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Termes IGN > mathématiques > statistique mathématique
statistique mathématique
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biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
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Comparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)
[article]
Titre : Comparative analysis of different CNN models for building segmentation from satellite and UAV images Type de document : Article/Communication Auteurs : Batuhan Sariturk, Auteur ; Damla Kumbasar, Auteur ; Dursun Zafer Seker, Auteur Année de publication : 2023 Article en page(s) : pp 97 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image captée par drone
[Termes IGN] image satellite
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Building segmentation has numerous application areas such as urban planning and disaster management. In this study, 12 CNN models (U-Net, FPN, and LinkNet using EfficientNet-B5 backbone, U-Net, SegNet, FCN, and six Residual U-Net models) were generated and used for building segmentation. Inria Aerial Image Labeling Data Set was used to train models, and three data sets (Inria Aerial Image Labeling Data Set, Massachusetts Buildings Data Set, and Syedra Archaeological Site Data Set) were used to evaluate trained models. On the Inria test set, Residual-2 U-Net has the highest F1 and Intersection over Union (IoU) scores with 0.824 and 0.722, respectively. On the Syedra test set, LinkNet-EfficientNet-B5 has F1 and IoU scores of 0.336 and 0.246. On the Massachusetts test set, Residual-4 U-Net has F1 and IoU scores of 0.394 and 0.259. It has been observed that, for all sets, at least two of the top three models used residual connections. Therefore, for this study, residual connections are more successful than conventional convolutional layers. Numéro de notice : A2023-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00084R2 Date de publication en ligne : 01/02/2023 En ligne : https://doi.org/10.14358/PERS.22-00084R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102718
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 2 (February 2023) . - pp 97 - 105[article]Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network / Jingan Wu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)
[article]
Titre : Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network Type de document : Article/Communication Auteurs : Jingan Wu, Auteur ; Liupeng Lin, Auteur ; Chi Zhang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 16 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] affinage d'image
[Termes IGN] approche hiérarchique
[Termes IGN] bande spectrale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] filtre passe-haut
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSIRésumé : (Auteur) Earth observations from the Sentinel-2 mission have been extensively accepted in a variety of land services. The thirteen spectral bands of Sentinel-2, however, are collected at three spatial resolutions of 10/20/60 m, and such a difference brings difficulties to analyze multispectral imagery at a uniform resolution. To address this problem, we developed a hierarchical fusion network (HFN) to sharpen 20/60-m bands and generate Sentinel-2 all-band 10-m data. The deep learning architecture is used to learn the complex mapping between multi-resolution input and output data. Given the deficiency of previous studies in which the spatial information is inferred only from the fine-resolution bands, the proposed hierarchical fusion framework simultaneously leverages the self-similarity information from coarse-resolution bands and the spatial structure information from fine-resolution bands, to enhance the sharpening performance. Technically, the coarse-resolution bands are super-resolved by exploiting the information from themselves and then sharpened by fusing with the fine-resolution bands. Both 20-m and 60-m bands can be sharpened via the developed approach. Experimental results regarding visual comparison and quantitative assessment demonstrate that HFN outperforms the other benchmarking models, including pan-sharpening-based, model-based, geostatistical-based, and other deep-learning-based approaches, showing remarkable performance in reproducing explicit spatial details and maintaining original spectral features. Moreover, the developed model works more effectively than the other models over the heterogeneous landscape, which is usually considered a challenging application scenario. To sum up, the fusion model can sharpen Sentinel-2 20/60-m bands, and the created all-band 10-m data allows image analysis and geoscience applications to be authentically carried out at the 10-m resolution. Numéro de notice : A2023-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.017 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102392
in ISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 16 - 31[article]Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)
[article]
Titre : Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models Type de document : Article/Communication Auteurs : Xikun Hu, Auteur ; Puzhao Zhang, Auteur ; Yifang Ban, Auteur Année de publication : 2023 Article en page(s) : pp 228 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dommage
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] jeu de données localisées
[Termes IGN] segmentation sémantique
[Termes IGN] surveillance forestière
[Termes IGN] zone sinistréeRésumé : (auteur) Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g., dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities. Numéro de notice : A2023-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.026 Date de publication en ligne : 11/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102498
in ISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 228 - 240[article]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)
[article]
Titre : Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model Type de document : Article/Communication Auteurs : Zensheng Wang, Auteur ; Feidong Lu, Auteur ; Zhaohui Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 339 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] approche hiérarchique
[Termes IGN] classification bayesienne
[Termes IGN] dynamique spatiale
[Termes IGN] estimation bayesienne
[Termes IGN] hétérogénéité spatiale
[Termes IGN] modèle de simulation
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] Shenzhen
[Termes IGN] téléphonie mobile
[Termes IGN] urbanisation
[Termes IGN] utilisation du solRésumé : (auteur) Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics. Numéro de notice : A2023-057 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2117363 En ligne : https://doi.org/10.1080/13658816.2022.2117363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102393
in International journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 339 - 359[article]Multi-nomenclature, multi-resolution joint translation: an application to land-cover mapping / Luc Baudoux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)
[article]
Titre : Multi-nomenclature, multi-resolution joint translation: an application to land-cover mapping Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Année de publication : 2023 Projets : AI4GEO / Article en page(s) : pp 403 - 437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] apprentissage profond
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] carte thématique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] harmonisation des données
[Termes IGN] nomenclature
[Termes IGN] pouvoir de résolution géométriqueRésumé : (auteur) Land-use/land-cover (LULC) maps describe the Earth’s surface with discrete classes at a specific spatial resolution. The chosen classes and resolution highly depend on peculiar uses, making it mandatory to develop methods to adapt these characteristics for a large range of applications. Recently, a convolutional neural network (CNN)-based method was introduced to take into account both spatial and geographical context to translate a LULC map into another one. However, this model only works for two maps: one source and one target. Inspired by natural language translation using multiple-language models, this article explores how to translate one LULC map into several targets with distinct nomenclatures and spatial resolutions. We first propose a new data set based on six open access LULC maps to train our CNN-based encoder-decoder framework. We then apply such a framework to convert each of these six maps into each of the others using our Multi-Landcover Translation network (MLCT-Net). Extensive experiments are conducted at a country scale (namely France). The results reveal that our MLCT-Net outperforms its semantic counterparts and gives on par results with mono-LULC models when evaluated on areas similar to those used for training. Furthermore, it outperforms the mono-LULC models when applied to totally new landscapes. Numéro de notice : A2023-075 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2120996 Date de publication en ligne : 10/10/2022 En ligne : https://doi.org/10.1080/13658816.2022.2120996 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101797
in International journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 403 - 437[article]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)PermalinkPSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkSpecies-specific deadwood density, its controlling factors and its role in the estimation of deadwood C stock of a Virgin European Beech-Silver Fir Mixed Forest in the Southern Carpathians / Ion Catalin Petritan in SSRN [preprint electronic journal], vol 2023 ([01/02/2023])PermalinkStochastic multicriteria acceptability analysis as a forest management priority mapping approach based on airborne laser scanning and field inventory data / Parvez Rana in Landscape and Urban Planning, vol 230 (February 2023)PermalinkTemporal spectrum of spatial correlations between GNSS station position time series / Yujiao Niu in Journal of geodesy, vol 97 n° 2 (February 2023)PermalinkTopology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds / Xin Xu in International journal of applied Earth observation and geoinformation, vol 116 (February 2023)PermalinkThe ULR-repro3 GPS data reanalysis and its estimates of vertical land motion at tide gauges for sea level science / Médéric Gravelle in Earth System Science Data, vol 15 n° 1 (2023)PermalinkAnalysis of cycling network evolution in OpenStreetMap through a data quality prism / Raphaël Bres (2023)PermalinkPermalinkPermalinkA CNN based approach for the point-light photometric stereo problem / Fotios Logothetis in International journal of computer vision, vol 131 n° 1 (January 2023)PermalinkA comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkDecadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkDecision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkEstablishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP / Pengfei Xia in GPS solutions, vol 27 n° 1 (January 2023)PermalinkField optical clocks and sensitivity to mass anomalies for geoscience applications / Guillaume Lion (2023)PermalinkForest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkGeneration of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkGeographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model / Zhenyang Du in Journal of Marine Systems, vol 237 (January 2023)PermalinkGeographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)Permalink