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Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)
[article]
Titre : Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Zhengdong Huang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bati
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion de trafic
[Termes IGN] graphe
[Termes IGN] logement
[Termes IGN] migration pendulaire
[Termes IGN] modèle de simulation
[Termes IGN] régression géographiquement pondérée
[Termes IGN] service public
[Termes IGN] Shenzhen
[Termes IGN] système de transport intelligent
[Termes IGN] transport public
[Termes IGN] transport urbainRésumé : (auteur) Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation. Numéro de notice : A2022-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101776 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100185
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776[article]Management or climate and which one has the greatest impact on forest soil’s protective value? A case study in Romanian mountains / Cosmin Cosofret in Forests, vol 13 n° 6 (June 2022)
[article]
Titre : Management or climate and which one has the greatest impact on forest soil’s protective value? A case study in Romanian mountains Type de document : Article/Communication Auteurs : Cosmin Cosofret, Auteur ; Gabriel Duduman, Auteur ; Ionut Barnoaiea, Auteur ; Olivier Bouriaud , Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : n° 916 Note générale : bibliographie
C.C. acknowledges funding from the European H2020 Grant 817903 EFFECT and G.D., I.B. and O.B. acknowledge funding from the Ministry of Research, Innovation and Digitalization within Program 1—Development of national research and development system, Subprogram 1.2—Institutional Performance—RDI excellence funding projects, under contract no. 10PFE/2021.Langues : Anglais (eng) Descripteur : [Termes IGN] altitude
[Termes IGN] analyse comparative
[Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] gestion forestière
[Termes IGN] modèle de simulation
[Termes IGN] modélisation de la forêt
[Termes IGN] protection des forêts
[Termes IGN] protection des sols
[Termes IGN] Roumanie
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The protective value of forests is expected to be affected by climate change. Applied forest management could absorb or enhance such an impact. In this context, we developed a new protective value index (PVI) that includes biometric and topographical indicators of forest stands. Using PVI and the LandClim model, we simulated 100 years with low- and high-intensity cuttings within three climate scenarios to analyze their influence on the protective value of forests included in the soil protection category. The management types had a low impact on PVI during the simulation period. However, the effects of moderate climate intensified in the second half of the simulation period. In contrast, the extreme climate had the highest impact on PVI and its variables throughout the whole period. The forest stands from lower elevation reached a higher protective value than intermediate and high elevation. Although the low-elevation forest stands are the most vulnerable to climate changes, the ongoing adaptation conducts to stands with higher protective value than stable forests from the higher elevation. The PVI is easily adaptable for different forest landscape models and can be widely applied to provide an integrated assessment of the forest protective value and the management measures to maintain or enhance it Numéro de notice : A2022-489 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f13060916 Date de publication en ligne : 12/06/2022 En ligne : https://doi.org/10.3390/f13060916 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100964
in Forests > vol 13 n° 6 (June 2022) . - n° 916[article]Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data Type de document : Article/Communication Auteurs : Lukas Winiwarter, Auteur ; Katharina Anders, Auteur ; Daniel Schröder, Auteur ; Bernhard Höfle, Auteur Année de publication : 2022 Article en page(s) : pp 79 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtre de Kalman
[Termes IGN] modèle de simulation
[Termes IGN] scène 3D
[Termes IGN] scène virtuelle
[Termes IGN] semis de points
[Termes IGN] Tyrol (Autriche)Résumé : (autuer) Virtual laser scanning (VLS) allows the generation of realistic point cloud data at a fraction of the costs required for real acquisitions. It also allows carrying out experiments that would not be feasible or even impossible in the real world, e.g., due to time constraints or when hardware does not exist. A critical part of a simulation is an adequate substitution of reality. In the case of VLS, this concerns the scanner, the laser-object interaction, and the scene. In this contribution, we present a method to recreate a realistic dynamic scene, where the surface changes over time. We first apply change detection and quantification on a real dataset of an erosion-affected high-mountain slope in Tyrol, Austria, acquired with permanent terrestrial laser scanning (TLS). Then, we model and extract the time series of a single change form, and transfer it to a virtual model scene. The benefit of such a transfer is that no physical modelling of the change processes is required. In our example, we use a Kalman filter with subsequent clustering to extract a set of erosion rills from a time series of high-resolution TLS data. The change magnitudes quantified at the locations of these rills are then transferred to a triangular mesh, representing the virtual scene. Subsequently, we apply VLS to investigate the detectability of such erosion rills from airborne laser scanning at multiple subsequent points in time. This enables us to test if, e.g., a certain flying altitude is appropriate in a disaster response setting for the detection of areas exposed to immediate danger. To ensure a successful transfer, the spatial resolution and the accuracy of the input dataset are much higher than the accuracy and resolution that are being simulated. Furthermore, the investigated change form is detected as significant in the input data. We, therefore, conclude the model of the dynamic scene derived from real TLS data to be an appropriate substitution for reality. Numéro de notice : A2022-437 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-79-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100746
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 79 - 86[article]Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)
[article]
Titre : Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning Type de document : Article/Communication Auteurs : Mingge Yu, Auteur ; Xiaoping Rui, Auteur ; Weiyi Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2446 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] échantillonnage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] modèle de simulation
[Termes IGN] surface cultivée
[Termes IGN] terrasseRésumé : (auteur) Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries. Numéro de notice : A2022-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14102446 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.3390/rs14102446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100705
in Remote sensing > vol 14 n° 10 (May-2 2022) . - n° 2446[article]Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns / Damilola Eyelade in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)
[article]
Titre : Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns Type de document : Article/Communication Auteurs : Damilola Eyelade, Auteur ; Keith C. Clarke, Auteur ; Ighodalo Ijagbone, Auteur Année de publication : 2022 Article en page(s) : pp 1037 - 1058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] changement d'utilisation du sol
[Termes IGN] croissance urbaine
[Termes IGN] dalle
[Termes IGN] données spatiotemporelles
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatiale
[Termes IGN] Nigéria
[Termes IGN] OpenStreetMapRésumé : (auteur) The SLEUTH model provides a framework for understanding land use evolution around urban areas. Calibration of SLEUTH’s behavioral coefficients can be impacted by scale and nonlinear transitions due to the SLEUTH land use deltatron module’s assumption of linear Markov change probabilities. This study attempted to establish what spatial resolution and temporal scale produces the most accurate forecasts given the linear change assumption. The impact of tiling the input data was also examined. To determine these, SLEUTH was calibrated at four spatial and three temporal scales for Ibadan, Nigeria using both untiled and tiled data. Calibration results were evaluated using accuracy metrics including Figure of Merit (FOM) and mean uncertainty. The best mix of calibration metrics (FOM 0.26) and mean uncertainty (11.64) was achieved at 30 m resolution and an intermediate temporal interval. Tiling input data led to overfitting, allowing good model fit within individual tiles but a reduction in trend recognition across land use types. Subsequently, a 2040 projection that is as accurate as possible, and scientifically justifiable given the available data, was produced. The findings provide a framework for understanding the effect of spatiotemporal scale on SLEUTH inputs that require tiling particularly for urban areas in the global south. Numéro de notice : A2022-347 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2011292 Date de publication en ligne : 16/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2011292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100531
in International journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1037 - 1058[article]Réservation
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