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Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network / Da He in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102667 Titre : Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network Type de document : Article/Communication Auteurs : Da He, Auteur ; Qian Shi, Auteur ; Xiaoping Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102667 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse infrapixellaire [Termes IGN] apprentissage profond [Termes IGN] arbre hors forêt [Termes IGN] arbre urbain [Termes IGN] base de données localisées [Termes IGN] Chine [Termes IGN] image Sentinel-MSI [Termes IGN] métropole [Termes IGN] Pékin (Chine) [Termes IGN] prise en compte du contexte [Termes IGN] Wuhan (Chine) Résumé : (auteur) Contrast to the global forest, few trees live in cities but contribute significantly to urban environment and human health. However, the classical satellite-derived land cover/forest cover products with limited resolution are not fine enough for the identification of urban tree, which is usually appeared in small size and intersected with infrastructure. To relieve the dilemma, this study developed an urban tree specific sub-pixel mapping (SPM) architecture with deep learning approach, which aimed to generate 2m fine-scale urban tree cover product from 10 m Sentinel-2 images for large-scale area of 34 metropolises in China. The proposed approach has remarkable reconstruction ability for delineating the contextual characteristic of the urban tree patterns, and reliable generalization ability to large-scale area. In addition, this study creates a large-volume urban tree cover dataset (UTCD) with 0.13 billion urban tree samples at 2 m resolution, which fills the deficiency of standard dataset in urban tree cover research field. Quantitative analysis of our products was conducted on two typical study sites of Beijing and Wuhan. The results show that our products recover averagely more than 58.72% of urban tree covers that have been underestimated in the existing land cover/forest cover products, and outperforms the state-of-the-art approach both visually and quantitatively, by averagely 11.31% improvement in overall accuracy. From our annual products during 2016–2020, we found an evolution characteristic of urban tree cover: it is more stable in developed cities like Beijing, while more fluctuated in developing cities like Wuhan, and the alteration are usually concentrated at the outer-ring of downtown, which may be caused by the municipal planning and the land development of real estate industry. Numéro de notice : A2022-073 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102667 En ligne : https://doi.org/10.1016/j.jag.2021.102667 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99438 [article]

Mapping global flying aircraft activities using Landsat 8 and cloud computing / Fen Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 184 (February 2022) . - pp 19 - 30 Titre : Mapping global flying aircraft activities using Landsat 8 and cloud computing Type de document : Article/Communication Auteurs : Fen Zhao, Auteur ; Lang Xia, Auteur ; Arve Kylling, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] aviation civile [Termes IGN] cartographie thématique [Termes IGN] climat [Termes IGN] détection d'objet [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-OLI [Termes IGN] informatique en nuage [Termes IGN] navigation aérienne [Termes IGN] trafic aérien [Termes IGN] transport aérien Résumé : (auteur) Satellite-based remote sensing might provide a potential way for monitoring the global flight activities and their environment impacts, while the remote sensing community pays less attention on it. In this study, we presented a flying aircraft detection algorithm which effectively handles the noise on Landsat 8 OLI cirrus band caused by energetic particles in the South Atlantic Anomaly region, and a new framework based on cloud infrastructure was proposed to map global flying aircraft activities from 2013 to 2020 using Landsat 8 Operational Land Imager (OLI) data. Validation was performed for 254 scenes recorded for various cloudy and surface conditions and vapor contents. The overall percentages of false alarms and omissions for these validation images were 5.37% and 7.80%, respectively. Limited to the resolution of Landsat data, cloud, the size and flight altitude of the aircraft, 42.99% flying aircraft were undetected compared with the FlightRadar24. Instead of using the Google Earth Engine, we employed more flexible cloud computing techniques, Google Cloud Storage and Google Calculation Engine, to construct our framework for the larger volume data. A total of 1.94 million Landsat images were analyzed to obtain the activities maps, and the results showed that globally flying aircraft increased by 25.85% from 2014 to 2019 (the year 2013 was excluded for the low coverage of Landsat scenes), with an annual rate of 4.31%. In 2020, flying aircraft were reduced by 40% compared with 2019 due to the influence of COVID-19 and traveling restrictions, and Europe was the most severely affected by COVID-19, with an 84.59% decline of flying aircraft in April 2020. This study provides a unique long-term supplement to monitor aviation activities and their climate impact. Numéro de notice : A2022-090 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.12.003 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.12.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99506 [article]

Architecture for semantic web service composition in spatial data infrastructures / Deniztan Ulutaş Karakol in Survey review, vol 54 n° 382 (January 2022)  

Public [article]  inSurvey review > vol 54 n° 382 (January 2022) . - pp 1 - 16 Titre : Architecture for semantic web service composition in spatial data infrastructures Type de document : Article/Communication Auteurs : Deniztan Ulutaş Karakol, Auteur ; Cetin Cömert, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] accès aux données localisées [Termes IGN] conception orientée utilisateur [Termes IGN] langage naturel (informatique) [Termes IGN] ontologie [Termes IGN] OWL [Termes IGN] web sémantique Résumé : (auteur) The importance of geospatial data has rendered it to be used in decision-making in both public and private sectors. The purpose of this study was to employ Semantic Web Technology (SWT) for the problems of Web Service Composition (WSC) in the context of Spatial Data Infrastructures (SDI). Some of these problems are identifying the workflow sequence and the user goal, discovering services according to service parameters, and matching these parameters. As a suggestion for the solution of all these problems a semi-automated WSC architecture was proposed in this study. In terms of architecture, users state their ‘goal’ with a natural language sentence. By semantically matching this sentence with a Spatial Services Ontology (SSO), the corresponding ‘abstract’ WSC was ‘located’ and the ‘concrete’ WSC was formed. Although there are still problems waiting to be solved due to the scope of the work, this study makes a valuable contribution to the area. Numéro de notice : A2022-110 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1858255 Date de publication en ligne : 25/12/2020 En ligne : https://doi.org/10.1080/00396265.2020.1858255 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99627 [article]

