Centre dedocumentation
scientifique

A regression model of spatial accuracy prediction for Openstreetmap buildings / Ibrahim Maidaneh Abdi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-4 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-4 (August 2020) . - pp 39 - 47 Titre : A regression model of spatial accuracy prediction for Openstreetmap buildings Type de document : Article/Communication Auteurs : Ibrahim Maidaneh Abdi, Auteur ; Arnaud Le Guilcher , Auteur ; Ana-Maria Olteanu-Raimond , Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Commanditaire Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 39 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bati [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] précision géométrique (imagerie) [Termes descripteurs IGN] qualité des données Résumé : (auteur) Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationship between measures of extrinsic quality of OSM (i.e. obtained by comparison with reference spatial data) and the measures of intrinsic quality of OSM (i.e. OSM features themselves) in order to estimate extrinsic quality on an unevaluated OSM dataset. The approach was applied on OSM buildings. On our dataset, the resulting regression model predicts the values on the extrinsic quality indicators with 30% less variance than an uninformed predictor. Numéro de notice : A2020-506 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2020-39-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2020-39-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95647 [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 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]

Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 16 p. Titre : Fine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method Type de document : Article/Communication Auteurs : Zhenzhong Peng, Auteur ; Ru Wang, Auteur ; Lingbo Liu, Auteur ; Hao Wu, Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] bati [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] diagramme de Voronoï [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données démographiques [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] petite échelle [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] téléphone intelligent Résumé : (auteur) Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data. Numéro de notice : A2020-315 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060344 date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060344 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95170 [article]

Soil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu 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 3190 - 3201 Titre : Soil moisture estimation with SVR and data augmentation based on alpha approximation method Type de document : Article/Communication Auteurs : Wei Xu, Auteur ; Zhaoxu Zhang, Auteur ; Qiming Qin, Auteur Année de publication : 2020 Article en page(s) : pp 3190 - 3201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] approximation [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] irrigation [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] surveillance agricole Résumé : (auteur) Soil moisture content is an important parameter in hydrological, meteorological, and agricultural applications. Balenzano et al. proposed the alpha approximation method in 2011 for solving some complex issues during the retrieval of soil moisture over agricultural crops with synthetic aperture radar data. However, determining the constraints and solving the underdetermined system of equations in this method add new challenges. Considering the questions of constraints and underdetermined system of equations, the alpha approximation method is used to augment the measured data, and can avoid solving the underdetermined system of equations with constraints directly. Then, these data are applied in a support vector regression machine for soil moisture estimation. It is found that when an optimal model is determined, the method proposed in this article is superior to the direct use of the alpha approximation method, and the root-mean-squared error (RMSE) decreased from 0.0775 to 0.0339 and R 2 increased from 0.0467 to 0.6491. In addition, the method obtained a good result from a data set collected that included a different growing period of crops by changing the standardized method from StandardScaler to Scale , where the RMSE is 0.0501 and R 2 is 0.3204. This indicates the good generalization capability of this method. In conclusion, the proposed method solves the two questions effectively and provides a potential way for long-time or large-scale soil moisture monitoring with much less in situ measurements. Numéro de notice : A2020-235 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950321 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950321 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94981 [article]

Spectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils / Haein Shin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2266 - 2275 Titre : Spectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils Type de document : Article/Communication Auteurs : Haein Shin, Auteur ; Jaehyung Yu, Auteur ; Lei Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2266 - 2275 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] arsenic [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] contamination [Termes descripteurs IGN] cuivre [Termes descripteurs IGN] dégradation du signal [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] interférence [Termes descripteurs IGN] métal lourd [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] plomb [Termes descripteurs IGN] pollution des sols [Termes descripteurs IGN] signature spectrale Résumé : (auteur) This article examined the spectral interference by heavy metal on the spectral signal of moisture content of heavy metal contaminated soils. Soil samples were collected from an abandoned mine area, and the chemical analysis revealed extremely high contamination amount of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb). The mineralogical analysis showed that the spectral signature of the heavy metal contaminated soils was manifested by secondary minerals. Water content suppressed the spectral reflectance of the soil samples but increased the absorption depths. Although a regression model can predict moisture content using the magnitude of the water absorption feature, the accuracy was much lower when the heavy metal concentration was extremely high. It indicates that geochemical reactions between the heavy metal cation and iron oxide/clay minerals may have affected the spectral responses of the contaminated soils at the water absorption bands. Our model also showed that there was a shift of the absorption features of moisture content if the heavy metal contamination level went up. Unlike normal soils, the absorption features of clay minerals and ferric iron were not able to accurately predict moisture in highly contaminated soils. Given the fact that the spectral bands selected in this article were associated with water absorption, the findings from this article may only be useful to a drone-based low-altitude remote sensing of soil moisture content. Numéro de notice : A2020-193 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946297 date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2946297 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94860 [article]

A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

Permalink

Regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures / Timofey Samsonov in International journal of cartography, Vol 6 n° 1 (March 2020)  

Permalink

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Permalink

Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Permalink

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping / Wojciech Drzewiecki in Geodesy and cartography, vol 66 n° 2 (December 2017)  

Permalink

Adaptive and optimal combination of local features for image retrieval / Neelanjan Bhowmik (2017)    

Permalink

Cross-domain image localization by adaptive feature fusion / Neelanjan Bhowmik (2017)  

Permalink

A new climatology of maximum and minimum temperature (1951–2010) in the Spanish mainland: a comparison between three different interpolation methods / D. Peña-Angulo in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

Permalink

A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds / Magnussen, Steen in Remote sensing of environment, vol 184 (October 2016)  

Permalink

Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Permalink