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Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])
[article]
Titre : Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal Type de document : Article/Communication Auteurs : Santa Pandit, Auteur ; Satoshi Tsuyuki, Auteur ; Timothy Dube, Auteur Année de publication : 2020 Article en page(s) : pp 1832 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse multibande
[Termes IGN] analyse texturale
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] forêt
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] NépalRésumé : (auteur) The potential of the improved resolution Sentinel-2 MSI data was explored through texture metrics, vegetation indices (VIs) and pooled dataset using the Random Forest (RF) machine learning algorithm to estimate Above-ground Biomass (AGB) in a sub-tropical forest of Nepal. Texture metrics were derived based on different working window sizes (3 × 3, 5 × 5, 7 × 7 and 9 × 9), and the results were compared with those obtained, using raw traditional bands (Analysis set 1: 2, 3, 4, 8, 11 and 12), raw traditional and red edge bands (Analysis set 2: Set 1 + Band 5, 6, 7 and 8A), and red edge bands (Analysis set 3) only. Comparatively, the use of pooled data (texture and VIs) yielded higher biomass estimates. The results from pooled data based on the 7 × 7 window size resulted in models with better model fitting parameters. For instance, pooled data produced an R2 = 0.99 and a RMSE = 4.51 t ha−1 (relRMSE = 2.82). Further, the RF model selected dissimilarity, variance and mean from Band 2 and SAVI (Soil adjusted vegetation index) as the most important AGB predictor variables. The results demonstrated that like the red-edge bands, traditional bands were equally important in AGB estimation. Numéro de notice : A2020-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1588390 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1588390 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96334
in Geocarto international > vol 35 n° 16 [01/12/2020] . - pp 1832 - 1849[article]Learning from urban form to predict building heights / Nikola Milojevic-Dupont in Plos one, vol 15 n° 12 (December 2020)
[article]
Titre : Learning from urban form to predict building heights Type de document : Article/Communication Auteurs : Nikola Milojevic-Dupont, Auteur ; Nicolai Hans, Auteur ; Lynn H. Kaack, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0242010 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] base de connaissances
[Termes IGN] données localisées des bénévoles
[Termes IGN] France (administrative)
[Termes IGN] hauteur du bâti
[Termes IGN] Italie
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] Pays-Bas
[Termes IGN] villeRésumé : (auteur) Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change. Numéro de notice : A2020-830 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1371/journal.pone.0242010 Date de publication en ligne : 09/12/2020 En ligne : https://doi.org/10.1371/journal.pone.0242010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97658
in Plos one > vol 15 n° 12 (December 2020) . - n° 0242010[article]Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks / Felix Schiefer in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
[article]
Titre : Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks Type de document : Article/Communication Auteurs : Felix Schiefer, Auteur ; Teja Kattenborn, Auteur ; Annett Frick, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 205-215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] arbre (flore)
[Termes IGN] carte forestière
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espèce végétale
[Termes IGN] Forêt-Noire, massif de la
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] segmentation sémantique
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution ( Numéro de notice : A2020-706 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.015 Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.015 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96236
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 205-215[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020121 RAB Revue Centre de documentation En réserve L003 Disponible MS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds / Haifeng Luo in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
[article]
Titre : MS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds Type de document : Article/Communication Auteurs : Haifeng Luo, Auteur ; Chongcheng Chen, Auteur ; Lina Fang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8301 - 8315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] cognition
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] représentation multiple
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Semantic segmentation is one of the fundamental tasks in understanding and applying urban scene point clouds. Recently, deep learning has been introduced to the field of point cloud processing. However, compared to images that are characterized by their regular data structure, a point cloud is a set of unordered points, which makes semantic segmentation a challenge. Consequently, the existing deep learning methods for semantic segmentation of point cloud achieve less success than those applied to images. In this article, we propose a novel method for urban scene point cloud semantic segmentation using deep learning. First, we use homogeneous supervoxels to reorganize raw point clouds to effectively reduce the computational complexity and improve the nonuniform distribution. Then, we use supervoxels as basic processing units, which can further expand receptive fields to obtain more descriptive contexts. Next, a sparse autoencoder (SAE) is presented for feature embedding representations of the supervoxels. Subsequently, we propose a regional relation feature reasoning module (RRFRM) inspired by relation reasoning network and design a multiscale regional relation feature segmentation network (MS-RRFSegNet) based on the RRFRM to semantically label supervoxels. Finally, the supervoxel-level inferences are transformed into point-level fine-grained predictions. The proposed framework is evaluated in two open benchmarks (Paris-Lille-3D and Semantic3D). The evaluation results show that the proposed method achieves competitive overall performance and outperforms other related approaches in several object categories. An implementation of our method is available at: https://github.com/HiphonL/MS_RRFSegNet . Numéro de notice : A2020-738 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2985695 Date de publication en ligne : 28/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2985695 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96363
