• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.591-598
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• 2020

The all revision of the Road Name Address Act, which contains the contents to be used by expanding the road name address as a means of indicationg the location, has been resloved by the National Assembly. Addresses will be assigned to large-sized facilities (3D mixed-use complex spaces). Here, the 3D (Three-dimensional) address is assigned an indoor path section in the inner passage, dividing the section at intervals. The 3D address will be built on the address information map. For 3D address, data should be built and managed for a 3D complex space(indoor space). Therefore, in this study, the object of the 3D address is defined based on the address conceptual model defined in the international standard, and the 3D address data model is proposed based on IndoorGML. To this, it is proposed as a method of mapping the Core and Navigation module of IndoorGML so that the entity of the 3D address can be expressed in IndoorGML. This study has a limitation in designing a 3D address data model only, but it is meaningful that it suggested a standard for constructing 3D address data in the future.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.53-65
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• 2021

C-ITS(Cooperative Intelligent Transport System) that pursues traffic safety and convenience uses various sensors to generate traffic information. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of CCTV in collecting video information has become more important due to advances in AI(Artificial Intelligence) technology. In this study, we propose to identify and track dynamic objects(vehicles, people, etc.) in CCTV images, and to analyze and provide information about them in various environments. To this end, we conducted identification and tracking of dynamic objects using the Yolov4 and Deepsort algorithms, establishment of real-time multi-user support servers based on Kafka, defining transformation matrices between images and spatial coordinate systems, and map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that CCTVs can serve as important sensors to provide relevant information by analyzing road conditions in real time in terms of road infrastructure beyond a simple monitoring role.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1025-1034
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• 2022

In this paper, we propose a post-processing method through interpolation of hole regions that occur when extracting point clouds. When image matching is performed on stereo image data, holes occur due to occlusion and building façade area. This area may become an obstacle to the creation of additional products based on the point cloud in the future, so an effective processing technique is required. First, an initial point cloud is extracted based on the disparity map generated by applying stereo image matching. We transform the point cloud into a grid. Then a hole area is extracted due to occlusion and building façade area. By repeating the process of creating Triangulated Irregular Network (TIN) triangle in the hall area and processing the inner value of the triangle as the minimum height value of the area, it is possible to perform interpolation without awkwardness between the building and the ground surface around the building. A new point cloud is created by adding the location information corresponding to the interpolated area from the grid data as a point. To minimize the addition of unnecessary points during the interpolation process, the interpolated data to an area outside the initial point cloud area was not processed. The RGB brightness value applied to the interpolated point cloud was processed by setting the image with the closest pixel distance to the shooting center among the stereo images used for matching. It was confirmed that the shielded area generated after generating the point cloud of the target area was effectively processed through the proposed technique.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1479-1488
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• 2022

Accidents in which oil spills occur intermittently in the ocean due to ship collisions and sinkings. In order to prepare prompt countermeasures when such an accident occurs, it is necessary to accurately identify the current status of spilled oil. To this end, the Coast Guard patrols the target area with a fixed-wing airplane or helicopter and checks it with the naked eye or video, but it was difficult to determine the area contaminated by the spilled oil and its exact location on the map. Accordingly, this study develops a technology for direct ortho-rectification by automatically geo-referencing aerial images collected by the Coast Guard without individual ground reference points to identify the current status of spilled oil. First, meta information required for georeferencing is extracted from a visualized screen of sensor information such as video by optical character recognition (OCR). Based on the extracted information, the external orientation parameters of the image are determined. Images are individually orthorectified using the determined the external orientation parameters. The accuracy of individual orthoimages generated through this method was evaluated to be about tens of meters up to 100 m. The accuracy level was reasonably acceptable considering the inherent errors of the position and attitude sensors, the inaccuracies in the internal orientation parameters such as camera focal length, without using no ground control points. It is judged to be an appropriate level for identifying the current status of spilled oil contaminated areas in the sea. In the future, if real-time transmission of images captured during flight becomes possible, individual orthoimages can be generated in real time through the proposed individual orthorectification technology. Based on this, it can be effectively used to quickly identify the current status of spilled oil contamination and establish countermeasures.

• Analytical Science and Technology
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• v.34 no.6
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• pp.251-258
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• 2021

Knives are most frequently used as weapons in violent crimes. Criminals leave behind knife sheaths made of paper and tape at crime scenes. It is difficult to develop fingerprints using tape attached to a porous surface, resulting in the need to explore effective techniques for identifying fingerprints as well as the distribution of fingerprints on each surface, when evidence such as paper knife sheaths are found. In this study, 50 knife sheaths were prepared. The cyanoacrylate fuming (CA fuming) method was applied to develop fingerprints on the non-adhesive side of the tape, and a dual-purpose 1,2-indanedione/Zn (1,2-IND/Zn) reagent was used to separate tape from paper while simultaneously developing fingerprints on the paper. The fingerprints on the adhesive side of the tape were developed using Wet Powder Black^®. Using the R statistical analysis program (The R Foundation for Statistical Computing), we used a heat map to indicate the location of fingerprints developed from each surface. More fingerprints were detected at the ends than in the center of the adhesive side of the tape, and although the non-adhesive sides of tape and paper did not present clear distribution patterns, many fingerprints were developed that had sufficient clarity for personal identification. The results of this study may be applicable for processing evidence when paper sheaths are found at crime scenes.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.1-11
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• 2023

