As human beings spend more time indoors, and with the growing complexity of indoor spaces, more focus is given to indoor spatial applications and services. 3D topological networks are used for various spatial applications that involve navigation indoors such as emergency evacuation, indoor positioning, and visualization. Manually generating indoor network data is impractical and prone to errors, yet current methods in automation need expensive sensors or datasets that are difficult and expensive to obtain and process. In this research, a methodology for semi-automatically generating a 3D indoor topological model based on IndoorGML (Indoor Geographic Markup Language) is proposed. The concept of Shooting Point is defined to accommodate the usage of omnidirectional images in generating IndoorGML data. Omnidirectional images were captured at selected Shooting Points in the building using a fisheye camera lens and rotator and indoor spaces are then identified using image processing implemented in Python. Relative positions of spaces obtained from CAD (Computer-Assisted Drawing) were used to generate 3D node-relation graphs representing adjacency, connectivity, and accessibility in the study area. Subspacing is performed to more accurately depict large indoor spaces and actual pedestrian movement. Since the images provide very realistic visualization, the topological relationships were used to link them to produce an indoor virtual tour.

The KDE (Kernel Density Estimation) technique in GIS (Geographic Information System) has been widely used as a method for determining whether a phenomenon occurring in space forms clusters. Most human-generated events such as traffic accidents and retail stores are distributed according to a road network. Even if events on forward and rear roads have short Euclidean distances, network distances may increase and the correlation between them may be low. Therefore, the NKDE (Network-based KDE) technique has been proposed and applied to the urban space where a road network has been developed. KDE is being studied in the field of business GIS, but there is a limit to the microscopic analysis of economic activity along a road. In this study, the NKDE technique is applied to the analysis of urban phenomena such as the density of shops rather than traffic accidents that occur on roads. The results of the NKDE technique are also compared to pedestrian networks and road centerline networks. The results show that applying NKDE to microscopic trade area analysis can yield relatively accurate results. In addition, it was found that pedestrian network data that can consider the movement of actual pedestrians are necessary for accurate trade area analysis using NKDE.

Ocean tides in Antarctica are not well constrained mostly due to the lack of tidal observations. Especially, tides underneath and around ice shelves are uncertain. InSAR (Interferometric Synthetic Aperture Radar) data has been used to observe ice shelf movements primarily caused by ocean tides. Here, we demonstrate that it is possible to estimate tidal constituents underneath the Sulzberger ice shelf, West Antarctica, solely using ERS-1/2 tandem mission DInSAR (differential InSAR) observations. In addition, the tidal constituents can be estimated in a high-resolution (~200 m) grid which is beyond any tidal model resolution. We assume that InSAR observed ocean tidal heights can be derived after correcting the InSAR data for the effect of atmospheric loading using the inverse barometric effect, solid earth tides, and ocean tide loading. The ERS (European Remote Sensing) tandem orbit configuration of a 1-day separation between SAR data takes diminishes the sensitivity to major tidal constituents including $K_1$ and $S_2$. Here, the dominant tidal constituent $O_1$ is estimated using 8 differential interferograms underneath the Sulzberger ice shelf. The resulting tidal constituent is compared with a contemporary regional tide model (CATS2008a) and a global tide model (TPXO7.1). The InSAR estimated tidal amplitude agrees well with both models with RMS (root-mean-square) differences of < 2.2 cm and the phase estimate corroborating both tide models to within $8^{\circ}$. We conclude that fine spatial scale (~200 m) Antarctic ice shelf ocean tide determination is feasible for dominant constituents using C-band ERS-1/2 tandem mission InSAR.

LiDAR (Light Detection And Ranging) strip adjustment is process to improve geo-referencing of the ALS (Airborne Laser Scanner) strips that leads to seamless LiDAR data. Multiple strips are required to collect data over the large areas, thus the strips are overlapped in order to ensure data continuity. The LSA (LiDAR Strip Adjustment) consists of identifying corresponding features and minimizing discrepancies in the overlapping strips. The corresponding features are utilized as control features to estimate transformation parameters. This paper applied SURF (Speeded Up Robust Feature) to identify corresponding features. To improve determination of the corresponding feature, false matching points were removed by applying three schemes: (1) minimizing distance of the SURF feature vectors, (2) selecting reliable matching feature with high cross-correlation, and (3) reflecting geometric characteristics of the matching pattern. In the strip adjustment procedure, corresponding points having large residuals were removed iteratively that could achieve improvement of accuracy of the LSA eventually. Only a few iterations were required to reach reasonably high accuracy. The experiments with simulated and real data show that the proposed method is practical and effective to airborne LSA. At least 80 % accuracy improvement was achieved in terms of RMSE (Root Mean Square Error) after applying the proposed schemes.

