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Unifying remote sensing image retrieval and classification with robust fine-tuning / Dimitri Gominski (2021)
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Titre : Unifying remote sensing image retrieval and classification with robust fine-tuning Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Valérie Gouet-Brunet
, Auteur ; Liming Chen, Auteur
Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Importance : 7 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image numérique
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image aérienne à axe vertical
[Termes IGN] image aérienne oblique
[Termes IGN] jeu de données
[Termes IGN] précision de la classification
[Termes IGN] recherche d'image basée sur le contenuRésumé : (auteur) Advances in high resolution remote sensing image analysisare currently hampered by the difficulty of gathering enoughannotated data for training deep learning methods, giving riseto a variety of small datasets and associated dataset-specificmethods. Moreover, typical tasks such as classification andretrieval lack a systematic evaluation on standard benchmarksand training datasets, which make it hard to identify durableand generalizable scientific contributions. We aim at uni-fying remote sensing image retrieval and classification witha new large-scale training and testing dataset, SF3001, in-cluding both vertical and oblique aerial images and madeavailable to the research community, and an associated fine-tuning method. We additionally propose a new adversarialfine-tuning method for global descriptors. We show that ourframework systematically achieves a boost of retrievalandclassification performance on nine different datasets com-pared to an ImageNet pretrained baseline, with currently noother method to compare to. Numéro de notice : P2021-003 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2102.13392 En ligne : https://doi.org/10.48550/arXiv.2102.13392 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97283 Using geometric and semantic attributes for semi-automated tag identification in OpenStreetMap data / Müslüm Hacar (2021)
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Titre : Using geometric and semantic attributes for semi-automated tag identification in OpenStreetMap data Type de document : Article/Communication Auteurs : Müslüm Hacar, Auteur Editeur : Cardiff [Royaume-Uni] : Cardiff University Année de publication : 2021 Conférence : GISRUK 2021, 29th GIS research UK annual conference 14/04/2021 16/04/2021 Cardiff online Royaume-Uni OA Proceedings Importance : 6 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Ankara (Turquie)
[Termes IGN] attribut géomètrique
[Termes IGN] attribut sémantique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] loisir
[Termes IGN] OpenStreetMap
[Termes IGN] traitement de données localiséesRésumé : (auteur) OpenStreetMap is one of the successful volunteered geographic al information projects. Participants contribute to this crowdsourced project by adding geometric and semantic data. However, both missing geometric and semantic data still cause complete ness problems. In this paper, a semi-automated approach is suggested to identify the values of leisure tag of polygon features. The approach uses geometric (rectangularity, density, area, and distances to bus stop and shop) and semantic (amenity) data and estimates the key values using random forest classifier. In short, the results show that tag identification was conducted in three districts of Ankara with f - score s 78%, 86%, and 87%. Numéro de notice : C2021-082 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication DOI : 10.5281/zenodo.4665518 Date de publication en ligne : 06/04/2021 En ligne : https://doi.org/10.5281/zenodo.4665518 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101043 Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements / Mikko Kukkonen in Silva fennica, vol 55 n° 1 (January 2021)
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Titre : Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements Type de document : Article/Communication Auteurs : Mikko Kukkonen, Auteur ; Eetu Kotivuori, Auteur ; Matti Maltamo, Auteur ; Lauri Korhonen, Auteur ; Petteri Packalen, Auteur Année de publication : 2021 Article en page(s) : n° 10360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification barycentrique
[Termes IGN] données de terrain
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] image captée par drone
[Termes IGN] image Sentinel-MSI
[Termes IGN] inventaire forestier local
[Termes IGN] modèle de simulation
[Termes IGN] régression
[Termes IGN] semis de points
[Termes IGN] volume en boisRésumé : (auteur) Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available. Numéro de notice : A2021-738 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10360 En ligne : https://doi.org/10.14214/sf.10360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98703
in Silva fennica > vol 55 n° 1 (January 2021) . - n° 10360[article]CNN-based tree species classification using high resolution RGB image data from automated UAV observations / Sebastian Egli in Remote sensing, vol 12 n° 23 (December-2 2020)
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Titre : CNN-based tree species classification using high resolution RGB image data from automated UAV observations Type de document : Article/Communication Auteurs : Sebastian Egli, Auteur ; Martin Höpke, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre (flore)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] espèce végétale
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] phénologieRésumé : (auteur) Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from aerial surveys is still not sufficiently developed to achieve reliable results independent of the phenological stage, time of day, season, tree vitality and prevailing atmospheric conditions. Here, we introduce a novel tree species classification approach based on high resolution RGB image data gathered during automated UAV flights that overcomes these insufficiencies. For the classification task, a computationally lightweight convolutional neural network (CNN) was designed. We show that with the chosen CNN model architecture, average classification accuracies of 92% can be reached independently of the illumination conditions and the phenological stages of four different tree species. We also show that a minimal ground sampling density of 1.6 cm/px is needed for the classification model to be able to make use of the spatial-structural information in the data. Finally, to demonstrate the applicability of the presented approach to derive spatially explicit tree species information, a gridded product is generated that yields an average classification accuracy of 88%. Numéro de notice : A2020-820 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12233892 Date de publication en ligne : 27/11/2020 En ligne : https://doi.org/10.3390/rs12233892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97239
in Remote sensing > vol 12 n° 23 (December-2 2020)[article]Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)
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Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] cartographie géomorphologique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] effondrement de terrain
[Termes IGN] figuré linéaire
[Termes IGN] indice de risque
[Termes IGN] inventaire
[Termes IGN] Lombardie
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modèle statistique
[Termes IGN] régression logistiqueRésumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244
in Applied geomatics > vol 12 n° 4 (December 2020) . - 23 p.[article]Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
PermalinkBioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates / Robert E. Keane in Forest ecology and management, vol 477 ([01/12/2020])
PermalinkConvolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery / Teja Kattenborn in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)
PermalinkA deep learning approach to improve the retrieval of temperature and humidity profiles from a ground-based microwave radiometer / Xing Yan in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
PermalinkDeep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
PermalinkExploring 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])
PermalinkA framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)
PermalinkMapping 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)
PermalinkMapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles / Ned Horning in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)
PermalinkMultistrategy 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)
PermalinkNonlocal 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)
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)
PermalinkThe effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran / Hojatolah Ganjkhanlo in Geocarto international, vol 35 n° 16 ([01/12/2020])
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)
PermalinkVisualization of 3D property data and assessment of the impact of rendering attributes / Stefan Seipel in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)
PermalinkAnalyse de la déforestation dans la périphérie ouest de la réserve de biosphère du Dja au Cameroun, à partir d'une série multi-annuelle d'images Landsat / Eric Wilson Tegno Nguekam in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)
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