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Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
[article]
Titre : Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data Type de document : Article/Communication Auteurs : Yi-Chun Lin, Auteur ; Ayman Habib, Auteur Année de publication : 2022 Article en page(s) : n° 100023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] autoroute
[Termes IGN] couplage GNSS-INS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] lidar mobile
[Termes IGN] pont
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Emerging mobile LiDAR mapping systems exhibit great potential as an alternative for mapping urban environments. Such systems can acquire high-quality, dense point clouds that capture detailed information over an area of interest through efficient field surveys. However, automatically recognizing and semantically segmenting different components from the point clouds with efficiency and high accuracy remains a challenge. Towards this end, this study proposes a semantic segmentation framework to simultaneously classify bridge components and road infrastructure using mobile LiDAR point clouds while providing the following contributions: 1) a deep learning approach exploiting graph convolutions is adopted for point cloud semantic segmentation; 2) cross-labeling and transfer learning techniques are developed to reduce the need for manual annotation; and 3) geometric quality control strategies are proposed to refine the semantic segmentation results. The proposed framework is evaluated using data from two mobile mapping systems along an interstate highway with 27 highway bridges. With the help of the proposed cross-labeling and transfer learning strategies, the deep learning model achieves an overall accuracy of 84% using limited training data. Moreover, the effectiveness of the proposed framework is verified through test covering approximately 42 miles along the interstate highway, where substantial improvement after quality control can be observed. Numéro de notice : A2022-814 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.ophoto.2022.100023 Date de publication en ligne : 24/10/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101975
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100023[article]Testing of a new way of cadastral maps renewal in Slovakia / Peter Kyseľ in Geodetski vestnik, vol 66 n° 4 (December 2022 - February 2023)
[article]
Titre : Testing of a new way of cadastral maps renewal in Slovakia Type de document : Article/Communication Auteurs : Peter Kyseľ, Auteur ; Ľubica Hudecová, Auteur Année de publication : 2022 Article en page(s) : pp 521 - 535 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Cadastre étranger
[Termes IGN] base de données foncières
[Termes IGN] carte numérique
[Termes IGN] cartographie cadastrale
[Termes IGN] données vectorielles
[Termes IGN] SlovaquieRésumé : (auteur) One of the biggest problems in the Slovak cadastre is the quality of maps. Approximately half of them require a new mapping. The quality of the other half is also not high, and they include local shifts. The paper deals with a proposal for a new way of their renewal—Cadastral Operate Renewal by Correction (RbC). This process is based on a transformation of the part of the map with local shifts using a new GNSS measurement. The process has three main stages—homogeneity analysis, transformation, and final control. The result of the RbC process is a renewed cadastral map without any local shifts. Another goal of this paper is to test this process in a chosen cadastral unit and to analyse the results. If the testing is successful, this process could be a fast and cheap alternative to a new mapping in case of these cadastral maps with local shifts. Numéro de notice : A2022-905 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.15292/geodetski-vestnik.2022.04.521-535 Date de publication en ligne : 17/11/2022 En ligne : https://doi.org/10.15292/geodetski-vestnik.2022.04.521-535 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102318
in Geodetski vestnik > vol 66 n° 4 (December 2022 - February 2023) . - pp 521 - 535[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2022041 RAB Revue Centre de documentation En réserve L003 Disponible The simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)
[article]
Titre : The simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Shao Zhengfeng, Auteur ; Andaleeb Yaseen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 783 - 790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] changement climatique
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification par réseau neuronal
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] MNS SRTM
[Termes IGN] modèle de simulation
[Termes IGN] Pakistan
[Termes IGN] planification urbaine
[Termes IGN] température au solRésumé : (auteur) Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an overall Kappa value of 0.82 and 86.2% of correctness for the years 2030 and 2040 using modules for land-use change evaluation. The study also indicates that land surface temperature is an important factor in environmental changes. Therefore, it is suggested that future urban planning should focus on urban rooftop plantations and vegetation conservation to minimize land surface temperature increases in Lahore. Numéro de notice : A2022-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00071R2 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.14358/PERS.22-00071R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102208
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 12 (December 2022) . - pp 783 - 790[article]Updating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia / Medria Shekar Rani in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)
[article]
Titre : Updating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia Type de document : Article/Communication Auteurs : Medria Shekar Rani, Auteur ; Ross Cameron, Auteur ; Olaf Schrott, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2549 - 2562 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] bassin hydrographique
