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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])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 121 -1 36 Titre : Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling Type de document : Article/Communication Auteurs : Stefanos Georganos, Auteur ; Tais Grippa, Auteur ; Assane Niang Gadiaga, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 121 -1 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Dakar [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] population [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. Numéro de notice : A2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595177 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96822 [article]

Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 84 (November 2020) . - n° 101524 Titre : Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city Type de document : Article/Communication Auteurs : Azam Raha Bahrehdar, Auteur ; Benjamin Adams, Auteur ; Ross S. Purves, Auteur Année de publication : 2020 Article en page(s) : n° 101524 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] Londres [Termes descripteurs IGN] mesure de similitude [Termes descripteurs IGN] métadonnées [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] orthoimage géoréférencée [Termes descripteurs IGN] perception [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation. Numéro de notice : A2020-710 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101524 date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101524 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96255 [article]

Camera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs / Grégoire Guillet in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 237 - 255 Titre : Camera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs Type de document : Article/Communication Auteurs : Grégoire Guillet, Auteur ; Thomas Guillet, Auteur ; Ludovic Ravanel, Auteur Année de publication : 2020 Article en page(s) : pp 237 - 255 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] ajustement de paramètres [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] étalonnage de chambre métrique [Termes descripteurs IGN] figuration de la densité [Termes descripteurs IGN] fonction inverse [Termes descripteurs IGN] image 2D [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] longueur focale [Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orientation externe [Termes descripteurs IGN] photographie numérique [Termes descripteurs IGN] vue 3D [Termes descripteurs IGN] vue perspective Résumé : (Auteur) Large collections of images have become readily available through modern digital catalogs, from sources as diverse as historical photographs, aerial surveys, or user-contributed pictures. Exploiting the quantitative information present in such wide-ranging collections can greatly benefit studies that follow the evolution of landscape features over decades, such as measuring areas of glaciers to study their shrinking under climate change. However, many available images were taken with low-quality lenses and unknown camera parameters. Useful quantitative data may still be extracted, but it becomes important to both account for imperfect optics, and estimate the uncertainty of the derived quantities. In this paper, we present a method to address both these goals, and apply it to the estimation of the area of a landscape feature traced as a polygon on the image of interest. The technique is based on a Bayesian formulation of the camera calibration problem. First, the probability density function (PDF) of the unknown camera parameters is determined for the image, based on matches between 2D (image) and 3D (world) points together with any available prior information. In a second step, the posterior distribution of the feature area of interest is derived from the PDF of camera parameters. In this step, we also model systematic errors arising in the polygon tracing process, as well as uncertainties in the digital elevation model. The resulting area PDF therefore accounts for most sources of uncertainty. We present validation experiments, and show that the model produces accurate and consistent results. We also demonstrate that in some cases, accounting for optical lens distortions is crucial for accurate area determination with consumer-grade lenses. The technique can be applied to many other types of quantitative features to be extracted from photographs when careful error estimation is important. Numéro de notice : A2020-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.013 date de publication en ligne : 02/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94404 [article]

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Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1703 - 1717 Titre : Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets Type de document : Article/Communication Auteurs : Daniel A. Griffith, Auteur ; Yongwan Chun, Auteur Année de publication : 2019 Article en page(s) : pp 1703 - 1717 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] approximation [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] tessellation [Termes descripteurs IGN] vecteur propre Mots-clés libres : Moran eigenvector spatial filtering Résumé : (auteur) Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. Numéro de notice : A2019-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1080/13658816.2019.1593421 date de publication en ligne : 02/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1593421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93479 [article]

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Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation : a simulation study / Sang-Il Lee in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1135 - 1154 Titre : Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation : a simulation study Type de document : Article/Communication Auteurs : Sang-Il Lee, Auteur ; Monghyeon Lee, Auteur ; Yongwan Chun, Auteur ; Daniel A. Griffith, Auteur Année de publication : 2019 Article en page(s) : pp 1135 - 1154 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] problème d'unité zonale modifiable Résumé : (Auteur) The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects. Numéro de notice : A2019-444 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542699 date de publication en ligne : 13/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92779 [article]

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Méthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs / Yann Méneroux (2019)  

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Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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Manuel d'analyse spatiale / Vincent Loonis (2018)    

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An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings / Sergio J. Rey in Transactions in GIS, vol 21 n° 4 (August 2017)  

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How does spatial scale affect species richness modelling? A test using remote sensing data and geostatistics / M. Marcantonio in Annali di Botanica, vol 7 (2017)  

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GNSS interferometric radio occultation / Manuel Martín-Neira in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

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R et espace : Traitement de l’information géographique / Groupe Element R (2014)    

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Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon fire of 2012 / Joshua Kent in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)  

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Improving the assessment of ICESat water altimetry accuracy accounting for autocorrelation / Hani Abdallah in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 (November 2011)  

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Spatial patterns and eco-epidemiological systems – part 1: multi-scale spatial modelling of the occurrence of Chagas disease insect vectors / Emmanuel Roux in Geospatial Health, vol 6 n° 1 (November 2011)  

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