EditorialAdvanced Sensor Technologies in Geospatial Sciencesand Engineering

Hyung-Sup Jung ,1 Lei Zhang ,2 and Sang-Hoon Hong 3

1Department of Geoinformatics, The University of Seoul, Seoul, Republic of Korea2Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong3Department of Geological Sciences, Pusan National University, Busan, Republic of Korea

Correspondence should be addressed to Sang-Hoon Hong; geoshong@pusan.ac.kr

Received 19 August 2019; Accepted 22 August 2019; Published 4 September 2019

Copyright © 2019 Hyung-Sup Jung et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Advanced sensor technologies in geospatial sciences andengineering have become more essential to (1) contributingto geographic mapping and (2) understanding geological,ecological, hydrological, and environmental characteristicsof Earth surfaces. Among 56 papers submitted to this spe-cial issue, 24 original research articles, which address theadvanced sensor technologies in geospatial sciences andengineering, have been published. The published paperscan be summarized as follows.

N.-W. Park and P. C. Kyriakidis (2019) in their paperentitled “A Geostatistical Approach to Spatial QualityAssessment of Coarse Spatial Resolution Remote SensingProducts” showed a geostatistical framework for spatialquality assessment of coarse resolution satellite-based prod-ucts using point-level validation data and geostatisticalsimulation.

F. Hu et al. (2019) in their paper entitled “DynamicLinear Predictive Optimization of Flexible Robot ProfilingMFA Model” proposed a flexible robot profiling Model-Free Adaptive (MFA) model based on adaptive predictivedynamic linear optimization. The authors claimed that theflexible robot profiling MFA model could establish the real-time control of airbag charging and discharging deformationprofiling of the flexible robot.

K.-Y. Oh et al. (2019) in their paper entitled “QualityAssessment of Four DEMs Generated Using In-TrackKOMPSAT-3 Stereo Images” analyzed the quality and char-acteristics of four digital elevation models (DEMs) generated

using in-orbit KOrea Multi-Purpose SATellite-3 (KOMP-SAT-3) stereo images. Their result showed that thesufficient-qualified DEM could be generated from theKOMPSAT-3 stereo images without ground control points(GCPs).

J. Dudak et al. (2019) in their paper entitled “Implemen-tation of Secure Communication via the RF Module for DataAcquisition” described the use of the VirtualWire communi-cation protocol using radio frequency waves with a carrierfrequency of 433MHz to 900MHz. This paper focused onthe design and implementation of a secure one-way commu-nication channel.

B. Hu et al. (2019) in their paper entitled “Time-SeriesDisplacement of Land Subsidence in Fuzhou Downtown,Monitored by SBAS-InSAR Technique” monitored the landsubsidence in the Fuzhou downtown after the metro con-struction program using multitemporal synthetic apertureradar (SAR) interferometry (MT-InSAR). For the study, theyused 24 scenes of X-band TerraSAR-X data from July 2013 toAugust 2015 and 32 scenes of C-band Sentinel-1 data fromJuly 2015 to February 2018. Their results showed that (i)the maximum subsidence rate was about -12mm/yr and (ii)eight subsidence funnels were found during the observedperiod.

J. Han et al. (2019) in their paper entitled “PreliminaryResults of the Development of a Single-Frequency GNSSRTK-Based Autonomous Driving System for a SpeedSprayer” developed an autonomous driving system using a

HindawiJournal of SensorsVolume 2019, Article ID 3095901, 3 pageshttps://doi.org/10.1155/2019/3095901

single-frequency GNSS RTK for commercialization of anautonomous driving speed sprayer. The field test of thedeveloped system achieved accuracy of about 0.01m.

Y. Xie et al. (2019) in their paper entitled “A NovelConvolutional Neural Network Architecture for SARTarget Recognition” proposed a novel convolutional neuralnetwork (CNN) architecture suitable for the SAR datathrough improving the Inception-ResNet architecture fromthe optical images. Their result showed that the proposedarchitecture achieved (i) higher than 99% accuracy forthe classification of 10-class targets and (ii) higher than96% accuracy for the classification of eight variants ofthe chosen target.

H. Wu et al. (2019) in their paper entitled “ConcreteSpalling Detection for Metro Tunnel from Point Cloud Basedon Roughness Descriptor” proposed a novel method that canautomatically detect the concrete spalling damage on thetunnel surface from 3D point clouds obtained by a mobilelaser scanning system. The proposed method has two charac-teristics: (i) automatic concrete spalling detection for tunnelsurfaces and (ii) guidelines for choosing optimal scanningparameters.

K.-H. Kim et al. (2019) in their paper entitled “Investiga-tion of Coastal Environment Change Using Wave Measure-ment Sensors and Geographical Laser Scanner” analyzedthe cause and mechanism of damage inflicted on coasts byartificial structures through reviewing beach erosions thathave occurred on beaches on the east coast of Korea afterthe large-scale construction of artificial structures.

S. Zhang et al. (2019) in their paper entitled “UAV-BasedGigapixel Panoramic Image Acquisition Planning with RayCasting-Based Overlap Constraints” proposed a gigapixelpanorama acquisition planning method for multirotors. Itis based on a ray casting image overlap calculation method.

D. Qiu et al. (2019) in their paper entitled “A Deforma-tion Prediction Approach for Supertall Building UsingSensor Monitoring System” applied the integration of thelevenberg-marquardt (LM) and the conditional deep beliefnetwork (CDBN) model to the deformation prediction ofthe CITIC tower, the tallest building in Beijing. They verifiedthe prediction accuracy and stability of the proposed modelthrough model comparison experiments and predictiveanalysis.

