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RTK-quality positioning with global precise point positioning corrections / Nacer Naciri in Navigation : journal of the Institute of navigation, vol 70 n° 3 (Fall 2023)  

Public [article]  inNavigation : journal of the Institute of navigation > vol 70 n° 3 (Fall 2023) . - n° 575 Titre : RTK-quality positioning with global precise point positioning corrections Type de document : Article/Communication Auteurs : Nacer Naciri, Auteur ; Sunil Bisnath, Auteur Année de publication : 2023 Article en page(s) : n° 575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] fréquence multiple [Termes IGN] modèle mathématique [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement ponctuel précis [Termes IGN] signal GNSS Résumé : (auteur) Global navigation satellite system (GNSS) precise point positioning (PPP) has potential as an alternative or replacement for real-time kinematic (RTK) processing. In this work, we reached for RTK levels of performance without the need for local information through PPP (i.e., centimeter-level positioning that was reached near-instantaneously). This work makes use of information currently available from processing signals from global positioning system (GPS), Galileo, BeiDou-2/3, and GLONASS by fixing ambiguities for the first three constellations on all available frequencies. This processing was done using a four-frequency, four-constellation uncombined decoupled clock model (DCM) that has been expanded as part of this work. The results were tested on 1448 global datasets and showed that instantaneous convergence on average to 2.5 cm error can be achieved for 81% of the stations. These findings were reinforced by the results of epoch-by-epoch processing, as an average of 80% of all single epochs converged below 2.5 cm error at 1σ, as opposed to less than the 0.5% typically observed for classic PPP. Numéro de notice : A2023-205 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.33012/navi.575 Date de publication en ligne : 29/01/2023 En ligne : https://doi.org/10.33012/navi.575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103107 [article]

Performance analysis of cross-frequency Doppler-assisted carrier phase tracking / Dun Wang in GPS solutions, vol 27 n° 3 (July 2023)  

Public [article]  inGPS solutions > vol 27 n° 3 (July 2023) . - n° 105 Titre : Performance analysis of cross-frequency Doppler-assisted carrier phase tracking Type de document : Article/Communication Auteurs : Dun Wang, Auteur ; Shuangna Zhang, Auteur ; Tong Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] bruit thermique [Termes IGN] erreur systématique interfréquence d'horloge [Termes IGN] fréquence [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle mathématique [Termes IGN] phase [Termes IGN] signal GNSS Résumé : (auteur) Using the Doppler frequency obtained from tracking a GNSS pilot signal to aid in tracking another signal modulated with higher rate navigation messages in a different frequency band can improve tacking robustness and lower the message demodulation threshold. Based on an analysis of received signal frequency coherence, a linearized mathematical model of the cross-frequency Doppler-assisted carrier phase tracking loop is built, a thermal noise jitter calculation equation for the assisted tracking loop is derived, and its dynamic stress response characteristics are examined. The loop design requirements for eliminating the influence of inter-frequency frequency bias are clarified, as are the cross-frequency assist signal selection criteria. Monte Carlo simulations and preliminary field tests validate the theoretical results using the B1C pilot signal-aided tracking B2b signal of the MEO satellite in the BeiDou satellite navigation system (BDS). Experimental results show that the carrier phase tracking threshold of the B2b signal can be reduced by about 4 dB. Numéro de notice : A2023-214 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-023-01434-4 En ligne : https://doi.org/10.1007/s10291-023-01434-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103143 [article]

Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs / Alvin Christopher G. Varquez in Sustainable Cities and Society, vol 91 (April 2023)  

Public [article]  inSustainable Cities and Society > vol 91 (April 2023) . - n° 104442 Titre : Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs Type de document : Article/Communication Auteurs : Alvin Christopher G. Varquez, Auteur ; Sifan Dong, Auteur ; Shinya Hanaoka, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] gare [Termes IGN] modèle de simulation [Termes IGN] modélisation spatiale [Termes IGN] réseau ferroviaire [Termes IGN] système d'information géographique [Termes IGN] urbanisation Résumé : (auteur) Plausible urban growth projections aid in the understanding and treatment of multidisciplinary issues faced in society. In this work, we investigated the possible effects of train stations on urban growth by comparing urban projections from a cellular-automata-based land use change model, named SLEUTH, with versions (i.e. SLEUTsH and SLEUTsHGA introduced in this study) that can consider railway-induced urban growth and those (i.e. SLEUTH and SLEUTHGA) that do not. It was found that the influence of the railway stations on urban growth varied with time and according to each city. In general, railway stations induced urbanization in their immediate surroundings. However, edge growth, which is growth at the urban boundaries was slow in the first five years of the future prediction. As demonstrated by the higher urban growth rates simulated for the first few years in the SLEUTsH cases than the SLEUTH cases, the presence of railway stations will lead to more rapid urbanization in the 2040s. Mainly relying on publicly available GIS datasets, this work demonstrates the potential for modeling railway-induced urban growth on a global scale. The findings can be further confirmed with other cellular-automata models using a similar methodology. Numéro de notice : A2023-151 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.scs.2023.104442 Date de publication en ligne : 08/02/2023 En ligne : https://doi.org/10.1016/j.scs.2023.104442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102824 [article]

Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal / Cristina Alegria in Forests, vol 14 n° 3 (March 2023)  

Public [article]  inForests > vol 14 n° 3 (March 2023) . - n° 591 Titre : Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal Type de document : Article/Communication Auteurs : Cristina Alegria, Auteur ; Alice M. Almeida, Auteur ; Natalia Roque, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 591 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] distribution spatiale [Termes IGN] entropie maximale [Termes IGN] gestion forestière [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] Pinus pinaster [Termes IGN] Portugal [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence–absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity. Numéro de notice : A2023-167 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f14030591 Date de publication en ligne : 16/03/2023 En ligne : https://doi.org/10.3390/f14030591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102904 [article]

Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)  

Public [article]  inSustainable Cities and Society > vol 89 (February 2023) . - n° 104337 Titre : Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities Type de document : Article/Communication Auteurs : Pavlos Tsagkis, Auteur ; Efthimios Bakogiannis, Auteur ; Alexandros Nikitas, Auteur Année de publication : 2023 Article en page(s) : n° 104337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] Corine (base de données) [Termes IGN] croissance urbaine [Termes IGN] données localisées libres [Termes IGN] étalement urbain [Termes IGN] Grèce [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle orienté agent [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward. Numéro de notice : A2023-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104337 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102486 [article]

Evaluation of growth models for mixed forests used in Swedish and Finnish decision support systems / Jorge Aldea in Forest ecology and management, vol 529 (February-1 2023)  

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Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model / Zensheng Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

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Nonparametric upscaling of bark beetle infestations and management from plot to landscape level by combining individual-based with Markov chain models / Bruno Walter Pietzsch in European Journal of Forest Research, vol 142 n° 1 (February 2023)  

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Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland / Man Hu in Forest ecology and management, vol 529 (February-1 2023)  

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An extended inter-system biases model for multi-GNSS precise point positioning / Xuexi Liu in Measurement, vol 206 (January 2023)  

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Analysis of cycling network evolution in OpenStreetMap through a data quality prism / Raphaël Bres (2023)  

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HGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation / Xuefeng Guan in Computers, Environment and Urban Systems, vol 99 (January 2023)  

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Simplified automatic prediction of the level of damage to similar buildings affected by river flood in a specific area / David Marín-García in Sustainable Cities and Society, vol 88 (January 2023)  

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Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])  

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Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

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