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Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2406 - 2423 Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] point de repère [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] saillance [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] ville Résumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559 [article]

Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 (December 2019)  

Public [article]  inGeocarto international > vol 34 n° 12 (December 2019) . - pp 1363 - 1383 Titre : Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador Type de document : Article/Communication Auteurs : Nguyen-Thanh Son, Auteur ; Chi-Farn Chen, Auteur ; Cheng-Ru Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1363 - 1383 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] climat tropical [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] Improved Vegetation Dryness Index [Termes descripteurs IGN] saison [Termes descripteurs IGN] Salvador [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] température au sol Résumé : (Auteur) This study aims to develop an approach to characterize cropland drought conditions in El Salvador, Central America. The data were processed for 2016–2017 through three main steps: (1) reconstructing MODIS land-surface temperature (LST), (2) Landsat-MODIS data fusion and (3) drought delineation using the temperature vegetation dryness index (TVDI). The results of LST reconstruction using the random forests (RF) indicated the median RMSE value of 0.5 °C. The fusion results achieved from the STARFM compared with the reference Landsat data revealed close agreement with the correlation coefficient (r) values higher than 0.84. The TVDI results verified with that from the reference Landsat data indicated r values of 0.85 and 0.75 for 2016 and 2017, respectively. The larger very dry area was observed for the 2016 primera season due to prolonged droughts. Approximately 11.5% and 10.7% of croplands were, respectively, associated with very dry moisture condition in the 2016 and 2017 primera seasons. Numéro de notice : A2019-466 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1489421 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1489421 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93619 [article]

Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 108 - 123 Titre : Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits Type de document : Article/Communication Auteurs : Tawanda W. Gara, Auteur ; Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 108 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf mass per area [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV  0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services. Numéro de notice : A2019-493 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.005 date de publication en ligne : 11/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93725 [article]

Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes / Antonio Tomás Mozas-Calvache in International journal of geographical information science IJGIS, vol 33 n°11 (November 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°11 (November 2019) . - pp 2170 - 2187 Titre : Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes Type de document : Article/Communication Auteurs : Antonio Tomás Mozas-Calvache, Auteur ; Francisco Javier Ariza-López, Auteur Année de publication : 2019 Article en page(s) : pp 2170 - 2187 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes descripteurs IGN] axe médian [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] trace GPS [Termes descripteurs IGN] vitesse de déplacement Résumé : (auteur) The sharing of Global Navigation Satellite System (GNSS) tracks on the Internet is increasing enormously. Every day a great number of users capture routes using different devices and share these data. Individually these tracks present a poor positional accuracy because these devices obtain positions with accuracy of about 5-10 metres. In addition, they are usually captured for navigation and not for surveying. However, we can take advantage of the great quantity of tracks of the same linear element in order to obtain a more accurate solution. This study analyses this possibility using a wide set of tracks obtained in known conditions. We emulated those tracks obtained by Volunteered Geographic Information (VGI) users and we compared the mean axis obtained using all tracks with others obtained from a more accurate source. Additionally, we analyse the displacement of other axes obtained by varying several parameters such as the number of tracks and their length or by dividing the route into sections in function of sinuosity, etc. The results have shown an improved 3D mean axis and the viability of the method proposed in this study in order to use axes obtained from several tracks in maps at certain scales. Numéro de notice : A2019-429 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1645335 date de publication en ligne : 26/07/2019 En ligne : https://doi.org/10.1080/13658816.2019.1645335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93563 [article]

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 155 - 170 Titre : Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images Type de document : Article/Communication Auteurs : Cheolhee Yoo, Auteur ; Daehyeon Han, Auteur ; Jungho Im, Auteur ; Benjamin Bechtel, Auteur Année de publication : 2019 Article en page(s) : pp 155 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chicago (Illinois) [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Madrid (Espagne) [Termes descripteurs IGN] Rome [Termes descripteurs IGN] World Urban Database and Access Portal Tools [Termes descripteurs IGN] zone urbaine dense Résumé : (Auteur) The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning. Numéro de notice : A2019-495 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.009 date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93728 [article]

Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 (November 2019)  

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Land-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images / Temulun Tangud in Geocarto international, vol 34 n° 11 (November 2019)  

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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 (October 2019)  

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Introducing a vertical land motion model for improving estimates of sea level rates derived from tide gauge records affected by earthquakes / Anna Klos in GPS solutions, vol 23 n° 4 (October 2019)  

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A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Mapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Optimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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