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8301 - 8315[article]Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
[article]
Titre : Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data Type de document : Article/Communication Auteurs : Christian Geiss, Auteur ; Henrik Schrade, Auteur ; Patrick Aravena Pelizari, Auteur ; Hannes Taubenböck, Auteur Année de publication : 2020 Article en page(s) : pp 57-71 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] hauteur du bâti
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image TanDEM-X
[Termes IGN] modèle de régression
[Termes IGN] morphologie urbaine
[Termes IGN] pondération
[Termes IGN] processus gaussien
[Termes IGN] zone urbaine denseRésumé : (Auteur) In this paper, we establish a workflow for estimation of built-up density and height based on multispectral Sentinel-2 data. To do so, we render the estimation of built-up density and height as a supervised learning problem. Given the rational level of measurement of those two target variables, the regression estimation problem is regarded as finding the mapping between an incoming vector, i.e., ubiquitously available features computed from Sentinel-2 data, and an observable output (i.e., training set), which is derived over spatially limited areas in an automated manner. As such, training sets are automatically generated from a joint exploitation of TanDEM-X mission elevation data and Sentinel-2 imagery, and, as an alternative, from cadastral sources. The training sets are used to regress the target variables for spatial processing units which correspond to urban neighborhood scales. From a methodological point of view, we introduce a novel ensemble regression approach, i.e., multistrategy ensemble regression (MSER), based on advanced machine learning-based regression algorithms including Random Forest Regression, Support Vector Regression, Gaussian Process Regression, and Neural Network Regression. To establish a robust ensemble, those algorithms are learned with a modified version of the AdaBoost.RT algorithm. However, to reliably ensure diversity between single boosted regressors, we include a random feature subspace method in the procedure. In contrast to existing approaches, we selectively prune non-favorable regressors trained during the boosting procedure and calculate the final prediction by a weighted mean function on the residual models to ensure enhanced accuracy properties of predictions. Finally, outputs are concatenated into a single prediction with a decision fusion strategy. Experimental results are obtained from four test areas which cover the settlement areas of the four largest German cites, i.e., Berlin, Hamburg, Munich, and Cologne. The results unambiguously underline the beneficial properties of the MSER approach, since all best predictions were obtained with a boosted regressor in conjunction with a decision fusion strategy in a comparative setup. The mean absolute errors of corresponding models vary between 3 and 16% and 1–5.4 m with respect to built-up density and height, respectively, depending on the validation strategy, size of the spatial processing units, and test area. Also in a domain adaptation setup (i.e., when learning a model over a source domain and applying it over a geographically different target domain) numerous predictions show comparable accuracy levels as predictions obtained within a source domain. This further underlines the viability to transfer a model and, thus, enable a substitution of the training data in the target domains. Numéro de notice : A2020-704 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.004 Date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96231
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 57-71[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020121 RAB Revue Centre de documentation En réserve L003 Disponible Nonlocal graph convolutional networks for hyperspectral image classification / Lichao Mou in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkA novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)PermalinkParsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkSemantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkSemi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree / Shuang Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkSTME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkUnderstanding the role of individual units in a deep neural network / David Bau in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 48 (1 December 2020)PermalinkUnderstanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkUnsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkUsing multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks / Egor Smirrnov in Geo-spatial Information Science, vol 23 n° 4 (December 2020)PermalinkForêt d'arbres aléatoires et classification d'images satellites : relation entre la précision du modèle d'entraînement et la précision globale de la classification / Aurélien N.G. Matsaguim in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)PermalinkActive and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkBayesian-deep-learning estimation of earthquake location from single-station observations / S. Mostafa Mousavi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkBayesian transfer learning for object detection in optical remote sensing images / Changsheng Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkA deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkEvaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria / Johannes Scholz in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkHigh-resolution remote sensing image scene classification via key filter bank based on convolutional neural network / Fengpeng Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkLandslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkLearning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)PermalinkOptimizing local geoid undulation model using GPS/levelling measurements and heuristic regression approaches / Mosbeh R. Kaloop in Survey review, vol 52 n° 375 (November 2020)Permalink