• Journal of Korean Society for Geospatial Information Science
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• v.17 no.3
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• pp.109-114
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• 2009

Although island is great worth in ecology, environmental conservation and important territory as a national land for developing the marine, the bottom of the sea and also ecological studying field for the ages to come, it has not been managed because of lack of a scientific surveying. In the case of a cadastral record, inaccessible islands have not been registered in current cadastral record because of the limit of surveying technology in 1910. Therefore, a scientific investigation and systematic management about unregistered islands are necessary. But, a airborne laser scanning system is possible to acquire an accurate positions with digital images about inaccessible islands. Therefore, scientific detection of unregistered islands became possible. This paper presented the results of the shoreline extraction around the Heuksan island using Lidar data and the detection of unregistered islands comparing the cadastral map to the ortho-image. Also, we presented the extraction technique of unregistered islands by calculating their positions and areas. As a result, we extracted effectively 16 unregistered islands around the Heuksan island.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

• Journal of Aerospace System Engineering
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• v.17 no.6
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• pp.16-26
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• 2023

With the growing demand for unmanned aerial vehicles (UAVs), various collision avoidance methods have been proposed, mainly using LiDAR and stereo cameras. However, it is difficult to apply these sensors to small UAVs due to heavy weight or lack of space. The recently proposed methods use a combination of object recognition models and distance sensors, but they lack information on the obstacle size. This disadvantage makes distance determination and obstacle coordination complicated in an early-stage collision avoidance. We propose a method for estimating obstacle sizes using a monocular camera-YOLO and infrared sensor. Our experimental results confirmed that the accuracy was 86.39% within the distance of 40 cm. In addition, the proposed method was applied to a small UAV to confirm whether it was possible to avoid obstacle collisions.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.50-61
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• 2010

This study was carried out to find out the way to build a comprehensive wetland ecosystem database using the technique of remote sensing and Geographic Information System. A Landsat TM image (taken in Oct. 30, 2002), Kompsat-2 images (Jan. 17, 2008 & Nov. 20, 2008), LiDAR(Mar. 1, 2009) were used for the primary source for the image analysis. Field surveys were conducted March to August of 2009 to help image analysis and examine the results. An actual wetland vegetation map was created based on the field survey. Satellite images were analyzed by unsupervised and supervised classification methods and finally categorized into such classes as Phragmites australis community, mixed community, sand beach, Scirpus planiculmis community and non-vegetation intertidal area. The map of wetland productivity was developed based on the productivity of Phragmites australis and the relationship to the proximity of adjacent water bodies. The developed 3 dimensional wetland map showed such several potential applications as flood inundation, birds flyway viewsheds and benthos distribution. Considering these results, we concluded that it is possible to use the remote sensing and GIS techniques for producing wetland ecosystem spatial database and these techniques are very effective for the development of the national wetland inventory in Korea.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.435-449
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• 2022

Forest canopy height is an indispensable vertical structure parameter that can be used for understanding forest biomass and carbon storage as well as for managing a sustainable forest ecosystem. Plot-based field surveys, such as the national forest inventory, have been conducted to provide estimates of the forest canopy height. However, the comprehensive nationwide field monitoring of forest canopy height has been limited by its cost, lack of spatial coverage, and the inaccessibility of some forested areas. These issues can be addressed by remote sensing technology, which has gained popularity as a means to obtain detailed 2- and 3-dimensional measurements of the structure of the canopy at multiple scales. Here, we reviewed both international and domestic studies that have used remote sensing technology approaches to estimate the forest canopy height. We categorized and examined previous approaches as: 1) LiDAR approach, 2) Stereo or SAR image-based point clouds approach, and 3) combination approach of remote sensing data. We also reviewed upscaling approaches of utilizing remote sensing data to generate a continuous map of canopy height across large areas. Finally, we provided suggestions for further advancement of the Korean forest canopy height estimation system through the use of various remote sensing technologies.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.21-30
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• 2024

In this paper, we propose a method to build a sample dataset of the features of eight sensor-only facilities built as infrastructure for autonomous cooperative driving. The feature extracted from point cloud data acquired by LiDAR and build them into the sample dataset for recognizing the facilities. In order to build the dataset, eight sensor-only facilities with high-brightness reflector sheets and a sensor acquisition system were developed. To extract the features of facilities located within a certain measurement distance from the acquired point cloud data, a cylindrical projection method was applied to the extracted points after applying DBSCAN method for points and then a modified OTSU method for reflected intensity. Coordinates of 3D points, projected coordinates of 2D, and reflection intensity were set as the features of the facility, and the dataset was built along with labels. In order to check the effectiveness of the facility dataset built based on LiDAR data, a common CNN model was selected and tested after training, showing an accuracy of about 90% or more, confirming the possibility of facility recognition. Through continuous experiments, we will improve the feature extraction algorithm for building the proposed dataset and improve its performance, and develop a dedicated model for recognizing sensor-only facilities for autonomous cooperative driving.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.1-14
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• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.401-410
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• 2013

Due to the increasing need for spatial information, there have been a lot of research related with monitoring and revision of changed regions for the acquisition of the accurate and latest information. In this paper, the optimal monitoring management plan for changed regions with respect to the revision periods was proposed. For this purpose, the representative monitoring methods, which are based on database, professional manpower and crowdsourcing of continuous revision, and aerial imagery, satellite imagery and LiDAR of cyclic revision, were investigated. Then, the properties and application status of monitoring systems in Korea were illustrated according to the methods. Finally, the optimal monitoring management plan for continuous and cyclic revisions was suggested through the comparison of properties and revisionable objects of each method. From the result, it was shown to be appropriate for the optimal monitoring management plan of continuous revision as using Internet-Architectural Information System (e-AIS) database cooperated with professional manpower and crowdsourcing, and cyclic revision as using domestic high-resolution satellite images and LiDAR data processed semi-automatically.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.109-117
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• 2012

Recently, large-scale multi-hazards have been occurred in the various areas of the world. A variety of Earth observation sensors such as satellite EO, aerial and terrestrial LiDAR have been utilized for global natural disaster monitoring. Especially, commercial satellites which observe the Earth regularly and repeatedly, and acquire images with cm-level high spatial resolution enable its applications to extend in the fields of disaster management from advanced disaster monitoring to timely recovery. However, due to existing satellite operation systems with some limitations in almost real-time and wide regional disaster response, close international collaborations between satellite operating organizations like NASA, JAXA, KARI etc. have been required for collecting satellite images in time through a satellite platform with multi-sensors or satellite constellation. For responding domestic natural disaster such as heavy snowfall and extreme rainfall in 2011, this paper proposes a disaster management system for timely decision-making; rapid acquisition of satellite imagery, data processing, GIS analysis, and digital mapping through cooperation with NDMI in Korea and International Charter-Space and Major disasters.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1095-1109
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• 2021

The technology used to recognize the location and surroundings of autonomous vehicles is called SLAM. SLAM standsfor Simultaneously Localization and Mapping and hasrecently been actively utilized in research on autonomous vehicles,starting with robotic research. Expensive GPS, INS, LiDAR, RADAR, and Wheel Odometry allow precise magnetic positioning and mapping in centimeters. However, if it can secure similar accuracy as using cheaper Cameras and GPS data, it will contribute to advancing the era of autonomous driving. In this paper, we present a method for converging monocular camera with RTK-enabled GPS data to perform RMSE 33.7 cm localization and mapping on the urban road.