Unlike general VHR (Very-High-Resolution) satellite sensors that are mainly for panchromatic and MS (Multispectral) imaging, Worldview-3 sensor additionally provides eight SWIR (Short Wavelength Infrared) bands in wavelength range from 1198 nm to 2365 nm. This study investigates the effect of informative Worldview-3 SWIR bands for wetland classification performance. Worldview-3 imagery acquired over Sunchon Bay, which is a coastal wetland located in South Korea, is used to implement the classification. Land-cover classes for the scene are determined by referring to national land-cover maps, which are provided by the Ministry of Environment, overlapped with the scene. After that, training data for each determined class are collected. In order to analyze the effect of SWIR bands, classifications with and without SWIR bands are carried out and the results are then compared. In this regard, a SVM (Support Vector Machine) is utilized as their classifier. As a result of the accuracy assessments performed by test data that are independently extracted from training data, it was confirmed that classification performance was improved when the SWIR bands are included as input features for SVM-based classification.

An address is sub-spatial data representing a location, and identifies the place in which a resident's life is based. The Korean address system has changed from an area-based address system to the Road Name Address System in 2014 to solve the problems of the area-based address system and to accept the international address standards. Although the Road Name Address System is continually being modified and implemented it limitations. Therefore, the purpose of this study is to resolve these limitations and to analyze and redefine the Road Name Address System of Korea to implement the street-based address system. To understand the address, we define the components of the address system and analyze the Korean Road Name Address System based on each component to derive the limit. To resolve the limitations of the Road Name Address System and to implement the street-based address system, the Road Name Address System is redefined according to three aspects. Finally, we evaluate the usability of the Road Name Address System proposed in this study through address based geocoding.

Recently, most of mobile devices are equipped with GNSS (Global Navigation Satellite System). When the GNSS signal is available, it is easy to obtain position information. However, GNSS is not suitable solution for indoor localization, since the signals are normally not reachable inside buildings. A wide varieties of technology have been developed as a solution for indoor localization such as Wi-Fi, beacons, and inertial sensor. With the increased sensor combinations in mobile devices, mobile devices also became feasible to provide a solution, which based on PDR (Pedestrian Dead Reckoning) method. In this study, we utilized the combination of three sensors equipped in mobile devices including accelerometer, digital compass, and gyroscope and applied three representative PDR methods. The proposed methods are done in three stages; step detection, step length estimation, and heading determination and the final indoor localization result was evaluated with terrestrial LiDAR (Light Detection And Ranging) data obtained in the same test site. By using terrestrial LiDAR data as reference ground truth for PDR in two differently designed experiments, the inaccuracy of PDR methods that could not be found by existing evaluation method could be revealed. The firstexperiment included extreme direction change and combined with similar pace size. Second experiment included smooth direction change and irregular step length. In using existing evaluation method which only checks traveled distance, The results of two experiments showed the mean percentage error of traveled distance estimation resulted from three different algorithms ranging from 0.028 % to 2.825% in the first experiment and 0.035% to 2.282% in second experiment, which makes it to be seen accurately estimated. However, by using the evaluation method utilizing terrestrial LiDAR data, the performance of PDR methods emerged to be inaccurate. In the firstexperiment, the RMSEs (Root Mean Square Errors) of x direction and y direction were 0.48 m and 0.41 m with combination of the best available algorithm. However, the RMSEs of x direction and y direction were 1.29 m and 3.13 m in the second experiment. The new evaluation result reveals that the PDR methods were not effective enough to find out exact pedestrian position information opposed to the result from existing evaluation method.

Recently, GNSS (Global Navigation Satellite System)-derived orthometric height determination has been studied to improve the time and cost-effectiveness of traditional leveling surveying. However, the accuracy of this new survey method was evaluated when unknown points are located lower than control points. In this study, the accuracy of GNSS-derived orthometric height was examined using TPs (Triangulation Points) to verify the stability of surveying in mountainous areas. The GNSS survey data were obtained from Mungyeong, Unbong/Hadong, Uljin, and Jangseong. Three unknown points were surrounded by more than three UCPs (Unified Control Points) or BMs (Benchmarks) following the guideline for applying GNSS-derived orthometric height determination. A newly developed national geoid model, KNGeoid17 (Korean National Geoid 2017), has been applied for determining the orthometric height. In comparison with the official orthometric heights of the TPs, the heights of the unknown points in Mungyeong and Unbong/Hadong differ by more than 20 cm. On the other hand, TPs in Uljin and Jangseong show 15-16 cm of local bias with respect to the official products. Since the precision of official orthometric heights of TPs is known to be about 10 cm, these errors exceed the limit of the precision. Therefore, the official products should be checked to offer more reliable results to surveyors. As an alternative method of verifying accuracy, three different GNSS post-processing software were applied, and the results from each software were compared. The results showed that the differences in the whole test areas did not exceed 5 cm. Therefore, it was concluded that the precision of the GNSS-derived orthometric height was less than 5 cm, even though the unknown points were higher than the control points.

Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.