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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 descripteurs IGN] algorithme du gradient [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] chaleur [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] Londres [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] mortalité [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] politique publique [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] santé [Termes descripteurs IGN] station météorologique [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] température de l'air [Termes descripteurs 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 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]

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070 Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] atmosphère terrestre [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] faisceau laser [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] foresterie [Termes descripteurs IGN] forêt feuillue [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transfert radiatif Résumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970 [article]

Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model / Mariana Madruga de bruto 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 1788-1806 Titre : Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model Type de document : Article/Communication Auteurs : Mariana Madruga de bruto, Auteur ; Adrian Almoradie, Auteur ; Mariele Evers, Auteur Année de publication : 2019 Article en page(s) : pp 1788-1806 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] Geospatial data abstraction library [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] inondation [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] vulnérabilité [Termes descripteurs IGN] zone à risque Résumé : (auteur) This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems. Numéro de notice : A2019-389 Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1599125 date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1599125 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93480 [article]

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Bridging open source tools and geoportals for interactive spatial data analytics / Bing She in Geo-spatial Information Science, vol 22 n° 3 (August 2019)  

Public [article]  inGeo-spatial Information Science > vol 22 n° 3 (August 2019) . - pp 185 - 192 Titre : Bridging open source tools and geoportals for interactive spatial data analytics Type de document : Article/Communication Auteurs : Bing She, Auteur ; Tao Hu, Auteur ; Xinyan Zhu, Auteur ; S. Bao, Auteur Année de publication : 2019 Article en page(s) : pp 185 - 192 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] code source libre [Termes descripteurs IGN] geoportail [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] WebSIG Résumé : (Auteur) Geoportals have been the primary source of spatial information to researchers in diverse fields. Recent years have seen a growing trend to integrate spatial analysis and geovisual analytics inside Geoportals. Researchers could use the Geoportal to conduct basic analysis without offline processing. In practice, domain-specific analysis often requires researchers to integrate heterogeneous data sources, leverage new statistical models, or build their own customized models. These tasks are increasingly being tackled with open source tools in programming languages such as Python or R. However, it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and analysis. This work provides an exploratory effort to bridge Geoportals and open source tools through Python scripting. The Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies. A python package is provided to manipulate this platform in the local programming environment. The server side of the Geoportal implements a set of service endpoints that allows the package to upload, transform, and process user data and seamlessly integrate them into the existing datasets. A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census data. This work attempts a new direction in Geoportal development, which could further promote the transformation of Geoportals into online analytical workbenches. Numéro de notice : A2019-327 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2019.1645497 date de publication en ligne : 29/07/2019 En ligne : https://doi.org/10.1080/10095020.2019.1645497 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93332 [article]

Mise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)

Public [article]  inRevue Française de Photogrammétrie et de Télédétection > n° 219-220 (juin - octobre 2019) . - pp 59 - 70 Titre : Mise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo Type de document : Article/Communication Auteurs : Cédric Lardeux, Auteur ; Anoumou Kemavo, Auteur ; Maxence Rageade, Auteur ; Mathieu Rahm, Auteur ; Pierre-Louis Frison , Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2019 Article en page(s) : pp 59 - 70 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] Guyane (département français) [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Orfeo Tool Box [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] QGIS [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] télédétection en hyperfréquence [Termes descripteurs IGN] Togo Résumé : (auteur) Remote sensing is a particularly suitable tool for monitoring land use but also particularly suited for deforestation monito-ring. By launching the Setinel-1 and Sentinel-2 satellites in the Copernicus program, the community now has free data with important time-revisits allowing the greatest number of people to effectively monitor land cover of a study area. This paper presents a land use monitoring method based on the combination of two approaches computed using Open Source tools ((MIS, Orfeo ToolBox, python). First, we focus on land use monitoring at an annual time scale using Sentinel-1 and Sentinel-2 data, and then, by the use f entienl-1 data, we detect changes in the forest cover due to deforestation at bi-monthly time scale. The results obtained show a very good synergy of these approaches allowing the complementary of optical and radar data. In order to make accessible the proposed method, all the used open source tools are available on this link http ://remotesensing4all.net/index.php/2018/09/11/ kit-use-des-donnees-radar- sentinel-1-in-de-latelier-radar-du-FOSS4G-en-2018-2/. Numéro de notice : A2019-348 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93385 [article]

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The possibility of measuring the dynamic response of structures using non-contact geodetic method / Bostjan Kovacic in Geodetski vestnik, vol 63 n° 1 (March - May 2019)  

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Contribution au développement d’une plateforme web d’analyse réglementaire et de gestion des vols de drones / Yassmine Boudili (2019) 

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Simultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography / Thibaud Toullier (2019)  

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Webscraping, bigdata et analyse spatiale de données immobilières : réponse à un projet ESPON au sein de l'UMS RIATE / Marc Lieury (2019) 

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Precise DEM extraction from Svalbard using 1936 high oblique imagery / Luc Girod in Geoscientific instrumentation methods and data systems, vol 7 n° 4 ([01/10/2018])    

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Voronoi tessellation on the ellipsoidal earth for vector data / Christos Kastrisios in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)  

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Traitement d’image en Python avec RSGISLib / Anonyme in Géomatique expert, n° 121 (mars - avril 2018)

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Adapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018) 

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An (almost) automated process to track the Martians dunes : ac.GetPreciseShifts / Arthur Coqué (2018) 

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Caractérisation et qualification de Modèles Numériques de Surfaces (MNS) - Analyse de la cohérence avec des masques d’eau / Guillaume Sutter (2018)

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