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Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea / Yang Xu in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 92 (March 2022) Titre : Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea Type de document : Article/Communication Auteurs : Yang Xu, Auteur ; Dan Zou, Auteur ; Sangwon Park, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] chaîne de Markov [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] Corée du sud [Termes IGN] durée de trajet [Termes IGN] mobilité [Termes IGN] modèle de simulation [Termes IGN] prévision à court terme [Termes IGN] téléphone intelligent [Termes IGN] téléphonie mobile [Termes IGN] tourisme [Termes IGN] voyage Résumé : (auteur) The abilities to predict tourist movements are critical to many urban applications, such as travel recommendations, targeted advertising, and infrastructure planning. Despite its importance, our understanding on the movement predictability of urban tourists and visitors is still limited, partially due to difficulties in accessing large scale mobility observations. In this study, we aim to bridge this gap by analyzing a nationwide mobile phone dataset. The dataset captures movement traces of a large number of international travelers who visited South Korea in 2018. By introducing two prediction models, one being Markov chain and the other with a recurrent neural network architecture, we assess how well travelers’ movements can be predicted under different model settings, and examine how predictability relates to travelers’ length of stay and activeness in travel patterns. Since travelers’ destination choices are quite diverse in South Korea, this enables us to further investigate the geographic variation of the models’ performance. Results show that the Markov chain model achieves an overall accuracy between 33.4% (@Acc1 metric) and 64.2% (@Acc5 metric), compared to 41.9% (@Acc1) and 67.7% (@Acc5) for the recurrent neural network model. The prediction capabilities of both models are largely unequal across individuals, with active travelers being more predictable in general. There is a notable geographic variation in the models’ performance, meaning that travelers’ movements are more predictable in some cities, but less in others. We believe this study represents a new effort in portraying the movement predictability of urban tourists and visitors. The analytical framework can be applied to assist tourism planning and service deployment in cities. Numéro de notice : A2022-085 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101753 Date de publication en ligne : 06/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101753 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99490 [article]

Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea / Yong Piao in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 13 n° 1 (2022) . - pp 432 - 450 Titre : Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea Type de document : Article/Communication Auteurs : Yong Piao, Auteur ; Dongkun Lee, Auteur ; Sangjin Park, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 432 - 450 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aléa [Termes IGN] cartographie des risques [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Corée du sud [Termes IGN] Google Earth Engine [Termes IGN] incendie de forêt [Termes IGN] pente [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (auteur) Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17° (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do. Numéro de notice : A2022-140 Affiliation des auteurs : non IGN Nature : Article DOI : 10.1080/19475705.2022.2030808 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1080/19475705.2022.2030808 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99942 [article]

A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2149 - 2167 Titre : A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area Type de document : Article/Communication Auteurs : Myung-Jin Jun, Auteur Année de publication : 2021 Article en page(s) : pp 2149 - 2167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] arbre de décision [Termes IGN] changement d'utilisation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Extreme Gradient Machine [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal artificiel [Termes IGN] Séoul [Termes IGN] zone urbaine Résumé : (auteur) This study compares the performance of gradient boosting decision tree (GBDT), artificial neural networks (ANNs), and random forests (RF) methods in LUC modeling in the Seoul metropolitan area. The results of this study showed that GBDT and RF have higher predictive power than ANN, indicating that tree-based ensemble methods are an effective technique for LUC prediction. Along with the outstanding predictive performance, the DT-based ensemble models provide insights for understanding which factors drive LUCs in complex urban dynamics with the relative importance and nonlinear marginal effects of predictor variables. The GBDT results indicate that distance to the existing residential site has the highest contribution to urban land use conversion (30.4% of the relative importance), while other significant predictor variables were proximity to industrial and public sites (combined 32.3% of relative importance). New residential development is likely to be adjacent to existing residential sites, but nonresidential development occurs at a distance (about 600 m) from such sites. The distance to the central business district (CBD) had increasing marginal effects on residential land use conversion, while no significant pattern was found for nonresidential land use conversion, indicating that Seoul has experienced more population suburbanization than employment decentralization. Numéro de notice : A2021-756 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887490 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887490 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98771 [article]

