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Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 326 Titre : Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area Type de document : Article/Communication Auteurs : Siming Yin, Auteur ; Xian Guo, Auteur ; Jie Jiang, Auteur Année de publication : 2022 Article en page(s) : n° 326 Note générale : résumé Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Streetview [Termes IGN] paysage urbain [Termes IGN] Pékin (Chine) [Termes IGN] segmentation sémantique [Termes IGN] site historique Résumé : (auteur) Accurate extraction of urban landscape features in the historic district of China is an essential task for the protection of the cultural and historical heritage. In recent years, deep learning (DL)-based methods have made substantial progress in landscape feature extraction. However, the lack of annotated data and the complex scenarios inside alleyways result in the limited performance of the available DL-based methods when extracting landscape features. To deal with this problem, we built a small yet comprehensive history-core street view (HCSV) dataset and propose a polarized attention-based landscape feature segmentation network (PALESNet) in this article. The polarized self-attention block is employed in PALESNet to discriminate each landscape feature in various situations, whereas the atrous spatial pyramid pooling (ASPP) block is utilized to capture the multi-scale features. As an auxiliary, a transfer learning module was introduced to supplement the knowledge of the network, to overcome the shortage of labeled data and improve its learning capability in the historic districts. Compared to other state-of-the-art methods, our network achieved the highest accuracy in the case study of Beijing Core Area, with an mIoU of 63.7% on the HCSV dataset; and thus could provide sufficient and accurate data for further protection and renewal in Chinese historic districts. Numéro de notice : A2022-410 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060326 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.3390/ijgi11060326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100760 [article]

Efficient calculation of distance transform on discrete global grid systems / Meysam Kazemi in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 322 Titre : Efficient calculation of distance transform on discrete global grid systems Type de document : Article/Communication Auteurs : Meysam Kazemi, Auteur ; Lakin Wecker, Auteur ; Faramarz Samavati, Auteur Année de publication : 2022 Article en page(s) : n° 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] ArcGIS [Termes IGN] distance [Termes IGN] données vectorielles [Termes IGN] frontière [Termes IGN] maillage par triangles [Termes IGN] Ontario (Canada) [Termes IGN] sphèroïde [Termes IGN] système d'information géographique [Termes IGN] système de grille globale discrète [Termes IGN] transformation Résumé : (auteur) Numéro de notice : A2022-411 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060322 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.3390/ijgi11060322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100761 [article]

GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 336 Titre : GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data Type de document : Article/Communication Auteurs : Wanqin He, Auteur ; Sara Shirowzhan, Auteur ; Christopher Pettit, Auteur Année de publication : 2022 Article en page(s) : n° 336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] brousse [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] coefficient de corrélation [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] humidité du sol [Termes IGN] incendie [Termes IGN] indice de végétation [Termes IGN] Nouvelle-Galles du Sud [Termes IGN] prévention des risques [Termes IGN] régression linéaire [Termes IGN] Spark [Termes IGN] système d'information géographique [Termes IGN] température de l'air Résumé : (auteur) The causes of bushfires are extremely complex, and their scale of burning and probability of occurrence are influenced by the interaction of a variety of factors such as meteorological factors, topography, human activity and vegetation type. An in-depth understanding of the combined mechanisms of factors affecting the occurrence and spread of bushfires is needed to support the development of effective fire prevention plans and fire suppression measures and aid planning for geographic, ecological maintenance and urban emergency management. This study aimed to explore how bushfires, meteorological variability and other natural factors have interacted over the past 40 years in NSW Australia and how these influencing factors synergistically drive bushfires. The CSIRO’s Spark toolkit has been used to simulate bushfire burning spread over 24 h. The study uses NSW wildfire data from 1981–2020, combined with meteorological factors (temperature, precipitation, wind speed), vegetation data (NDVI data, vegetation type) and topography (slope, soil moisture) data to analyse the relationship between bushfires and influencing factors quantitatively. Machine learning-random forest regression was then used to determine the differences in the influence of bushfire factors on the incidence and burn scale of bushfires. Finally, the data on each influence factor was imported into Spark, and the results of the random forest model were used to set different influence weights in Spark to visualise the spread of bushfires burning over 24 h in four hotspot regions of bushfire in NSW. Wind speed, air temperature and soil moisture were found to have the most significant influence on the spread of bushfires, with the combined contribution of these three factors exceeding 60%, determining the spread of bushfires and the scale of burning. Precipitation and vegetation showed a greater influence on the annual frequency of bushfires. In addition, burn simulations show that wind direction influences the main direction of fire spread, whereas the shape of the flame front is mainly due to the influence of land classification. Besides, the simulation results from Spark could predict the temporal and spatial spread of fire, which is a potential decision aid for fireproofing agencies. The results of this study can inform how fire agencies can better understand fire occurrence mechanisms and use bushfire prediction and simulation techniques to support both their operational (short-term) and strategic (long-term) fire management responses and policies. Numéro de notice : A2022-481 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060336 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/ijgi11060336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100894 [article]

3D modeling method for dome structure using digital geological map and DEM / Xian-Yu Liu in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 339 Titre : 3D modeling method for dome structure using digital geological map and DEM Type de document : Article/Communication Auteurs : Xian-Yu Liu, Auteur ; An-Bo Li, Auteur ; Hao Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 339 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte géologique [Termes IGN] carte stratigraphique [Termes IGN] courbe de Bézier [Termes IGN] modèle géologique [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] structure géologique Résumé : (auteur) Geological maps have wide coverage with low acquisition difficulty. When other geological survey data are scarce, they are a valuable source of geological structure information for geological modeling. However, for structures with large deformation, geological map information has difficulty meeting the requirement of its 3D geological modeling. Therefore, this paper takes the dome structure as an example to explore a 3D modeling method based on geological maps, DEM and related geological knowledge. The method includes: (1) adaptively calculating the attitude of points on the stratigraphic boundaries; (2) inferring and generating the bottom boundary of the model from the attitude data of the boundary points; (3) generating the model interface constrained by Bézier curves based on the bottom boundary; (4) generating the top and bottom surfaces of the stratum; and (5) stitching each surface of the geological body to generate the final dome model. Case studies of the dome in Wulongshan in China and the Richat structure in Mauritania show that this method can build a solid model of the dome based only on geological maps and DEM data, whose morphological features are basically consistent with those embodied in the section view or the model generated by traditional methods. Numéro de notice : A2022-482 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11060339 Date de publication en ligne : 07/06/2022 En ligne : https://doi.org/10.3390/ijgi11060339 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100895 [article]