network Datasets for Supporting Multimodal Transportation
Alexis Richard C. Claridades,Jiyeong Lee 한국측량학회 2023 한국측량학회지 Vol.41 No.6
As urban areas have been undergoing accelerated development, there has also been increasing interest in solving problems in mobility. Experts have identified multimodal transportation as a key concept in addressing this problem by providing transportation options. In providing LBS (Location-based Services), spatial datasets, particularly network data, are essential in representing the navigable spaces for each mode of transportation. However, previous studies on spatial data modeling of multimodal transportation have omitted the spaces that exist between the spaces represented by such network data. Additionally, the integration of these datasets for providing such services is still faced with numerous problems, such as lack of specification, the variety in data formats, potential difficulties in data conversion, or computational burdens due to reliance on geometric properties. This paper proposes a method for integrating network datasets to represent the connectivity of various navigable spaces for supporting multimodal transportation based on topological relationships, described in a spatial data model expressed in UML (Unified Modeling Language). Moreover, using sample data, we demonstrate the models potential for representing seamless travel across transportation modes in a routing experiment. This paper presents a topological relationship-based method to integrate existing network data representing multimodal transportation despite the differences in data format or standard.
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