D.-C. Lee et al. (2019) in their paper entitled “Determina-tion of Building Model Key Points Using MultidirectionalShaded Relief Images Generated from Airborne LiDARData” implemented the curvature scale space (CSS) algo-rithm to determine model key points (MKPs) of the buildingswith various roof types.

S.-Y. Park et al. (2019) in their paper entitled “Segmenta-tion of LiDAR Data Using Multilevel Cube Code” introduceda shape descriptor method for segmenting LiDAR pointclouds using a “multilevel cube code” that is an extension ofthe 2D chain code to 3D space. The cube operator segmentspoint clouds into roof surface patches, including superstruc-tures, removes unnecessary objects, detects the boundaries ofbuildings, and determines model key points for buildingmodeling. The proposed method was validated by using bothreal and simulated LiDAR data.

J. Shin et al. (2019) in their paper entitled “Characteristicsof Laser Backscattering Intensity to Detect Frozen and WetSurfaces on Roads” investigated the signal characteristics oflaser backscattering intensity in order to detect frozen andwet surfaces on roads.

T.-S. Bae and C.-K. Hong (2019) in their paper entitled“Analysis of Parameter Correlations of the ECOM SolarRadiation Pressure Model for GPS Orbit” analyzed the globalpositioning system (GPS) orbit model in terms of the corre-lation between solar radiation pressure (SRP) parametersand the residuals of orbit estimation.

Y. Han et al. (2019) in their paper entitled “AutomatedCoregistration of Multisensor Orthophotos Generated fromUnmanned Aerial Vehicle Platforms” proposed an auto-mated coregistration approach for orthophotos generatedfrom very-high-resolution (VHR) images acquired frommultisensors mounted on unmanned aerial vehicle (UAV)platforms.

X. Zhu et al. (2019) in their paper entitled “TopologicalCharacteristics and Vulnerability Analysis of Rural TrafficNetwork” built a proper rural rating system in China,including National Road, Provincial Road, County Road,Rural Road, and Special Road. The authors modeled threestatic networks of No-power Traffic Network Model(NTNM), Distance Weight Traffic Network Model(DWTNM), and Road Level Weight Traffic Network Model(RLWTNM) to explore the vulnerability of the rural trafficnetwork (RTN) in Zhangwu in China.

B. Hu et al. (2019) in their paper entitled “Monitoring theThaw Slump-Derived Thermokarst in the Qinghai-TibetPlateau Using Satellite SAR Interferometry” investigatedand validated the distributions and active level of the thaw-induced slope failures with field observations, quantifyingspatial-temporal variability in surface deformation at thawslumps from 2007 to 2010. The authors utilized time seriesSAR interferometry (InSAR) application to map and studythaw slumps in the Qinghai-Tibet plateau (QTP), which havenot been well investigated in previous studies.

J. Wang et al. (2019) in their paper entitled “Demonstra-tion of Time-Series InSAR Processing in Beijing Using aSmall Stack of Gaofen-3 Differential Interferograms” investi-gated the surface deformation of the metropolitan area inBeijing from March 2017 to January 2018 using fiveGaofen-3 (GF-3) SAR. The study evaluates the interferomet-ric capability of GF-3 SAR data.

H.-U. Kim and T.-S. Bae (2019) in their paper entitled“Deep Learning-Based GNSS Network-Based Real-TimeKinematic Improvement for Autonomous Ground VehicleNavigation” developed a model to estimate the position ofthe vehicle without redesigning the analytical model of eachindividual sensor with constraints. The authors applied thedeep learning technique to predict the position of the vehiclebased on multisensor data including GNSS. The field experi-ment was carried out using the mobile mapping system(MMS).

M. E. H. Chowdhury et al. (2019) in their paper entitled“A Low-Cost Closed-Loop Solar Tracking System Based onthe Sun Position Algorithm” developed a simple, cost-effective algorithm-based reliable two-axis tracking system

2 Journal of Sensors

for real-time solar position measurement on an 8-bit micro-controller platform.

B. Hu et al. (2019) in their paper entitled “UsingMultisensor SAR Datasets to Monitor Land Subsidence inLos Angeles from 2003 to 2017” mapped the temporal andspatial variations of land subsidence in Los Angeles. Theauthors used an improved small baseline subset (SBAS) tech-nique and multisensor SAR datasets to analyze the causes ofdeformations from October 2003 to October 2017.

J. Liu et al. (2019) in their paper entitled “Neural Networkwith Confidence Kernel for Robust Vibration Frequency Pre-diction” proposed a novel noncontact machine learning-based system to predict directly vibration frequency withhigh accuracy and good reliability by using image sequencesacquired from a single camera.

Q. Zhang et al. (2018) in their paper entitled “PrecipitableWater Vapor Retrieval and Analysis by Multiple DataSources: Ground-Based GNSS, Radio Occultation, Radio-sonde, Microwave Satellite, and NWP Reanalysis Data”presented a case of retrieved precipitable water vapor(PWV) using microwave satellite observation.

Conflicts of Interest

The Guest Editors declare that there is no conflict of interestregarding the publication of this article.

Acknowledgments

We would like to thank all the authors for their valuablecontributions in this special issue as well as all the reviewerswho are all experts on the theme. And also, we would liketo thank all members of the editorial board for approving thisspecial issue. This work was supported by the NationalResearch Foundation of Korea (NRF) grant funded by theKorea government (MSIT) (No. 2019R1F1A1059570), andit was also supported by Korea Agency for InfrastructureTechnology Advancement (KAIA) grant funded by theKorea Government (MOLIT) (No. 19TLRP-C145770-02,Development of Drone-Based Technology for EfficientMaintenance of Cutting Slope and Pavement).

Hyung-Sup JungLei Zhang

Sang-Hoon Hong

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