• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.6_1
• /
• pp.565-574
• /
• 2007

Because of most current cadastral systems maintain 2D geometric descriptions of parcels linked to administrative records, the system may not reflect current tendency to use space above and under the surface. The land has been used in multi-levels, e.g. constructions of multi-used complex buildings, subways and infrastructure above/under the ground. This cadastre situation of multilevel use of lands cannot be defined as cadastre objects (2D parcel-based) in the cadastre systems. This trend has requested a new system in which right to land is clearly and indisputably recorded because a right of ownership on a parcel relates to a space in 3D, not any more relates to 2D surface area. Therefore, this article proposes a 3D spatial data model to represent geometrical and topological data of 3D (property) situation on multilevel uses of lands in 3D cadastre systems, and a conceptual 3D cadastral model in Korea to design a conceptual schema for a 3D cadastre. Lastly, this paper presents the results of an experimental implementation of the 3D Cadastre to perform topological analyses based on 3D Network Data Model to identify spatial neighbors.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.1
• /
• pp.19-25
• /
• 2001

A novel approach for the feature extraction of handwritten characters is proposed by using spatial filtering neural networks with 4 layers. The proposed system first removes rough pixels which are easy to occur in handwritten characters. The system then extracts and removes the boundary information which have no influence on characters recognition. Finally, The system extracts feature information and removes the noises from feature information. The spatial filters adapted in the system correspond to the receptive fields of ganglion cells in retina and simple cells in visual cortex. With PE2 Hangul database, we perform experiments extracting features of handwritten characters recognition. It will be shown that the network can extract feature informations from handwritten characters successfully.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3685-3707
• /
• 2021

In recent years, facial attribute editing has been successfully used to effectively change face images of various attributes based on generative adversarial networks and encoder-decoder models. However, existing models have a limitation in that they may change an unintended part in the process of changing an attribute or may generate an unnatural result. In this paper, we propose a model that improves the learning of the attention mask by adding a spatial attention mechanism based on the unified selective transfer network (referred to as STGAN) using semi-supervised learning. The proposed model can edit multiple attributes while preserving details independent of the attributes being edited. This study makes two main contributions to the literature. First, we propose an encoder-decoder model structure that learns and edits multiple facial attributes and suppresses distortion using an attention mask. Second, we define guide masks and propose a method and an objective function that use the guide masks for multiple facial attribute editing through semi-supervised learning. Through qualitative and quantitative evaluations of the experimental results, the proposed method was proven to yield improved results that preserve the image details by suppressing unintended changes than existing methods.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.223-224
• /
• 2021

Recently, k-farthest neighbor (kFN) queries have not as much attention as k-nearest neighbor (kNN) queries. Therefore, this study considers moving k-farthest neighbor (MkFN) queries for spatial network databases. Given a positive integer k, a moving query point q, and a set of data points P, MkFN queries can constantly retrieve k data points that are farthest from the query point q. The challenge with processing MkFN queries in spatial networks is to avoid unnecessary or superfluous distance calculations between the query and associated data points. This study proposes a batch processing algorithm, called MOFA, to enable efficient processing of MkFN queries in spatial networks. MOFA aims to avoid dispensable distance computations based on the clustering of both query and data points. Moreover, a time complexity analysis is presented to clarify the effect of the clustering method on the query processing time. Extensive experiments using real-world roadmaps demonstrated the efficiency and scalability of the MOFA when compared with a conventional solution.

