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A Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 5 (May 2021) . - pp 999 - 1031 Titre : A Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm Type de document : Article/Communication Auteurs : Vorapong Suppakitpaisarn, Auteur ; Atthaphon Ariyarit, Auteur ; Supanut Chaidee, Auteur Année de publication : 2021 Article en page(s) : pp 999 - 1031 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme du gradient [Termes IGN] algorithme génétique [Termes IGN] benchmark spatial [Termes IGN] diagramme de Voronoï [Termes IGN] mode d'occupation du sol [Termes IGN] Thaïlande [Termes IGN] utilisation du sol Résumé : (Auteur) The land-use optimization involves divisions of land into subregions to obtain spatial configuration of compact subregions and desired connections among them. Computational geometry-based algorithms, such as Voronoi diagram, are known to be efficient and suitable for iterative design processes to achieve land-use optimization. However, such algorithms assume that generating point positions are given as inputs, while we usually do not know the positions in advance. In this study, we propose a method to automatically calculate the suitable point positions. The method uses (1) semidefinite programming to approximate locations while maintaining relative positions among locations; and (2) gradient descent to iteratively update locations subject to area constraints. We apply the proposed framework to a practical case at Chiang Mai University and compare its performance with a benchmark, the differential genetic algorithm. The results show that the proposed method is 28 times faster than the differential genetic algorithm, while the resulting land allocation error is slightly larger than that of the benchmark but still acceptable. Additionally, the output does not contain disconnected areas, as found in all evolutionary computations, and the compactness is almost equal to the maximum possible value. Numéro de notice : A2021-336 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1841203 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1841203 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97555 [article]

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A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381 Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] algorithme du gradient [Termes IGN] appariement automatique [Termes IGN] appariement de données localisées [Termes IGN] apprentissage automatique [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] exploration de données [Termes IGN] intégration de données [Termes IGN] point d'intérêt [Termes IGN] pondération [Termes IGN] qualité des données Résumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158 [article]

Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 10 p. Titre : Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data Type de document : Article/Communication Auteurs : Rochelle Schneider dos Santos, Auteur Année de publication : 2020 Article en page(s) : 10 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme du gradient [Termes IGN] apprentissage automatique [Termes IGN] chaleur [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] ilot thermique urbain [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] Londres [Termes IGN] modèle de régression [Termes IGN] mortalité [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] politique publique [Termes IGN] Python (langage de programmation) [Termes IGN] régression linéaire [Termes IGN] santé [Termes IGN] station météorologique [Termes IGN] température au sol [Termes IGN] température de l'air [Termes IGN] zone urbaine Résumé : (auteur) Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population. Numéro de notice : A2020-448 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102066 Date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102066 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95524 [article]

Convergence of one-step projected gradient methods for variational inequalities / Paul-Emile Maingé in Journal of Optimization Theory and Applications, vol 171 n° 1 (October 2016)  

Public [article]  inJournal of Optimization Theory and Applications > vol 171 n° 1 (October 2016) . - pp 146 - 168 Titre : Convergence of one-step projected gradient methods for variational inequalities Type de document : Article/Communication Auteurs : Paul-Emile Maingé, Auteur ; Marie-Line Gobinddass , Auteur Année de publication : 2016 Article en page(s) : pp 146 - 168 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique [Termes IGN] algorithme du gradient [Termes IGN] calcul variationnel [Termes IGN] optimisation (mathématiques) Résumé : (auteur) In this paper, we revisit the numerical approach to some classical variational inequalities, with monotone and Lipschitz continuous mapping A, by means of a projected reflected gradient-type method. A main feature of the method is that it formally requires only one projection step onto the feasible set and one evaluation of the involved mapping per iteration. Contrary to what was done so far, we establish the convergence of the method in a more general setting that allows us to use varying step-sizes without any requirement of additional projections. A linear convergence rate is obtained, when A is assumed to be strongly monotone. Preliminary numerical experiments are also performed. Numéro de notice : A2016-973 Affiliation des auteurs : LASTIG LAREG+Ext (2012-mi2018) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10957-016-0972-4 Date de publication en ligne : 06/07/2016 En ligne : http://dx.doi.org/10.1007/s10957-016-0972-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94217 [article]

Single tree detection from airborne laser scanning data using a marked point process based method / Junjie Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W1 (May 2013)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 W1 (May 2013) . - pp 41 - 46 Titre : Single tree detection from airborne laser scanning data using a marked point process based method Type de document : Article/Communication Auteurs : Junjie Zhang, Auteur ; Gunho Sohn, Auteur ; Mathieu Brédif , Auteur Année de publication : 2013 Conférence : ISPRS VCM 2013, ISPRS Workshop on 3D Virtual City Modeling 28/05/2013 25/05/2013 Regina Saskatchewan - Canada OA ISPRS Annals Article en page(s) : pp 41 - 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme du gradient [Termes IGN] arbre (flore) [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] enthalpie libre [Termes IGN] forêt [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] placette d'échantillonnage [Termes IGN] programmation par contraintes [Termes IGN] reconstruction d'objet Mots-clés libres : Single tree detection  marked point process  segmentation Résumé : (auteur) Tree detection and reconstruction is of great interest in large-scale city modelling. In this paper, we present a marked point process model to detect single trees from airborne laser scanning (ALS) data. We consider single trees in ALS recovered canopy height model (CHM) as a realization of point process of circles. Unlike traditional marked point process, we sample the model in a constraint configuration space by making use of image process techniques. A Gibbs energy is defined on the model, containing a data term which judge the fitness of the model with respect to the data, and prior term which incorporate the prior knowledge of object layouts. We search the optimal configuration through a steepest gradient descent algorithm. The presented hybrid framework was test on three forest plots and experiments show the effectiveness of the proposed method. Numéro de notice : A2013-821 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W1-41-2013 Date de publication en ligne : 16/05/2013 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-W1-41-2013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80995 [article]

Sensitivity analysis of spatially aggregated responses: a gradient-based method / F. Pantus in International journal of geographical information science IJGIS, vol 22 n° 4-5 (april 2008)  

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