The comprehensive natural disaster reduction plan, the highest plan in the disaster prevention field, was implemented by local governments. second plan is currently being formulated. In order to minimize human and property damage, structural and non-structural measures for each of the nine disaster types are established and implemented for 10 years. Structural measures are based on engineering and quantitative analysis, and the criteria for setting reduction measures are clear. Non-structural measures, however, currently lack the set criteria. the basic disaster and safety management law included the safety vulnerable class in 2018. Currently, the safety vulnerable class of the detailed establishment criteria of the comprehensive natural disaster reduction plan is being established, including children, the elderly, and the disabled. However, due to the lack of data securing and database construction by local governments, it is difficult to prepare a location map for establishing reduction measures for the safety vulnerable. Therefore, in this study, OPEN API data of the safety vulnerable class were collected and statistical information and GIS of SGIS information services were used. The distribution diagram of the safety vulnerable class in Samcheok, Gangwon-do, which is a sample area, and the distribution diagram of the safety vulnerable class in units of the output area (OA) in Geundeok-myeon were prepared.

• Journal of the Daesoon Academy of Sciences
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• v.26
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• pp.215-253
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• 2016

In 2007, according to the records, I made an on-the-spot survey of the place where Doju Cho Jeongsan and his family might have lived in exile in Manchuria and released a paper in which I decided that the site could be the Shuidongchun (village) of Luotongshanzhen in Liuhexian, Jilin Province. Thereafter, sponsored by the Doju Cho Jeongsan's Memorial Project from 2008 to 2013, many times, I carried out the field investigations and researches on Liuhexian, including the visits of the institutions which have the historical documents, in order to find the data related to the participation of him and his family in the anti-Japanese movement. I was, hence, able to reconfirm that the village had been the place of their exile, based on my collected data and the oral reports which the local historians and ethnic Korean elders had provided. In this study, using the historical documents and maps and the oral materials, I made an attempt to prove the historical truth thoroughly once again. First, the existing sources of Doju Cho and his family's settling in Manchuria from March 1909 to 1917, were carefully analyzed which were described in The Jin-gyeong. In doing so, the misspelling of the names and the spatio-temporal errors of the people's activities were corrected. Next, I researched on another town, Shuitungou of Liuhexian in Fengtian Province (in West Gando of Manchuria), which it is known that Doju and his family stayed in, and the Laogushan (mountain), which it is believed that Doju cultivated himself in. Finally, through the attempt, I reached the conclusion that Doju and his family had settled at Shuidongchun (once called Shuidonggou or Shuitongchun) of Luotongshanzhen (once called Datonggou) in Liuhexian, Jilin Province. In the Liuhexian-related documents and maps published in the eras of Republican China and Manchuria, the place name called Shuitungou was not found. However, I discovered a map in the era of Republican China on which Shuitongchun was recorded as Shuidonggou. In addition, considering the administration system of Republic China, tun(屯) and gou(溝) could not be used together in the place names. Accordingly, Shuitungou was more likely misspelled as Korean people in those days mispronounced Shuidonggou. Furthermore, people in China has habitually called the Dagushan(大孤山), located in the north of Gushanzizhen of Liuhexian, as the Laogushan(老孤山). This means that the Korean people who lived in the area then perhaps recorded the mountain as the Nogosan(老姑山), the mountain of the old goddess, according to Korean enunciation, because they had the custom of worshipping the mountain goddess. I tried my best to find the historical documents regarding Doju and his family's anti-Japanese activities to prove the location of exile in which they settled in northeastern China (Manchuria). However, I was not able to reach the initial goal completely due to the shortage of objective evidences, only to leave tasks to be solved. I hope that this study can give a little help to researchers who are interested in this matter.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.200-208
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• 2022

In this study, an automatic information collection system and geographic information construction algorithm for the transportation disadvantaged using electric mobility aids are implemented using an object recognition model. Recognizes objects that the disabled person encounters while moving, and acquires coordinate information. It provides an improved route selection map compared to the existing geographic information for the disabled. Data collection consists of a total of four layers including the HW layer. It collects image information and location information, transmits them to the server, recognizes, and extracts data necessary for geographic information generation through the process of classification. A driving experiment is conducted in an actual barrier-free zone, and during this process, it is confirmed how efficiently the algorithm for collecting actual data and generating geographic information is generated.The geographic information processing performance was confirmed to be 70.92 EA/s in the first round, 70.69 EA/s in the second round, and 70.98 EA/s in the third round, with an average of 70.86 EA/s in three experiments, and it took about 4 seconds to be reflected in the actual geographic information. From the experimental results, it was confirmed that the walking weak using electric mobility aids can drive safely using new geographic information provided faster than now.