• Spatial Information Research
• /
• v.20 no.1
• /
• pp.91-103
• /
• 2012

Recently, there has been much interest in the middleware that can smoothly acquire and analyze Geosensor information which includes sensor readings, location, and its surrounding spatial information. In relation to development of the middleware, researchers have proposed various algorithms for energy-efficient information filtering in Geosensor networks and have proposed Geosensor web technologies which can efficiently mash up sensor readings with spatial information on the web, also. The filtering algorithms and Geosensor Web technologies have contributions on energy-efficiency and OpenAPI, however the algorithms and technologies could not support easy and rapid development of u-GIS applications that need various Geosensor networks. Therefore, we propose a new Geosensor network middleware that can dramatically reduce the time and cost required for development of u-GIS applications that integrate heterogeneous Geosensor networks. The proposed middleware has several merits of being capable of acquiring heterogeneous Geosensor information using the standard SWE and an extended SQL, optimally performing various attribute and spatial operators, and easily integrating various Geosensor networks. Finally, we clarify our middleware's distinguished features by developing a prototype that can monitor environmental information in realtime using spatial information and various sensor readings of temperature, humidity, illumination, imagery, and location.

• Journal of the Economic Geographical Society of Korea
• /
• v.23 no.1
• /
• pp.93-109
• /
• 2020

The purpose of this study is to examine the effects of industrialization and urbanization of Gwangyang Bay Region on the change of urban system and spatial structure between triangle-cities located in Gwangyang Bay, Yeosu City, Suncheon City, and Gwangyang City, one of the famous industrial zones in Korea. Large-scale development projects carried out by the central government in the Gwangyang Bay Region such as construction of the Second Oil Refinery in the mid-1960s, completion of the POSCO Gwangyang Steelworks in the mid-1980s, construction of the Gwangyang Port Container Terminal in 1987 and designation of the Gwangyang Bay Area Free Economic Zone in 2003, and EXPO 2012 Yeosu Korea, affected to changes of the urban system and spatial structure between triangle-cities in Gwangyang Bay Region. The above four-development projects transformed the urban and spatial structures between the three cities in the Gwangyang Bay Region from a mononuclear urban system centered on Suncheon to a network city system. Historically, Suncheon has served as an exclusive center in the eastern region of Jeonnam, including the Gwangyang Bay Region. However, the hosting of the 2012 Yeosu Expo Korea is reorganizing the three cities into a network-type spatial structure with the strengthening of connectivity and integration in the region. And this trend is expected to intensify in the future.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.681-692
• /
• 2019

In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.5
• /
• pp.789-796
• /
• 2012

Fingerprinting is a widely used positioning technology for received signal strength (RSS) based wireless local area network (WLAN) positioning system. Though spatial RSS variation is the key factor of the positioning technology, temporal RSS variation needs to be considered for more accuracy. To deal with the spatial and temporal RSS characteristics within a unified framework, this paper proposes an extended signal propagation mode (ESPM) and a fingerprint generation method. The proposed spatiotemporal fingerprint generation method consists of two algorithms running in parallel; Kalman filtering at several measurement-sampling locations and Kriging to generate location fingerprints at dense reference locations. The two different algorithms are connected by the extended signal propagation model which describes the spatial and temporal measurement characteristics in one frame. An experiment demonstrates that the proposed method provides an improved positioning accuracy.

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.637-643
• /
• 2020

In this study, the information from environmental impact statements was converted into spatial data because environmental data from development sites are collected during the environmental impact assessment (EIA) process. Spatiotemporal big data were built from environmental spatial data for each environmental medium for 2,235 development sites during 2007-2018, available from public data portals. Comparing air-quality monitoring stations, 33,863 measurement points were constructed, which is approximately 75 times more measurement points than that 452 in Air Korea's real-time measurement network. Here, spatiotemporal big data from 2,677,260 EIAs were constructed. In the future, such data might be used not only for EIAs but also for various spatial plans.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.866-868
• /
• 2003

In this paper, we developed those Directory Services based on the Web-Service, because Web-Service environments provide a suitable method to gather requested information in an appropriate way. The proposed architecture cooperate with other OpenLS(Open Location Service) Core Services ( Location Utility Service and Router Determin ation service) and is an interoperability one - it ident ifies those global elements of the global Web-Services network that are required in order to ensure interoperability between Web-Services. In this paper, a new architecture of Directory Service with OpenLS Core Services is proposed and tested in OpenLS Core Services environments.