Improving the (re-)convergence of multi-GNSS real-time precise point positioning through regional between-satellite single-differenced ionospheric augmentation / Ahao Wang in GPS solutions, vol 26 n° 2 (April 2022)  

Public [article]  inGPS solutions > vol 26 n° 2 (April 2022) . - n° 39 Titre : Improving the (re-)convergence of multi-GNSS real-time precise point positioning through regional between-satellite single-differenced ionospheric augmentation Type de document : Article/Communication Auteurs : Ahao Wang, Auteur ; Yize Zhang, Auteur ; Junping Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] correction ionosphérique [Termes IGN] modèle ionosphérique [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement ponctuel précis [Termes IGN] temps de convergence [Termes IGN] temps réel [Termes IGN] teneur verticale totale en électrons [Termes IGN] transformation polynomiale Résumé : (auteur) The long (re-)convergence time seriously limits many applications of real-time precise point positioning (RTPPP) in challenging environments like urban vehicle navigation and hazards monitoring. Thus, we proposed a real-time fast-positioning model by introducing the regional between-satellite single-differenced (SD) ionospheric constraints into the undifferenced and uncombined PPP (UU-PPP). The line-of-sight ionospheric observables are extracted by the multi-GNSS (GPS + Galileo) UU-PPP method. The polynomial function with simple structure and high efficiency is applied to establish the real-time regional between-satellite SD ionospheric vertical total electron content (VTEC) model. The differential slant total electron content (dSTEC) variations retrieved from three VTEC models are validated with the between-satellite SD and epoch-differenced geometry-free combinations of dual-frequency phase observations. The average RMS values are 0.77, 0.78 and 0.47 TEC unit for the CLK93 real-time VTEC, CODE final GIM and regional between-satellite SD ionospheric VTEC model, respectively. In the positioning domain, the data of ten stations for 12 consecutive days in 2020 were used for implementing kinematic RTPPP with single-frequency (SF) and dual-frequency (DF) observations. Compared with the GPS + Galileo SF-RTPPP based on the GRoup And PHase Ionospheric Correction model, the initialization time of the SD ionospheric-constrained (SDIC) SF-RTPPP when converged to 0.2 m at the 68% confidence level can be improved from 58 to 32 min in horizontal and 72 to 49 min in vertical, and its positioning accuracy can be improved by 29.7 and 20.3% in the horizontal and vertical components, respectively. Meanwhile, the re-convergence errors of SDIC SF-RTPPP from the first epoch can be maintained at 0.15 m in three components. As to GPS + Galileo SDIC DF-RTPPP, the re-convergence time when converged to 0.1 m can be lower than 3 min in horizontal and 9 min in vertical, and the re-convergence errors at the first epoch could even be lower than 0.15 m in horizontal. Hence, the new positioning model can maintain high accuracy and improve the continuity of real-time kinematic positioning in a short time when the number of tracked satellites in the urban or canyon environment was greatly dropped due to signal blocking. Numéro de notice : A2022-107 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01229-z Date de publication en ligne : 21/02/2022 En ligne : https://doi.org/10.1007/s10291-022-01229-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99608 [article]

3D modeling of urban area based on oblique UAS images—An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January 2 2022)  

Public [article]  inRemote sensing > vol 14 n° 2 (January 2 2022) . - n° 422 Titre : 3D modeling of urban area based on oblique UAS images—An end-to-end pipeline Type de document : Article/Communication Auteurs : Valeria-Ersilia Oniga, Auteur ; Ana-Ioana Breaban, Auteur ; Norbert Pfeifer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 422 Note générale : biliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] Bâti-3D [Termes IGN] CityGML [Termes IGN] classification par forêts aléatoires [Termes IGN] données lidar [Termes IGN] image aérienne oblique [Termes IGN] image captée par drone [Termes IGN] indice de végétation [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] Roumanie [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] zone urbaine Résumé : (auteur) 3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Numéro de notice : A2022-101 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14020422 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.3390/rs14020422 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99566 [article]

Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January 2 2022)  

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Self-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (January 2022)  

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Fast local adaptive multiscale image matching algorithm for remote sensing image correlation / Niccolò Dematteis in Computers & geosciences, vol 159 (February 2022)  

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

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MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images / Majedaldein Almahasneh in Machine Vision and Applications, vol 33 n° 1 (January 2022)  

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Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata / Aditya Tafta Nugraha in Computers, Environment and Urban Systems, vol 92 (March 2022)  

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Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)  

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Classification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)  

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Detection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks / Stefan Reder in Remote sensing, vol 14 n° 1 (January-1 2022)  

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A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)  

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