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Geographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)
[article]
Titre : Geographically masking addresses to study COVID-19 clusters Type de document : Article/Communication Auteurs : Walid Houfaf-Khoufaf, Auteur ; Guillaume Touya , Auteur Année de publication : 2023 Projets : 1-Pas de projet / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] adresse postale
[Termes IGN] anonymisation
[Termes IGN] carte sanitaire
[Termes IGN] classification barycentrique
[Termes IGN] surveillance sanitaire
[Termes IGN] traitement de données localiséesRésumé : (auteur) The spatio-temporal analysis of cases is a good way an epidemic, and the recent COVID-19 pandemic unfortunately generated a huge amount of data. But analysing this raw data, with for instance the address of the people who contracted COVID-19, raises some privacy issues, and geomasking is necessary to preserve both people privacy and the spatial accuracy required for analysis. This paper proposes dierent geomasking techniques adapted to this COVID-19 data. Methods: Different techniques are adapted from the literature, and tested on a synthetic dataset mimicking the COVID-19 spatio-temporal spreading in Paris and a more rural nearby region. Theses techniques are assessed in terms of k-anonymity and cluster preservation. Results: Three adapted geomasking techniques are proposed: aggregation, bimodal gaussian perturbation, and simulated crowding. All three can be useful in different use cases, but the bimodal gaussian perturbation is the overall best techniques, and the simulated crowding is the most promising one, provided some improvements are introduced to avoid points with a low k-anonymity. Conclusions: It is possible to use geomasking techniques on addresses of people who caught COVID-19, while preserving the important spatial patterns. Numéro de notice : A2023-084 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers RSquare Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1977709 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1080/15230406.2021.1977709 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96857
in Cartography and Geographic Information Science > vol inconnu (2023)[article]GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates / Valerio Marsocci (2023)
Titre : GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates Type de document : Article/Communication Auteurs : Valerio Marsocci, Auteur ; Nicolas Gonthier, Auteur ; Anatol Garioud , Auteur ; Simone Scardapane, Auteur ; Clément Mallet , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : 11 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'occupation du sol
[Termes IGN] image à très haute résolution
[Termes IGN] jeu de données localisées
[Termes IGN] métadonnées géographiques
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the frequency of classes. This approach is the first to use geographical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), reducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here. Numéro de notice : C2023-004 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication DOI : 10.48550/arXiv.2304.07750 En ligne : https://doi.org/10.48550/arXiv.2304.07750 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103083 Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)
[article]
Titre : Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image Type de document : Article/Communication Auteurs : Taposh Mollick, Auteur ; MD Golam Azam, Auteur ; Sabrina Karim, Auteur Année de publication : 2023 Article en page(s) : n° 100859 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] Bangladesh
[Termes IGN] classification non dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification pixellaire
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] occupation du sol
[Termes IGN] rendement agricole
[Termes IGN] segmentation d'image
[Termes IGN] utilisation du solRésumé : (auteur) Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective. Numéro de notice : A2023-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rsase.2022.100859 Date de publication en ligne : 22/11/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100859 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102224
in Remote Sensing Applications: Society and Environment, RSASE > vol 29 (January 2023) . - n° 100859[article]A hexagon-based method for polygon generalization using morphological operators / Lu Wang in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)
[article]
Titre : A hexagon-based method for polygon generalization using morphological operators Type de document : Article/Communication Auteurs : Lu Wang, Auteur ; Tinghua Ai, Auteur ; Dirk Burghardt, Auteur ; Yilang Shen, Auteur ; Min Yang, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données maillées
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] morphologie mathématique
[Termes IGN] polygone
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins. Numéro de notice : A2023-071 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2108036 Date de publication en ligne : 10/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2108036 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101387
in International journal of geographical information science IJGIS > vol 37 n° 1 (January 2023)[article]HGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation / Xuefeng Guan in Computers, Environment and Urban Systems, vol 99 (January 2023)
[article]
Titre : HGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation Type de document : Article/Communication Auteurs : Xuefeng Guan, Auteur ; Weiran Xing, Auteur ; Jingbo Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101900 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] adjacence
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] automate cellulaire
[Termes IGN] changement d'utilisation du sol
[Termes IGN] croissance urbaine
[Termes IGN] étalement urbain
[Termes IGN] hétérogénéité spatiale
[Termes IGN] modèle de simulation
[Termes IGN] Queensland (Australie)
[Termes IGN] réseau neuronal de graphes
[Termes IGN] voisinage (relation topologique)
[Termes IGN] zone tamponRésumé : (auteur) Since urban growth results from frequent spatial interaction between urban units, adequate representation of spatial interaction is important for urban growth modeling. Among urban growth models, vector-based cellular automata (VCA) excels at expressing spatial interaction with realistic entities, and has accordingly been used extensively in recent studies. However, two issues with VCA modeling still remain: 1) inefficient manual selection of interaction targets with various neighborhood configurations; 2) inaccurate quantification of interaction intensity due to ignorance of spatial heterogeneity in entity interaction. To address these two limitations, this study proposed a novel VCA model with high-order graph attention network (HGAT-VCA). In this model, a graph structure is first built from the topology adjacency relationship between cadastral parcels. In terms of the HGAT components, the original 1st-order parcel neighborhood is extended to high-order to capture the distant dependency, while graph attention is applied to quantify the heterogeneous interaction intensity between parcels. Finally, the conversion probability obtained by HGAT is integrated with VCA to simulate urban land use change. Land use data from the Moreton Bay Region in Queensland, Australia from 2005 to 2009 are selected to verify the proposed HGAT-VCA model. Experimental results illustrate that HGAT-VCA outperforms four classical CA models and achieves the highest simulation accuracy (e.g., the increase of FoM is about 40.7%). In addition, extensive neighborhood configuration experiments show that with HGAT only tuning discrete topological order can generate similar accuracy results compared with the repetitive buffer-based neighborhood configuration, and this can significantly improve the calibration efficiency of VCA models. Numéro de notice : A2023-031 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101900 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101900 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102163
in Computers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101900[article]A hierarchical multiview registration framework of TLS point clouds based on loop constraint / Hao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)PermalinkIncorporating ideas of structure and meaning in interactive multi scale mapping environments / Guillaume Touya in International journal of cartography, vol inconnu (2023)PermalinkLandscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population / Heng Wan in Computers, Environment and Urban Systems, vol 99 (January 2023)PermalinkLinear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkMachine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)PermalinkMeasuring metro accessibility: An exploratory study of Wuhan based on multi-source urban data / Tao Wu in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)PermalinkA method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkMitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkModern vectorization and alignment of historical maps: An application to Paris Atlas (1789-1950) / Yizi Chen (2023)PermalinkPermalinkSemi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments / Yuhao Jiang (2023)PermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkUsing Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkAutomatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkAutomatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkCoastal land use and shoreline evolution along the Nador lagoon Coast in Morocco / Khalid El Khalidi in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkHyperspectral imagery and urban areas: results of the HYEP project / Christiane Weber in Revue Française de Photogrammétrie et de Télédétection, n° 224 (2022)PermalinkIntegration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkLinkClimate: An interoperable knowledge graph platform for climate data / Jiantao Wu in Computers & geosciences, vol 169 (December 2022)PermalinkMapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)Permalink