[Termes IGN] carte thématique
[Termes IGN] changement d'occupation du sol
[Termes IGN] Java (île de)
[Termes IGN] mise à jour
[Termes IGN] modèle de Markov
[Termes IGN] modélisation spatiale
[Termes IGN] Perceptron multicoucheRésumé : (auteur) In developing countries, data gaps are common and lead to uncertainties in land cover change analysis. This study demonstrates how to mitigate uncertainties in modeling land change in the Ci Kapundung upper water catchment area by comparing the outcomes of two simulation phases. A conventional cellular automata (CA)–Markov model was complemented with a multilayer perceptron (MLP) to project future land cover maps in the study area. The CA–Markov–MLP model results in high uncertainties in forested sites where a data gap is apparent in the input data (land cover maps and driver variables) and parameters. The results show that the model accuracy is improved from 47.90% in the first phase to 81.36% in the second phase. Both first and second phases integrate six demographic–economic and environmental drivers in the modeling, but the second phase also incorporates an updating and backdating analysis to revise the base-maps. This study suggests that uncertainties can be mitigated by linking such base-map revision process with the updating and backdating analyses using remote sensing datasets retrieved at different times. Numéro de notice : A2022-845 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2103820 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1080/13658816.2022.2103820 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102076
in International journal of geographical information science IJGIS > vol 36 n° 12 (December 2022) . - pp 2549 - 2562[article]Vertical deformation and residual altimeter systematic errors around continental Australia inferred from a Kalman-based approach / Mohammad-Hadi Rezvani in Journal of geodesy, vol 96 n° 12 (December 2022)
[article]
Titre : Vertical deformation and residual altimeter systematic errors around continental Australia inferred from a Kalman-based approach Type de document : Article/Communication Auteurs : Mohammad-Hadi Rezvani, Auteur ; Christopher S. Watson, Auteur ; Matt A. King, Auteur Année de publication : 2022 Article en page(s) : n° 96 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] altimètre
[Termes IGN] Australie occidentale (Australie)
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] données altimétriques
[Termes IGN] données marégraphiques
[Termes IGN] erreur systématique
[Termes IGN] filtre de Kalman
[Termes IGN] montée du niveau de la mer
[Termes IGN] série temporelle
[Termes IGN] variabilitéRésumé : (auteur) We further developed a space–time Kalman approach to investigate time-fixed and time-variable signals in vertical land motion (VLM) and residual altimeter systematic errors around the Australian coast, through combining multi-mission absolute sea-level (ASL), relative sea-level from tide gauges (TGs) and Global Positioning System (GPS) height time series. Our results confirmed coastal subsidence in broad agreement with GPS velocities and unexplained by glacial isostatic adjustment alone. VLM determined at individual TGs differs from spatially interpolated GPS velocities by up to ~ 1.5 mm/year, yielding a ~ 40% reduction in RMSE of geographic ASL variability at TGs around Australia. Our mission-specific altimeter error estimates are small but significant (typically within ~ ± 0.5–1.0 mm/year), with negligible effect on the average ASL rate. Our circum-Australia ASL rate is higher than previous results, suggesting an acceleration in the ~ 27-year time series. Analysis of the time-variability of altimeter errors confirmed stability for most missions except for Jason-2 with an anomaly reaching ~ 2.8 mm/year in the first ~ 3.5 years of operation, supported by analysis from the Bass Strait altimeter validation facility. Data predominantly from the reference missions and located well off narrow shelf regions was shown to bias results by as much as ~ 0.5 mm/year and highlights that residual oceanographic signals remain a fundamental limitation. Incorporating non-reference-mission measurements well on the shelf helped to mitigate this effect. Comparing stacked nonlinear VLM estimates and altimeter systematic errors with the El Niño-Southern Oscillation shows weak correlation and suggests our approach improves the ability to explore nonlinear localized signals and is suitable for other regional- and global-scale studies. Numéro de notice : A2022-897 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01680-3 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01680-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102251
in Journal of geodesy > vol 96 n° 12 (December 2022) . - n° 96[article]Wall-to-wall mapping of forest biomass and wood volume increment in Italy / Francesca Giannetti in Forests, vol 13 n° 12 (December 2022)PermalinkDevelopment and long-term dynamics of old-growth beech-fir forests in the Pyrenees: Evidence from dendroecology and dynamic vegetation modelling / Dario Martín-Benito in Forest ecology and management, vol 524 (November-15 2022)PermalinkVine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)PermalinkAutomatic vectorization of fluvial corridor features on historical maps to assess riverscape changes / Samuel Dunesme in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)PermalinkBeyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)PermalinkExploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkA fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation / Xiaoguo Guan in Survey review, vol 54 n° 387 (November 2022)PermalinkFeatures predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (November-1 2022)PermalinkLessons learned from using historical maps to create a digital gazetteer of historical places / Mark Polczynski in International journal of cartography, vol 8 n° 3 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)Permalink