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Time-series analysis of geodetic reference frame aligned to International Terrestrial Reference Frame / Tae-Suk Bae in Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, vol 39 n° 5 ([01/11/2021])  

Public [article]  inJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography > vol 39 n° 5 [01/11/2021] . - pp 313 - 319 Titre : Time-series analysis of geodetic reference frame aligned to International Terrestrial Reference Frame Type de document : Article/Communication Auteurs : Tae-Suk Bae, Auteur ; Chang-Ki Hong, Auteur ; Jisun Lee, Auteur ; Zuheir Altamimi , Auteur ; Patrick Sillard , Auteur ; Claude Boucher , Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : pp 313 - 319 Note générale : bibliographie This research was supported by a grant from National R&D Project of “Development of Ground-based Centimeter-level Maritime Precise PNT Technologies” funded by Ministry of Oceans and Fisheries, Korea (20200451). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] Corée du sud [Termes IGN] International Terrestrial Reference Frame [Termes IGN] rattachement [Termes IGN] réseau géodésique permanent [Termes IGN] série temporelle Résumé : (auteur) The national geodetic reference frame of Korea was adopted in 2003, which is referenced to ITRF (International Terrestrial Reference Frame) 2000 at the epoch of January 1, 2002. For precise positioning based on the satellites, it should be thoroughly maintained to the newest global reference frame. Other than plate tectonic motion, there are significant events or changes such as earthquakes, antenna replacement, PSD (Post-Seismic Deformation), seasonal variation etc. We processed three years of GNSS (Global Navigation Satellite System) data (60 NGII CORS stations, 51 IGS core stations) to produce daily solutions minimally constrained to ITRF. From the time series of daily solutions, the sites with unexpected discontinuity were identified to set up an event (mostly antenna replacement). The combined solution with minimum constraints was estimated along with the velocity, the offsets, and the periodic signals. The residuals show that the surrounding environment also affects the time series to a certain degree, thus it should be improved eventually. The transformation parameters to ITRF2014 were calculated with stability and consistency, which means the national geodetic reference frame is properly aligned to the global reference frame. Numéro de notice : A2021-943 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.7848/ksgpc.2021.39.5.313 En ligne : https://doi.org/10.7848/ksgpc.2021.39.5.313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99483 [article]

A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)  

Public [article]  inRemote sensing > vol 13 n°10 (May-2 2021) . - n° 2003 Titre : A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration Type de document : Article/Communication Auteurs : Daeyong Jin, Auteur ; Eojin Lee, Auteur ; Kyonghwan Kwon, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2003 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] chlorophylle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Corée du sud [Termes IGN] distribution spatiale [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] hydrodynamique [Termes IGN] image COMS-GOCI Résumé : (auteur) In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Numéro de notice : A2021-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102003 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97759 [article]

Artificial intelligence methods applied to urban remote sensing and GIS / Chang-Wook Lee (2021)  

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A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)  

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Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])  

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Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model / Minkyu Kim in Remote sensing, vol 12 n° 21 (November 2020)  

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Detecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Error-regulated multi-pass DInSAR analysis for landslide risk assessment / Jung Rack Kim in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)  

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Development of a 3D underground cadastral system with indoor mapping for as-built BIM: the case study of Gangnam subway station in Korea / Kim Sangmin in Sensors, vol 15 n° 12 (December 2015)

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Application of fuzzy combination operators to flood vulnerability assessments in Seoul, Korea / Moung-Jin Lee in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)  

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GBAS ionospheric threat model assessment for category I operation in the Korean region / Minchan Kim in GPS solutions, vol 19 n° 3 (July 2015)  

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Spatial and temporal change in landslide hazard by future climate change scenarios using probabilistic-based frequency ratio model / Moung-Jin Lee in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

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