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Loosening the grid: topology as the basis for a more inclusive GIS / L. Westerveld in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 10 (October 2021) . - pp 2108 - 2127 Titre : Loosening the grid: topology as the basis for a more inclusive GIS Type de document : Article/Communication Auteurs : L. Westerveld, Auteur ; A.K. Knowles, Auteur Année de publication : 2021 Article en page(s) : pp 2108 - 2127 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] analyse spatiale [Termes IGN] cartographie historique [Termes IGN] données spatiotemporelles [Termes IGN] Europe de l'est [Termes IGN] graphe topologique [Termes IGN] guerre [Termes IGN] histoire [Termes IGN] historique des données [Termes IGN] relation topologique Résumé : (auteur) The past 25 years have seen a proliferation of interest in GIS among humanists and humanistic social scientists. Under various banners – historical GIS, qualitative and mixed methods GIS, spatial and digital history, digital humanities, spatial humanities or geohumanities – researchers have developed new ways to include qualitative data within the framework of GIS. At the same time, social and theoretical critiques of GIS as a tool wielded chiefly by and for those in power have increased awareness of its limitations, particularly for studying human perceptions, experiences, and the meanings of place. We agree with a growing chorus of geographers that the most common data structures in GIS are inadequate to capture or analyze the relational dimensions of subjectivity. As part of the solution, we propose an alternative hybrid framework that prioritizes topological relationships while retaining coordinate locations as points of connection between geographic places and experiential evidence. This article explains how the challenges posed by Holocaust survivor testimony inspired us to pursue inductive spatial analysis and non-coordinate means of mapping. We then set out a model for a topological GIS and illustrate its application with two survivor testimonies and a map of their experiences. Numéro de notice : A2021-680 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1856854 date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1080/13658816.2020.1856854 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98399 [article]

Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 36 - 50 Titre : Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning Type de document : Article/Communication Auteurs : Xin Jiang, Auteur ; Shijing Liang, Auteur ; Xinyue He, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] chaîne de traitement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Fleuve bleu (Chine) [Termes IGN] Google Earth Engine [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] inondation [Termes IGN] modèle numérique de surface [Termes IGN] segmentation d'image [Termes IGN] superpixel [Termes IGN] surveillance hydrologique Résumé : (auteur) Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. Numéro de notice : A2021-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.019 date de publication en ligne : 09/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98118 [article]

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Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec / Jean-Marie Zokagoa in Natural Hazards, vol 107 n° 1 (May 2021)  

Public [article]  inNatural Hazards > vol 107 n° 1 (May 2021) . - pp 285 - 310 Titre : Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec Type de document : Article/Communication Auteurs : Jean-Marie Zokagoa, Auteur ; Azzeddine Soulaïmani, Auteur ; Pierre Dupuis, Auteur Année de publication : 2021 Article en page(s) : pp 285 - 310 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] bathymétrie [Termes IGN] calcul de flux [Termes IGN] cartographie des risques [Termes IGN] hypercube [Termes IGN] inondation [Termes IGN] modèle hydrographique [Termes IGN] modèle mathématique [Termes IGN] propagation d'incertitude [Termes IGN] Québec (Canada) Résumé : (auteur) This study uses uncertainty propagation in real flood events to derive a probabilistic flood map. The flood event of spring 2017 in Quebec was selected for this analysis, with the computational domain being a reach of the Mille Iles River. The main parameter deemed uncertain in this work is the upstream water discharge; a given value of this discharge is utilized to build a random sample of 500 scenarios using the Latin hypercube sampling method. Simulations were run using CuteFlow-Cuda, an in-house finite volume-based shallow water equations solver, to derive the statistical mean and the standard deviation of the free surface elevation and the water depth at each node. For this real flood case, the initial interface flux scheme had to be adapted, combining a developed version of the scheme introduced by Harten, Lax and van Leer at wet interfaces and the Lax–Friedrichs scheme with additional free surface corrections for wet and dry transitions. Comparisons with results obtained from TELEMAC and from in situ observations show generally close predictions, and overall good agreement with observations. Errors of the free surface prediction relative to observations are less than 2.75%. A map based on the standard deviation of the water depth is presented to enhance the areas most prone to flooding. Finally, a flood map is produced, showing the flooded inhabited areas near the municipalities of Saint-Eustache and Deux Montagnes around the reach of the Mille Iles River as it overflows its natural bed. Numéro de notice : A2021-538 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-021-04583-2 date de publication en ligne : 20/02/2021 En ligne : https://doi.org/10.1007/s11069-021-04583-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98008 [article]

Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 132 - 143 Titre : Learning from multimodal and multitemporal earth observation data for building damage mapping Type de document : Article/Communication Auteurs : Bruno Adriano, Auteur ; Naoto Yokoya, Auteur ; Junshi Xia, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 132 - 143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] catastrophe naturelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cyclone [Termes IGN] dommage [Termes IGN] données multitemporelles [Termes IGN] image à haute résolution [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] observation de la Terre [Termes IGN] segmentation sémantique [Termes IGN] séisme [Termes IGN] surveillance d'ouvrage [Termes IGN] tsunami Résumé : (auteur) Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. Numéro de notice : A2021-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.016 date de publication en ligne : 17/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97343 [article]

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Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 8 (April-2 2021) . - n° 1579 Titre : Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping Type de document : Article/Communication Auteurs : Jiadi Yin, Auteur ; Ping Fu, Auteur ; Nicholas A.S. Hamm, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte d'utilisation du sol [Termes IGN] cartographie urbaine [Termes IGN] Chine [Termes IGN] données massives [Termes IGN] image Sentinel-MSI [Termes IGN] intégration de données [Termes IGN] OpenStreetMap [Termes IGN] point d'intérêt [Termes IGN] zone urbaine Résumé : (auteur) Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy. Numéro de notice : A2021-383 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081579 date de publication en ligne : 19/04/2021 En ligne : https://doi.org/10.3390/rs13081579 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97634 [article]

Urban expansion in the megacity since 1970s: a case study in Mumbai / Sisi Yu in Geocarto international, vol 36 n° 6 ([30/03/2021])  

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Characterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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Integration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

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Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

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Susceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie : application de la méthode de la théorie de l’évidence / Taoufik Byou in Geomatica [en ligne], vol 75 n° 1 (Mars 2021)  

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Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)  

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A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Extracting knowledge from legacy maps to delineate eco-geographical regions / Lin Yang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand / Narissara Nuthammachot in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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