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From Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 13 - 30 Titre : From Google Maps to a fine-grained catalog of street trees Type de document : Article/Communication Auteurs : Steve Branson, Auteur ; Jan Dirk Wegner, Auteur ; David Hall, Auteur ; Nico Lang, Auteur ; Konrad Schindler, Auteur ; Pietro Perona, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 30 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] arbre urbain [Termes IGN] architecture pipeline (processeur) [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] Google Maps [Termes IGN] inventaire de la végétation [Termes IGN] photo-interprétation assistée par ordinateur [Termes IGN] réseau neuronal convolutif [Termes IGN] ville Résumé : (Auteur) Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect >70% of the street trees, assign correct species to >80% for 40 different species, and correctly detect and classify changes in >90% of the cases. Numéro de notice : A2018-068 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.008 Date de publication en ligne : 20/11/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89426 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018012	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2018013	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis / Xiya Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 93 - 111 Titre : A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis Type de document : Article/Communication Auteurs : Xiya Zhang, Auteur ; Peijun Li, Auteur Année de publication : 2018 Article en page(s) : pp 93 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] éclairage public [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] Pékin (Chine) [Termes IGN] température de l'air [Termes IGN] urbanisation [Termes IGN] zone urbaine Résumé : (Auteur) Accurate and timely information regarding the extent and spatial distribution of urban areas on regional and global scales is crucially important for both scientific and policy-making communities. Stable nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a unique proxy of human settlement and activity, which has been used in the mapping and analysis of urban areas and urbanization dynamics. However, blooming and saturation effects of DMSP/OLS NTL data are two unresolved problems in regional urban area mapping and analysis. This study proposed a new urban index termed the Temperature and Vegetation Adjusted NTL Urban Index (TVANUI). It is intended to reduce blooming and saturation effects and to enhance urban features by combining DMSP/OLS NTL data with Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra satellite. The proposed index was evaluated in two study areas by comparison with established urban indices. The results demonstrated the proposed TVANUI was effective in enhancing the variation of DMSP/OLS light in urban areas and in reducing blooming and saturation effects, showing better performance than three established urban indices. The TVANUI also significantly outperformed the established urban indices in urban area mapping using both the global-fixed threshold and the local-optimal threshold methods. Thus, the proposed TVANUI provides a useful variable for urban area mapping and analysis on regional scale, as well as for urbanization dynamics using time-series DMSP/OLS and related satellite data. Numéro de notice : A2018-069 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89427 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018012	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2018013	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 173 - 188 Titre : A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning Type de document : Article/Communication Auteurs : Rasmus M. Houborg, Auteur ; Matthew F. McCabe, Auteur Année de publication : 2018 Article en page(s) : pp 173 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image RapidEye [Termes IGN] Leaf Area Index [Termes IGN] réflectance de surface [Termes IGN] régression Résumé : (Auteur) With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes. Numéro de notice : A2018-070 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89428 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018012	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2018013	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt