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Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)
[article]
Titre : Determination of building flood risk maps from LiDAR mobile mapping data Type de document : Article/Communication Auteurs : Yu Feng, Auteur ; Qing Xiao, Auteur ; Claus Brenner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101759 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] cartographie d'urgence
[Termes IGN] cartographie des risques
[Termes IGN] classification semi-dirigée
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] infiltration
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] segmentation sémantiqueRésumé : (auteur) With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such data is not yet readily available in most cities. Traditional surveying of the desired targets may be used, but this is a very time-consuming and laborious process. Instead, mobile mapping using LiDAR (light detection and ranging) is an efficient tool to obtain a large amount of high-density 3D measurement data. To use this method, it is required to extract the desired facade openings from the data in a fully automatic manner. This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data. Deep learning object detection models are trained to identify these objects. Usually, this requires to provide large amounts of manual annotations.
In this paper, we mitigate this problem by leveraging a rule-based method. In a first step, the rule-based method is used to generate pseudo-labels. A semi-supervised learning strategy is then applied with three different levels of supervision. The results show that using only automatically generated pseudo-labels, the learning-based model outperforms the rule-based approach by 14.6% in terms of F1-score. After five hours of human supervision, it is possible to improve the model by another 6.2%. By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels. Thus, our research provides a new geographic information layer for fine-grained urban emergency response. This information can be combined with flood forecasting to provide a more targeted disaster prevention guide for the city's infrastructure and residential buildings. To the best of our knowledge, this work is the first attempt to achieve such a large scale, fine-grained building flood risk mapping.Numéro de notice : A2022-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101759 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99964
in Computers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101759[article]Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
[article]
Titre : Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Zhipeng Gui, Auteur ; Jimin Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 799 - 821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] descripteur
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] image cartographique
[Termes IGN] métadonnées
[Termes IGN] projection
[Termes IGN] système d'information géographique
[Termes IGN] Web Map Service
[Termes IGN] web mappingRésumé : (auteur) Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach. Numéro de notice : A2022-258 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/13658816.2021.1968407 En ligne : https://doi.org/10.1080/13658816.2021.1968407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100231
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 799 - 821[article]Flood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (April 2022)
[article]
Titre : Flood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco Type de document : Article/Communication Auteurs : Brahim Benzougagh, Auteur ; Pierre-Louis Frison , Auteur ; Sarita Gajbhiye Meshram, Auteur ; Larbi Boudad, Auteur ; Abdallah Dridri, Auteur ; Driss Sadkaoui, Auteur ; Khalid Mimich, Auteur ; Khaled Mohamed Khedher, Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 1481 - 1490 Note générale : bibliographie
This research work was supported by the Deanship of Scientific Research at King Khalid University under Grant number RGP. 2/173/42.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] Maroc
[Termes IGN] plan de prévention des risques
[Termes IGN] prévention des risques
[Termes IGN] risque naturelRésumé : (auteur) Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods. Numéro de notice : A2022-580 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1007/s40996-021-00683-y Date de publication en ligne : 18/06/2021 En ligne : https://doi.org/10.1007/s40996-021-00683-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99581
in Iranian Journal of Science and Technology - Transactions of Civil Engineering > vol 46 n° 2 (April 2022) . - pp 1481 - 1490[article]A GAN-based approach toward architectural line drawing colorization prototyping / Qian (Chayn) Sun in The Visual Computer, vol 38 n° 4 (April 2022)
[article]
Titre : A GAN-based approach toward architectural line drawing colorization prototyping Type de document : Article/Communication Auteurs : Qian (Chayn) Sun, Auteur ; Yan Chen, Auteur ; Wenyuan Tao, Auteur ; Han Jiang, Auteur ; Mu Zhang, Auteur ; Kan Chen, Auteur ; Marius Erdt, Auteur Année de publication : 2022 Article en page(s) : pp 1283 - 1300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] architecture
[Termes IGN] bâtiment
[Termes IGN] couleur (variable spectrale)
[Termes IGN] prototype
[Termes IGN] réseau antagoniste génératif
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping. Numéro de notice : A2022-154 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-021-02219-x Date de publication en ligne : 23/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02219-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100292
in The Visual Computer > vol 38 n° 4 (April 2022) . - pp 1283 - 1300[article]Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica, vol 26 n° 2 (April 2022)
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Titre : Graph neural network based model for multi-behavior session-based recommendation Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Ruoqian Zhang, Auteur ; Wei Chen, Auteur ; Junhua Fang, Auteur Année de publication : 2022 Article en page(s) : pp 429 - 447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] comportement
[Termes IGN] consommation
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau sémantique
[Termes IGN] service fondé sur la positionMots-clés libres : session Résumé : (auteur) Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior transition through feature-level representation learning. Besides, sequential and non-sequential patterns should be properly fused in session modeling to capture dynamic interests within the session. To this end, this paper proposes a Graph Neural Network based Hybrid Model GNNH, which enables feature-level deeper representations of multi-behavior interaction sequences for session-based recommendation. Specifically, we first construct multi-relational item graph (MRIG) and feature graph (MRFG) based on session sequences. On top of the MRIG and MRFG, our model takes advantage of GNN to capture item and feature representations, such that global item-to-item and feature-to-feature relations are fully preserved. Afterwards, each multi-behavior session is modeled by a seamless fusion of interacted item and feature representations, where self-attention and mean-pooling are used to obtain sequential and non-sequential patterns simultaneously. Experiments on two real datasets show that the GNNH model significantly outperforms the state-of-the-art methods. Numéro de notice : A2022-326 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00439-w Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.1007/s10707-021-00439-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100489
in Geoinformatica > vol 26 n° 2 (April 2022) . - pp 429 - 447[article]Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)PermalinkNatural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches / Joyce Machado Nunes Romeiro in Forest ecology and management, vol 509 (April-1 2022)PermalinkSpatial modeling of migration using GIS-based multi-criteria decision analysis: A case study of Iran / Naeim Mijani in Transactions in GIS, vol 26 n° 2 (April 2022)PermalinkAutomated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkComparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)PermalinkDynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkEarly warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkExploring the strategy goals and strategy drivers of national mapping, cadastral, and land registry authorities / Erik Hämäläinen in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkFlood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)PermalinkMeasuring and mapping long-term changes in migration flows using population-scale family tree data / Caglar Koylu in Cartography and Geographic Information Science, vol 49 n° 2 (March 2022)PermalinkLes noms de lieux mentionnés dans des récits de vie de républicains espagnols : distribution géographique et perceptions associées / Laurence Jolivet in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkThe re-invention of the Goori cultural landscape: Telling the country: Mapping two pockets / Paul Memmott in Cartographica, Vol 57 n° 1 (Spring 2022)PermalinkUnderstanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea / Yang Xu in Computers, Environment and Urban Systems, vol 92 (March 2022)PermalinkA national fuel type mapping method improvement using sentinel-2 satellite data / Alexandra Stefanidou in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkScorch height and volume modeling in prescribed fires: Effects of canopy gaps in Pinus pinaster stands in Southern Europe / J.R. Molina in Forest ecology and management, vol 506 (February-15 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkPourquoi la forêt française a besoin d’un traitement de fond / Guillaume Decocq in The Conversation France, vol 2022 ([10/02/2022])PermalinkDébris spatiaux, l’inquiétante prolifération / Laurent Polidori in Géomètre, n° 2199 (février 2022)PermalinkDetection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)PermalinkDevelopment of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan / Eunbeen Park in GIScience and remote sensing, vol 59 n° 1 (2022)Permalink