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Urban geospatial information acquisition mobile mapping system based on close-range photogrammetry and IGS site calibration / Ming Guo in Geo-spatial Information Science, vol 24 n° 4 (October 2021)
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Titre : Urban geospatial information acquisition mobile mapping system based on close-range photogrammetry and IGS site calibration Type de document : Article/Communication Auteurs : Ming Guo, Auteur ; Yuquan Zhou, Auteur ; Jianghong Zhao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 558 - 579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes IGN] coordonnées GNSS
[Termes IGN] couplage GNSS-INS
[Termes IGN] données lidar
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] orientation du capteur
[Termes IGN] précision des mesures
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] station GNSS
[Termes IGN] système de numérisation mobile
[Termes IGN] zone urbaineRésumé : (auteur) The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measurement accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relationship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation parameters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision requirements of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as autonomous driving, digital twin city, urban brain et al. Numéro de notice : A2021-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2021.1924084 Date de publication en ligne : 20/08/2021 En ligne : https://doi.org/10.1080/10095020.2021.1924084 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99354
in Geo-spatial Information Science > vol 24 n° 4 (October 2021) . - pp 558 - 579[article]Assessment and prediction of urban growth for a mega-city using CA-Markov model / Veerendra Yadav in Geocarto international, vol 36 n° 17 ([15/09/2021])
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Titre : Assessment and prediction of urban growth for a mega-city using CA-Markov model Type de document : Article/Communication Auteurs : Veerendra Yadav, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2021 Article en page(s) : pp 1960 - 1992 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] coefficient de corrélation
[Termes IGN] croissance urbaine
[Termes IGN] mégalopole
[Termes IGN] modèle de Markov
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMap
[Termes IGN] Tamil Nadu (Inde ; état)
[Termes IGN] urbanisationRésumé : (auteur) Most of World’s mega-cities are facing high population growth. To accommodate the increased population, new built-up areas are emerging at the periphery or fringe area of cities. New urbanisation has an adverse impact on the existing Land Use Land Cover (LULC). To monitor and analyse the impact of urbanisation, LULC change analysis has become the primary concern for LULC monitoring agencies. In this study, LULC change of Chennai has been assessed during 1981–2011 using temporal Landsat data. All the dataset has been classified using Maximum Likelihood Classifier (MLC). Quantitative change in LULC has been carried out using Pearson’s Correlation Coefficient, Transition Potential Matrix, Land Use Dynamic Degree and MLC. Further, spatio-temporal change analysis has been performed using Post-classification comparison technique. Cellular Automata-Markov (CA-Markov) Model used for LULC prediction for 2021–2051. The urban area of Chennai has increased from 40.74 to 103.52 km2 during 1981–2011. Further, LULC prediction using the CA-Markov model shows that the urban area of Chennai district may increase from 103.52 to 140.79 km2 during 2011–2051. During the period 1981–2051, the prediction model indicates that mostly vegetation and barren land will be converted into urban land class. Numéro de notice : A2021-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2019.1690054 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1690054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98507
in Geocarto international > vol 36 n° 17 [15/09/2021] . - pp 1960 - 1992[article]Recurrent-based regression of Sentinel time series for continuous vegetation monitoring / Anatol Garioud in Remote sensing of environment, vol 263 (15 September 2021)
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Titre : Recurrent-based regression of Sentinel time series for continuous vegetation monitoring Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano
, Auteur ; Clément Mallet
, Auteur
Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° 112419 Note générale : bibliographie
This work is funded by the Agence de la transition écologique (ADEME) and the Centre National d'Études Spatiales (CNES).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Dense time series of optical satellite imagery describing vegetation activity provide essential information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal resolution of optical sensors is strongly affected by cloud cover, resulting in significant missing information. The use of complementary acquisitions, such as Synthetic Aperture Radar (SAR) data, opens the door to the development of new multi-sensor methodologies aiming at the reconstruction of missing information. However, the joint exploitation of new radar and optical missions, such as the Sentinel, raises new challenges given the different nature and response of the two data sources. In this work, the SenRVM methodology is proposed as a new multi-sensor approach to regress SAR time series towards Normalized Difference Vegetation Index (NDVI). A deep Recurrent Neural Network architecture which integrates SAR acquisitions and ancillary data is adopted. The regression task permits a continuous optical temporal resolution of 6 days. Multiple experiments are carried out to assess the SenRVM framework by studying two large-scale areas in France. Through an extensive interpretation of the results, SenRVM is evaluated on three main vegetation types (grasslands, crops, and forests). High accurate results (R2 > 0.83 and MAE Numéro de notice : A2021-499 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2021.112419 Date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98004
in Remote sensing of environment > vol 263 (15 September 2021) . - n° 112419[article]The impact of landscape characteristics on the performance of upscaled maps / Peijun Sun in Geocarto international, vol 36 n° 17 ([15/09/2021])
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Titre : The impact of landscape characteristics on the performance of upscaled maps Type de document : Article/Communication Auteurs : Peijun Sun, Auteur ; Russell G. Congalton, Auteur Année de publication : 2021 Article en page(s) : pp 1905 - 1922 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] analyse du paysage
[Termes IGN] carte de base
[Termes IGN] logiciel de simulation
[Termes IGN] mise à l'échelle
[Termes IGN] paysage
[Termes IGN] précision cartographiqueRésumé : (auteur) Upscaled maps, as necessary data sources, have drawn much attention to fill data gaps or match the spatial resolution of pre-existing projects. Nevertheless, it remains a challenging task to quantitatively assess the impact of landscape characteristics on the upscaled maps. To simplify the investigation, three characteristics: fragmentation, number of classes and major class impact factor (MCIF), were selected. We utilized SIMMAP to produce categorical maps for generating base maps with different landscape characteristics. The Majority Rule Based algorithm was then used to produce upscaled maps at 11 different spatial resolutions. The findings indicate that the combined effect of landscape patterns greatly impacts upscaling accuracy. This important result should be carefully considered when developing the next generation of upscaling techniques. Overall, extending our understanding of the impacts of landscape characteristics is a critical step forward improving upscaling accuracy and therefore, our use of these maps. Numéro de notice : A2021-693 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678681 Date de publication en ligne : 18/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678681 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98511
in Geocarto international > vol 36 n° 17 [15/09/2021] . - pp 1905 - 1922[article]3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)
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Titre : 3D map creation using crowdsourced GNSS data Type de document : Article/Communication Auteurs : Terence Lines, Auteur ; Anahid Basiri, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] approche participative
[Termes IGN] Bootstrap (statistique)
[Termes IGN] cartographie 3D
[Termes IGN] données GNSS
[Termes IGN] données localisées 2,5D
[Termes IGN] hauteur du bâti
[Termes IGN] interface de programmation
[Termes IGN] régression logistique
[Termes IGN] signal GNSS
[Termes IGN] trajet multiple
[Termes IGN] vision par ordinateurRésumé : (auteur) 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. Numéro de notice : A2021-535 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101671 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97998
in Computers, Environment and Urban Systems > vol 89 (September 2021)[article]Accuracy estimation of site coordinates derived from GNSS-observations by non-classical error theory of measurements / Petro Dvulit in Geodesy and Geodynamics, vol 12 n° 5 (September 2021)
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PermalinkInfluence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series / Kevin Gobron in Journal of geophysical research : Solid Earth, vol 126 n° 9 (September 2021)
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PermalinkProtection naturelle contre la submersion, apport de l'intelligence artificielle / Antoine Mury in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)
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