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A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery / Seth Goodman in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 674 - 691 Titre : A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery Type de document : Article/Communication Auteurs : Seth Goodman, Auteur ; Ariel BenYishay, Auteur ; Daniel Runfola, Auteur Année de publication : 2021 Article en page(s) : pp 674 - 691 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] conflit [Termes IGN] image Landsat-8 [Termes IGN] implémentation (informatique) [Termes IGN] Nigéria [Termes IGN] prédiction [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Convolutional neural networks (CNNs) trained with satellite imagery have been successfully used to generate measures of development indicators, such as poverty, in developing nations. This article explores a CNN‐based approach leveraging Landsat 8 imagery to predict locations of conflict‐related deaths. Using Nigeria as a case study, we use the Armed Conflict Location & Event Data (ACLED) dataset to identify locations of conflict events that did or did not result in a death. Imagery for each location is used as an input to train a CNN to distinguish fatal from non‐fatal events. Using 2014 imagery, we are able to predict the result of conflict events in the following year (2015) with 80% accuracy. While our approach does not replace the need for causal studies into the drivers of conflict death, it provides a low‐cost solution to prediction that requires only publicly available imagery to implement. Findings suggest that the information contained in moderate‐resolution imagery can be used to predict the likelihood of a death due to conflict at a given location in Nigeria the following year, and that CNN‐based methods of estimating development‐related indicators may be effective in applications beyond those explored in the literature. Numéro de notice : A2021-361 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12661 Date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1111/tgis.12661 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97625 [article]

Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource / Cédric Vega (2021) 

Public Titre : Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Milena Planells, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2021 Conférence : Atelier Theia 2021, Les utilisations de la télédétection pour la forêt 11/10/2021 Montpellier France slides & videos Langues : Français (fre) Descripteur : [Termes IGN] données auxiliaires [Termes IGN] données multisources [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] modèle numérique [Termes IGN] prédiction [Termes IGN] segmentation en régions [Termes IGN] série temporelle [Termes IGN] surveillance forestière [Vedettes matières IGN] Inventaire forestier Numéro de notice : C2021-041 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99239

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A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1740 - 1764 Titre : A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery Type de document : Article/Communication Auteurs : Bo Yang, Auteur ; Lin Liu, Auteur ; Minxuan Lan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1740 - 1764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] coefficient de corrélation [Termes IGN] criminalité [Termes IGN] données spatiotemporelles [Termes IGN] géostatistique [Termes IGN] historique des données [Termes IGN] image NPP-VIIRS [Termes IGN] krigeage [Termes IGN] modèle dynamique [Termes IGN] nuit [Termes IGN] Ohio (Etats-Unis) [Termes IGN] prédiction [Termes IGN] prévention des risques [Termes IGN] prise de vue nocturne [Termes IGN] test statistique [Termes IGN] zone urbaine Résumé : (auteur) Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. Numéro de notice : A2020-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1737701 Date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1737701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95622 [article]

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Real-time clock prediction of multi-GNSS satellites and its application in precise point positioning / Yaquan Peng in Advances in space research, vol 64 n°7 (1 October 2019)  

Public [article]  inAdvances in space research > vol 64 n°7 (1 October 2019) . - pp 1445 - 1454 Titre : Real-time clock prediction of multi-GNSS satellites and its application in precise point positioning Type de document : Article/Communication Auteurs : Yaquan Peng, Auteur ; Yidong Lou, Auteur ; Xiaopeng Gong, Auteur ; YinTong Wang, Auteur ; Xiaolei Dai, Auteur Année de publication : 2019 Article en page(s) : pp 1445 - 1454 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] horloge atomique [Termes IGN] horloge du satellite [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement par Galileo [Termes IGN] positionnement par GNSS [Termes IGN] positionnement ponctuel précis [Termes IGN] prédiction [Termes IGN] temps réel Résumé : (auteur) With the development of Global Navigation Satellite System (GNSS), multi-GNSS is expected to greatly benefit precise point positioning (PPP), especially during the outage of real time service (RTS). In this paper, we focus on the performance of multi-GNSS satellite clock prediction and its application in real-time PPP. Based on the statistical analysis of multi-system satellite clock products, a model consisting of polynomial and periodic terms is employed for multi-system satellite clock prediction. To evaluate the method proposed, both post-processed and real-time satellite clock products are employed in simulated real-time processing mode. The results show that the accuracy of satellite clock prediction is related to atomic clock type and satellite type. For GPS satellites, the average standard deviations (STDs) of Cs atomic clocks will reach as high as 0.65 ns while the STD of Rb atomic clocks is only about 0.15 ns. As for BDS and Galileo, the average STD of 2-hour satellite clock prediction are 0.30 ns and 0.06 ns, respectively. In addition, it is validated that real-time PPP can still achieve positioning accuracy of one to three decimeters by using products of 2-hour satellite clock prediction. Moreover, compared to the results of GPS-only PPP, multi-system can greatly enhance the accuracy of real-time PPP from 12.5% to 18.5% in different situations. Numéro de notice : A2019-410 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.06.040 Date de publication en ligne : 08/07/2019 En ligne : https://doi.org/10.1016/j.asr.2019.06.040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93525 [article]

A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 833 - 854 Titre : A methodology with a distributed algorithm for large-scale trajectory distribution prediction Type de document : Article/Communication Auteurs : QiuLei Guo, Auteur ; Hassan A. Karimi, Auteur Année de publication : 2019 Article en page(s) : pp 833 - 854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] gestion de trafic [Termes IGN] migration pendulaire [Termes IGN] mobilité urbaine [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] Pékin (Chine) [Termes IGN] population urbaine [Termes IGN] prédiction [Termes IGN] trafic urbain [Termes IGN] trajet (mobilité) Résumé : (Auteur) In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. Numéro de notice : A2019-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1536981 Date de publication en ligne : 31/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1536981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92690 [article]

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Analysis of GPS satellite clock prediction performance with different update intervals and application to real-time PPP / H. Yang in Survey review, vol 51 n° 364 (January 2019)  

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Machine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)  

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Remote sensing of precipitation, 2. Volume 2 / Silas Michaelides (2019)  

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Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

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Effective and annotation efficient deep learning for image understanding / Spyridon Gidaris (2018)  

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Improving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)  

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Quantifying the sources of epistemic uncertainty in model predictions of insect disturbances in an uncertain climate / David R. Gray in Annals of Forest Science [en ligne], vol 74 n° 3 (September 2017)  

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TM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)  

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Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)  

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