Centre dedocumentation
scientifique

Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping / Sandro Martinis in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113077 Titre : Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping Type de document : Article/Communication Auteurs : Sandro Martinis, Auteur ; Sandro Groth, Auteur ; Marc Wieland, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] Allemagne [Termes IGN] Australie [Termes IGN] carte thématique [Termes IGN] fusion d'images [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] inondation [Termes IGN] Mozambique [Termes IGN] prévention des risques [Termes IGN] série temporelle [Termes IGN] Soudan [Termes IGN] surveillance hydrologique [Termes IGN] variation saisonnière [Termes IGN] zone à risque Résumé : (auteur) Satellite-based flood mapping has become an important part of disaster response. In order to accurately distinguish flood inundation from normally present conditions, up-to-date, high-resolution information on the seasonal water cover is crucial. This information is usually neglected in disaster management, which may result in a non-reliable representation of the flood extent, mainly in regions with highly dynamic hydrological conditions. In this study, we present a fully automated method to generate a global reference water product specifically designed for the use in global flood mapping applications based on high resolution Earth Observation data. The proposed methodology combines existing processing pipelines for flood detection based on Sentinel-1/2 data and aggregates permanent as well as seasonal water masks over an adjustable reference time period. The water masks are primarily based on the analysis of Sentinel-2 data and are complemented by Sentinel-1-based information in optical data scarce regions. First results are demonstrated in five selected study areas (Australia, Germany, India, Mozambique, and Sudan), distributed across different climate zones and are systematically compared with external products. Further, the proposed product is exemplary applied to three real flood events in order to evaluate the impact of the used reference water mask on the derived flood extent. Results show, that it is possible to generate a consistent reference water product at 10–20 m spatial resolution, that is more suitable for the use in rapid disaster response than previous masks. The proposed multi-sensor approach is capable of producing reasonable results, even if only few or no information from optical data is available. Further it becomes clear, that the consideration of seasonality of water bodies, especially in regions with highly dynamic hydrological and climatic conditions, reduces potential over-estimation of the inundation extent and gives a more reliable picture on flood-affected areas. Numéro de notice : A2022-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113077 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100801 [article]

ART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage / M. Moreno in Journal of Cultural Heritage, vol 55 (May - June 2022)  

Public [article]  inJournal of Cultural Heritage > vol 55 (May - June 2022) . - pp 263 - 276 Titre : ART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage Type de document : Article/Communication Auteurs : M. Moreno, Auteur ; R. Ortiz, Auteur ; D. Cagigas-Muñiz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 263 - 276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse des risques [Termes IGN] conservation du patrimoine [Termes IGN] église [Termes IGN] Espagne [Termes IGN] gelée [Termes IGN] Inférence floue [Termes IGN] inondation [Termes IGN] intelligence artificielle [Termes IGN] logique floue [Termes IGN] monument historique [Termes IGN] patrimoine culturel [Termes IGN] risque naturel [Termes IGN] séisme [Termes IGN] système d'information géographique [Termes IGN] température de l'air Résumé : (auteur) Heritage preservation poses numerous difficulties, especially in emergency situations or during budget cuts. In these contexts, having tools that facilitate efficient and rapid management of hazards-vulnerabilities is a priority for the preventive conservation and triage of cultural assets. This paper presents the first (to the authors' knowledge) free and public availability Artificial Intelligence platform designed for conservation strategies in cultural heritage. Art-Risk 3.0 is a platform designed as a fuzzy-logic inference system that combines information from geographical information system maps with expert assessments, in order to identify the contextual threat level and the degree of vulnerability that heritage buildings present. Thanks to the possibilities that the geographic information system offers, 12 Spanish churches (11th - 16th centuries) were analyzed. The artificial intelligence platform developed makes it possible to analyze the index of hazard, vulnerability and functionality, classify buildings according to the risk in order to do a sustainable use of budgets through the rational management of preventive conservation. The data stored in the system allows identify the danger due to geotechnics, precipitation, torrential downpour, thermal oscillation, frost, earthquake and flooding. Through the use of fuzzy logic, the tool interrelates environmental conditions with 14 other variables related to structural risks and the vulnerability of buildings, which are evaluated through bibliographic search and review of photographic images. The geographic information system has identified torrential rains and thermal oscillations as the environmental threats that mostly impact heritage buildings in Spain. The results obtained highlight the Church of Santiago de Jesús as the most vulnerable building due to a lack of preventive conservation programs. These results, consistent with the inclusion of this monument on the list of heritage at risk defined by Hispania Nostra, corroborate the functionality of the model. Numéro de notice : A2022-472 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.culher.2022.03.012 Date de publication en ligne : 14/04/2022 En ligne : https://doi.org/10.1016/j.culher.2022.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100818 [article]

Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101755 Titre : Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data Type de document : Article/Communication Auteurs : Cheng-Chun Lee, Auteur ; Nasir G. Gharaibeh, Auteur Année de publication : 2022 Article en page(s) : n° 101755 Note générale : bibliogrphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drainage [Termes IGN] écoulement des eaux [Termes IGN] Houston (Texas) [Termes IGN] inondation [Termes IGN] lidar mobile [Termes IGN] modèle numérique de surface [Termes IGN] ruissellement [Termes IGN] segmentation sémantique Résumé : (auteur) Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. Numéro de notice : A2022-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101755 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99661 [article]

Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101759 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 sémantique Résumé : (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 [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)  

Public [article]  inIranian Journal of Science and Technology - Transactions of Civil Engineering > vol 46 n° 2 (April 2022) . - pp 1481 - 1490 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 naturel Résumé : (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 : A2021-937 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 [article]

Flood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 2022)  

Permalink

CIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica [en ligne], vol 26 n° 1 (January 2022)  

Permalink

Evaluation de méthodes automatisées de cartographie des zones inondables adaptées à la prévision des crues soudaines / Nabil Hocini (2022)  

Permalink

Flood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

Permalink

Remote sensing in applications of geoinformation / Silas Michaelides (2022)    

Permalink

Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region) / Huma Hayat in Geocarto international, vol 37 n° 1 ([01/01/2022])  

Permalink

Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment / Ali Azareh in Geocarto international, vol 36 n° 20 ([01/12/2021])  

Permalink

Potential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin / Olabanji Aladejana in Geocarto international, vol 36 n° 19 ([01/11/2021])  

Permalink

Disaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Permalink

Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model / Gaurav Talukdar in Natural Hazards, vol 109 n° 1 (October 2021)  

Permalink