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High-resolution geoid modeling using least squares modification of Stokes and Hotine formulas in Colorado / Mustafa Serkan Işık in Journal of geodesy, vol 95 n° 5 (May 2021)
[article]
Titre : High-resolution geoid modeling using least squares modification of Stokes and Hotine formulas in Colorado Type de document : Article/Communication Auteurs : Mustafa Serkan Işık, Auteur ; Bihter Erol, Auteur ; Serdar Erol, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] Colorado (Etats-Unis)
[Termes IGN] correction
[Termes IGN] géoïde local
[Termes IGN] intégrale de Stokes
[Termes IGN] levé gravimétrique
[Termes IGN] matrice de covariance
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle de géopotentiel
[Termes IGN] modèle mathématique
[Termes IGN] montagne
[Termes IGN] nivellement
[Termes IGN] système de référence altimétriqueRésumé : (auteur) The Colorado geoid experiment was initiated and organized as a joint study by the Joint Working Group (JWG) 2.2.2 (1-cm geoid experiment) of the International Association of Geodesy (IAG) in 2017, and different institutions and research groups contributed to this study. The aim of this experiment was to clarify the repeatability of gravity potential values as International Height Reference System (IHRS) coordinates from different geoid determination approaches carried out with the same input dataset. The dataset included the terrestrial and airborne gravity observations, a digital terrain model, the XGM2016 global geopotential model and GPS/leveling data for model validations belonging to a mountainous area of approximately 550 km × 730 km in Colorado, US. The dataset was provided by National Geodetic Survey (NGS) department. In this frame, this article aims providing a discussion on Colorado geoid modeling through individual experimental results obtained by Istanbul Technical University-Gravity Research Group (ITU-GRG). This contribution mainly focused on modeling the Colorado geoid using the least squares modifications of Stokes and Hotine integral formulas with additive corrections. The computations using each formula were carried out using ITU-GRG software, including the solution variants based on terrestrial-only, airborne-only and combined gravity datasets. Then, the calculated experimental geoid models were validated using historical and recently measured profile-based GPS/leveling datasets, and they were also compared with the official solutions submitted by different institutions for the “1-cm geoid experiment” of IAG JWG 2.2.2. For all validation results, the Hotine and Stokes integral formulas yielded similar performances in terms of geoid accuracy; however, the models computed using the combined data had better accuracy than those using the terrestrial-only and airborne-only solutions. The geoid model solutions using the combined data had an accuracy of 2.69 cm for the Hotine method and 2.87 cm for the Stokes method in the test results using GPS/leveling data of the GSVS17 (Geoid Slope Validation Survey 2017) profile. Airborne data from the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project contributed significantly towards improving the geoid model, especially in the mountainous parts of the area. Numéro de notice : A2021-311 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01501-z Date de publication en ligne : 07/04/2021 En ligne : https://doi.org/10.1007/s00190-021-01501-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97503
in Journal of geodesy > vol 95 n° 5 (May 2021) . - n° 49[article]Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)
[article]
Titre : Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Masoud Minaei, Auteur ; Robert Gilmore Pontius Jr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance urbaine
[Termes IGN] extrapolation
[Termes IGN] image Landsat
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] Téhéran
[Termes IGN] utilisation du solRésumé : (auteur) This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. Numéro de notice : A2021-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101595 Date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101595 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97357
in Computers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101595[article]Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)
[article]
Titre : Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future Type de document : Article/Communication Auteurs : Tamás Gál, Auteur ; Sándor István Mahó, Auteur ; Norà Skarbit, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101600 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre urbain
[Termes IGN] changement climatique
[Termes IGN] climat urbain
[Termes IGN] espace vert
[Termes IGN] flore urbaine
[Termes IGN] forêt périurbaine
[Termes IGN] forêt urbaine
[Termes IGN] Hongrie
[Termes IGN] ilot thermique urbain
[Termes IGN] image à haute résolution
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] parc urbain
[Termes IGN] planification urbaine
[Termes IGN] série temporelle
[Termes IGN] utilisation du solRésumé : (auteur) This paper focuses on urban green spaces in terms of climate and human thermal comfort containing their effect on heat load mitigation. It incorporates a modelling study in which the role of green spaces was investigated in terms of heat stress modification by applying MUKLIMO_3 model. During the experiment, the thermal effects of dense trees, scattered trees, grasslands and mixed green infrastructure has been investigated in the case of Szeged (Hungary) and assessed using different climate indices. The investigations encompassed 3 climatological time periods (1981–2010, 2021–2050 and 2071–2100) and two emission scenarios for future climate (RCP4.5 and RCP8.5). It was found that urban green spaces (e.g. parks) generally cool the environment, although, the cooling potential of the different green types differs. The highest reduction of heat load was found in the case of large urban parks comprising of dense trees near the downtown. The spatial extension of detected cooling was found small. However, it would increase during the future, especially in the case of grasslands. For urban planners, it is highly recommended to introduce new green sites within a city and to increase the spatial extension of the existing ones to mitigate and adapt to the impacts of climate change in the urban environment. Numéro de notice : A2021-276 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101600 Date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101600 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97362
in Computers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101600[article]A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
[article]
Titre : A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media Type de document : Article/Communication Auteurs : Yi Bao, Auteur ; Zhou Huang, Auteur ; Linna Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données spatiotemporelles
[Termes IGN] géopositionnement
[Termes IGN] graphe
[Termes IGN] modèle de simulation
[Termes IGN] point d'intérêt
[Termes IGN] réseau social
[Termes IGN] service fondé sur la position
[Termes IGN] utilisateur
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit. Numéro de notice : A2021-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808896 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97324
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 639 - 660[article]A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)
[article]
Titre : A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection Type de document : Article/Communication Auteurs : Xi Wu, Auteur ; Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2021 Article en page(s) : pp 87 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] altitude
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection des nuages
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] image Gaofen
[Termes IGN] information géographique
[Termes IGN] latitude
[Termes IGN] longitude
[Termes IGN] modèle statistique
[Termes IGN] neige
[Termes IGN] Normalized Difference Snow IndexRésumé : (auteur) Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet. Numéro de notice : A2021-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.023 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97187
in ISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 87 - 104[article]Réservation
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