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Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 1 (March 2021) . - n° 4 Titre : Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest Type de document : Article/Communication Auteurs : Seyedeh Kosar Hamidi, Auteur ; Eric K. Zenner, Auteur ; Mahmoud Bayat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] dynamique de la végétation [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] forêt inéquienne [Termes descripteurs IGN] Iran [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] réseau neuronal artificiel Résumé : (auteur) Key message: We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis. Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management. Numéro de notice : A2021-071 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01011-6 date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1007/s13595-020-01011-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96794 [article]

A graph-based semi-supervised approach to classification learning in digital geographies / Pengyuan Liu in Computers, Environment and Urban Systems, vol 86 (March 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101583 Titre : A graph-based semi-supervised approach to classification learning in digital geographies Type de document : Article/Communication Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2021 Article en page(s) : n° 101583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse contextuelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] partage de données localisées [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] Time-geography Résumé : (auteur) As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks. Numéro de notice : A2021-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101583 date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101583 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96608 [article]

An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments / Zohreh Masoumi in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 137 - 154 Titre : An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments Type de document : Article/Communication Auteurs : Zohreh Masoumi, Auteur ; John L. Van Genderen, Auteur ; Sadeghi Niaraki Abolghasem, Auteur Année de publication : 2021 Article en page(s) : pp 137 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] informatique ubiquitaire [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] planification [Termes descripteurs IGN] recherche du chemin optimal Résumé : (auteur) One of the important issues in ubiquitous geographic information science (GIS) is designing user-centric path finding algorithms to meet user needs. Mostly, in a route planning problem, the user’s purpose is optimization of two or more objective functions simultaneously. Thus, the problem is a multi-objective problem. In the present study, having considered multi-objective optimization methods in path finding, we developed an algorithm for solving this problem using an improved multi-objective ant colony optimization (ACO) algorithm. Modifications are introduced for various components of the ant colony metaheuristics; specifically, for those associated with the ‘ant decision rule’. The proposed algorithm was tested on the studied network. The results demonstrate that the proposed approach has acceptable settings, repeatability and run time. In addition, one of the important research outputs is a pareto-front which allows the user to select the final path according to the desired priorities. Numéro de notice : A2021-081 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595176 date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595176 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96823 [article]

A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 321 - 347 Titre : A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Zhice Fang, Auteur ; Yi Wang, Auteur ; Ling Peng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 321 - 347 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] pondération [Termes descripteurs IGN] régression logistique [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] risque naturel Résumé : (auteur) This study introduces four heterogeneous ensemble-learning techniques, that is, stacking, blending, simple averaging, and weighted averaging, to predict landslide susceptibility in Yanshan County, China. These techniques combine several state-of-the-art classifiers of convolutional neural network, recurrent neural network, support vector machine, and logistic regression in specific ways to produce reliable results and avoid problems with the model selection. The study consists of three main steps. The first step establishes a spatial database consisting of 16 landslide conditioning factors and 380 historical landslide locations. The second step randomly selects training (70% of the total) and test (30%) datasets out of grid cells corresponding to landslide and non-slide locations in the study area. The final step constructs the proposed heterogeneous ensemble-learning methods for landslide susceptibility mapping. The proposed ensemble-learning methods show higher prediction accuracy than the individual classifiers mentioned above based on statistical measures. The blending ensemble-learning method achieves the highest overall accuracy of 80.70% compared to the other ensemble-learning methods. Numéro de notice : A2021-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808897 date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808897 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96704 [article]

Extracting knowledge from legacy maps to delineate eco-geographical regions / Lin Yang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 250 - 272 Titre : Extracting knowledge from legacy maps to delineate eco-geographical regions Type de document : Article/Communication Auteurs : Lin Yang, Auteur ; Xinming Li, Auteur ; Qinye Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 250 - 272 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] carte ancienne [Termes descripteurs IGN] carte climatique [Termes descripteurs IGN] cartographie écologique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] délimitation [Termes descripteurs IGN] données cartographiques [Termes descripteurs IGN] écorégion [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] sous ensemble flou [Termes descripteurs IGN] zone tampon Résumé : (auteur) Legacy ecoregion maps contain knowledge on relationships between eco-region units and their environmental factors. This study proposes a method to extract knowledge from legacy area-class maps to formulate a set of fuzzy membership functions useful for regionalization. We develop a buffer zone approach to reduce the uncertainty of boundaries between eco-region units on area-class maps. We generate buffer zones with a Euclidean distance perpendicular to the boundaries, then the original eco-region units without buffer zones serve as the basic units to generate the probability density functions (PDF) of environmental variables. Then, we transform the PDFs to fuzzy membership functions for class-zones on the map. We demonstrate the proposed method with a climatic zone map of China. The results showed that the buffer zone approach effectively reduced the uncertainties of boundaries. A buffer distance of 10–15 km was recommended in this study. The climatic zone map generated based on the extracted fuzzy membership functions showed a higher spatial stratification heterogeneity (compared to the original map). Based on the fuzzy membership functions with climate data of 1961–2015, we also prepared an updated climatic zone map. This study demonstrates the prospects of using fuzzy membership functions to delineate area classes for regionalization purpose. Numéro de notice : A2021-025 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1806284 date de publication en ligne : 17/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1806284 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96692 [article]

Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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A spatiotemporal structural graph for characterizing land cover changes / Bin Wu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Uncertainties and errors in algorithms for elevation gradients / Dong Shi in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Accurate assessment of protected area boundaries for land use planning using 3D GIS / Dilek Tezel in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions / Kamil Maciuk in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

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Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)  

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Copula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain / Roya Mousavian in GPS solutions, vol 25 n° 1 (January 2021)  

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