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scientifique

Automated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 8 (April-2 2022) . - n° 1931 Titre : Automated inventory of broadleaf tree plantations with UAS imagery Type de document : Article/Communication Auteurs : Aishwarya Chandrasekaran, Auteur ; Guofan Shao, Auteur ; Songlin Fei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1931 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] feuillu [Termes IGN] hauteur à la base du houppier [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] Indiana (Etats-Unis) [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] orthophotoplan numérique [Termes IGN] plantation forestière [Termes IGN] R (langage) [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) With the increased availability of unmanned aerial systems (UAS) imagery, digitalized forest inventory has gained prominence in recent years. This paper presents a methodology for automated measurement of tree height and crown area in two broadleaf tree plantations of different species and ages using two different UAS platforms. Using structure from motion (SfM), we generated canopy height models (CHMs) for each broadleaf plantation in Indiana, USA. From the CHMs, we calculated individual tree parameters automatically through an open-source web tool developed using the Shiny R package and assessed the accuracy against field measurements. Our analysis shows higher tree measurement accuracy with the datasets derived from multi-rotor platform (M600) than with the fixed wing platform (Bramor). The results show that our automated method could identify individual trees (F-score > 90%) and tree biometrics (root mean square error Numéro de notice : A2022-351 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14081931 Date de publication en ligne : 16/04/2022 En ligne : https://doi.org/10.3390/rs14081931 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100539 [article]

Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 4 [15/02/2022] . - pp 961 - 977 Titre : Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms Type de document : Article/Communication Auteurs : Marzieh Fadaee, Auteur ; Amin Mahdavi-Meymand, Auteur ; Mohammad Zounemat-Kermani, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 961 - 977 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] algorithme de Levenberg-Marquardt [Termes IGN] algorithme génétique [Termes IGN] analyse comparative [Termes IGN] Indiana (Etats-Unis) [Termes IGN] Inférence floue [Termes IGN] modèle de simulation [Termes IGN] optimisation (mathématiques) [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel [Termes IGN] sédiment Résumé : (auteur) The present study investigates the capability of two metaheuristic optimization approaches, namely the Butterfly Optimization Algorithm (BOA) and the Genetic Algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) are applied as the predictive data-driven models. Independent input variables, i.e., the water temperature (T), river discharge (Q), and specific conductance (SC) are used for the prediction of SSL based on several statistical indices. The results indicate that the performances of all studied models were close to one another; moreover, the metaheuristic algorithms were found to increase the accuracy of the ANFIS and ANN models for approximately 11.73 percent and 4.30 percent, respectively. In general, the BOA outperformed the GA in enhancing the optimization performance of the learning process in the applied machine learning models. Numéro de notice : A2022-392 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1753821 Date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1753821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100685 [article]

Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 1 (February 2022) . - pp 236 - 258 Titre : Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method Type de document : Article/Communication Auteurs : Yijiang Zhao, Auteur ; Wentao Yang, Auteur ; Yizhi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 236 - 258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] Açores, archipel des [Termes IGN] algorithme glouton [Termes IGN] données localisées des bénévoles [Termes IGN] étiquette [Termes IGN] géobalise [Termes IGN] Indiana (Etats-Unis) [Termes IGN] OpenStreetMap [Termes IGN] réseau routier Résumé : (auteur) Numerous studies have shown that OpenStreetMap (OSM) data can achieve high positional quality. However, the thematic attributes of OSM objects can be modified several times, which has a large impact on semantic heterogeneity. Identifying transition patterns within OSM feature classes is an important preliminary step for the tag recommendation algorithm, which can reduce the number of modifications and enhance the efficiency of OSM data updates. In this article, we propose an approach for discovering transition patterns among OSM feature classes. We first produced the transition matrix of feature classes and then developed a graph. Next, the Louvain method for community detection was utilized to cluster the feature classes. OSM data from Indiana, USA, and the Azores, Portugal, were used for our experiments. Some transition patterns were discovered: (1) many feature classes with the most transitions are the same in both datasets and most transitions occur in road-related feature classes; (2) people tend to tag general classes if they are unsure of the specific classes of tagged objects; and (3) most class transitions occurred as a result of volunteers improving the specificity and precision of feature classes. Moreover, consistently confusing concept pairs were identified. Numéro de notice : A2022-178 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12843 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1111/tgis.12843 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99835 [article]

A deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 759 - 766 Titre : A deep multi-modal learning method and a new RGB-depth data set for building roof extraction Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2021 Article en page(s) : pp 759 - 766 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] détection du bâti [Termes IGN] données multisources [Termes IGN] effet de profondeur cinétique [Termes IGN] empreinte [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image RVB [Termes IGN] Indiana (Etats-Unis) [Termes IGN] réseau neuronal convolutif [Termes IGN] réseau neuronal profond [Termes IGN] segmentation d'image [Termes IGN] superpixel [Termes IGN] toit Résumé : (Auteur) This study focuses on tackling the challenge of building mapping in multi-modal remote sensing data by proposing a novel, deep superpixel-wise convolutional neural network called DeepQuantized-Net, plus a new red, green, blue (RGB)-depth data set named IND. DeepQuantized-Net incorporated two practical ideas in segmentation: first, improving the object pattern with the exploitation of superpixels instead of pixels, as the imaging unit in DeepQuantized-Net. Second, the reduction of computational cost. The generated data set includes 294 RGB-depth images (256 training images and 38 test images) from different locations in the state of Indiana in the U.S., with 1024 × 1024 pixels and a spatial resolution of 0.5 ftthat covers different cities. The experimental results using the IND data set demonstrates the mean F1 scores and the average Intersection over Union scores could increase by approximately 7.0% and 7.2% compared to other methods, respectively. Numéro de notice : A2021-677 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00007R2 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.14358/PERS.21-00007R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98878 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Improving the upscaling of land cover maps by fusing uncertainty and spatial structure information / Peijun Sun in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 2 (February 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 2 (February 2018) . - pp 87 - 100 Titre : Improving the upscaling of land cover maps by fusing uncertainty and spatial structure information Type de document : Article/Communication Auteurs : Peijun Sun, Auteur ; Russell G. Congalton, Auteur ; Yaozhong Pan, Auteur Année de publication : 2018 Article en page(s) : pp 87 - 100 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Caroline du Sud (Etats-Unis) [Termes IGN] carte agricole [Termes IGN] carte d'occupation du sol [Termes IGN] erreur systématique [Termes IGN] fusion de données [Termes IGN] incertitude des données [Termes IGN] Indiana (Etats-Unis) [Termes IGN] mise à jour de base de données Résumé : (Auteur) Upscaling land cover maps is broadly employed to fill data gaps or match the spatial-resolution of preexisting projects. However, existing methods introduce systematic errors in the area information and the landscape pattern. We developed an upscaling method fusing the spatial structure information (i.e., class Membership probability) and the uncertainty information of the base map (e.g., Confidence level probability), called Fusing class Membership probability and Confidence level probability (FMC). The results showed that FMC obtained higher upscaling efficiency, and mitigated the negative influence of landscape heterogeneity and the negative influence of unequal proportions of land cover in the base maps, on the upscaling compared to Majority Rule Based (MRB) method. Additionally, FMC can reduce the uncertainty/error when these upscaled maps are used as input to Earth observation model (e.g., land cover change). Numéro de notice : A2018-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.84.2.87 En ligne : https://doi.org/10.14358/PERS.84.2.87 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89316 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Hyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)  

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Landscape metrics for analysing urbanization-induced land use and land cover changes / Hua Liu in Geocarto international, vol 28 n° 7-8 (November - December 2013)  

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An edge-oriented approach to thematic map error assessment / S. Sweeney in Geocarto international, vol 27 n° 1 (February 2012)  

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Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method / Xuefei Hu in Geocarto international, vol 26 n° 1 (February 2011)  

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Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images / S. Al-Kheder in International journal of geographical information science IJGIS, vol 22 n°11-12 (november 2008)  

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Modelling house unit density from land cover metrics: a Midwestern US example / P. Hardin in Geocarto international, vol 23 n° 5 (October - November 2008)  

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People, pixels and weights in Vanderburgh County, Indiana: toward a new urban geography of human-environment interactions / E.W. Lafary in Geocarto international, vol 23 n° 1 (February - March 2008)  

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Urban surface biophysical descriptors and land surface temperature variations / D. Weng in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 11 (November 2006)  

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Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery / Dong Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 9 (September 2004)  

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Racing into tomorrow, 1985 ACSM ASPRS Fall convention / American society for photogrammetry and remote sensing (1985)

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