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Provenance in GIServices: A semantic web approach / Zhaoyan Wu in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)
[article]
Titre : Provenance in GIServices: A semantic web approach Type de document : Article/Communication Auteurs : Zhaoyan Wu, Auteur ; Hao Li, Auteur ; Peng Yue, Auteur Année de publication : 2023 Article en page(s) : n° 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées
[Termes IGN] métadonnées
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] service web
[Termes IGN] service web sémantique
[Termes IGN] source de données
[Termes IGN] système d'information géographique
[Termes IGN] web sémantiqueRésumé : (auteur) Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Numéro de notice : A2023-145 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030118 En ligne : https://doi.org/10.3390/ijgi12030118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102848
in ISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 118[article]Residents’ Experiential Knowledge and Its Importance for Decision-Making Processes in Spatial Planning: A PPGIS Based Study / Edyta Bąkowska-Waldmann in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)
[article]
Titre : Residents’ Experiential Knowledge and Its Importance for Decision-Making Processes in Spatial Planning: A PPGIS Based Study Type de document : Article/Communication Auteurs : Edyta Bąkowska-Waldmann, Auteur Année de publication : 2023 Article en page(s) : n° 102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] aménagement du territoire
[Termes IGN] base de connaissances
[Termes IGN] outil d'aide à la décision
[Termes IGN] Pologne
[Termes IGN] questionnaire
[Termes IGN] SIG participatif
[Termes IGN] urbanismeRésumé : (auteur) Decisions are a key element of spatial planning processes and in the face of increasing public participation in local governance, they become even more complex. The diversity of stakeholders in planning processes causes a significant increase in the number and scope of articulated expectations, needs, and knowledge that could be integrated into the process. Along with the participatory approaches in spatial planning, a departure from the expert-oriented decision-making model towards its collaborative form is expected. As everyday users of space, residents have knowledge about the functioning of its elements resulting from their experience, the so-called experiential knowledge. The study aimed to investigate the role of residents’ input in diagnosing space in spatial planning processes using public participation geographic information systems (PPGIS). The article presents the study’s results conducted in Poznan, Poland, among residents and urban planners using geo-questionnaires and in-depth interviews. The article presents the characteristics of the residents’ contribution to the spatial diagnosis and the possibilities and limitations of the involvement of residents’ knowledge collected using a geoweb tool in the professional work of urban planners. Numéro de notice : A2023-158 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030102 Date de publication en ligne : 01/03/2023 En ligne : https://doi.org/10.3390/ijgi12030102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102849
in ISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 102[article]SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)
[article]
Titre : SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Wenting Luo, Auteur ; Anqi Lin, Auteur ; Fanghua Hao, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Lanfa Liu, Auteur ; Yan Li, Auteur Année de publication : 2023 Projets : 1-Pas de projet / Article en page(s) : n° 101921 Note générale : Bibliographie
This work was supported by the National Natural Science Foundation of China [42201468, 42071358], Postdoctoral Innovation Talents Support Program of China [BX20220128], China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018].Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multicritère
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] cartographie urbaine
[Termes IGN] Chine
[Termes IGN] détection du bâti
[Termes IGN] données localisées des bénévoles
[Termes IGN] image à très haute résolution
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] point d'intérêt
[Termes IGN] représentation spatiale
[Termes IGN] zone urbaineRésumé : (auteur) Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. Numéro de notice : A2023-125 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101921 Date de publication en ligne : 06/12/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102504
in Computers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921[article]Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning / Iris de Gelis in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)
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Titre : Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning Type de document : Article/Communication Auteurs : Iris de Gelis, Auteur ; Sébastien Lefèvre, Auteur ; Thomas Corpetti, Auteur Année de publication : 2023 Article en page(s) : pp 274 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau neuronal siamois
[Termes IGN] semis de points
[Termes IGN] végétation
[Termes IGN] zone urbaineRésumé : (auteur) This study is concerned with urban change detection and categorization in point clouds. In such situations, objects are mainly characterized by their vertical axis, and the use of native 3D data such as 3D Point Clouds (PCs) is, in general, preferred to rasterized versions because of significant loss of information implied by any rasterization process. Yet, for obvious practical reasons, most existing studies only focus on 2D images for change detection purpose. In this paper, we propose a method capable of performing change detection directly within 3D data. Despite recent deep learning developments in remote sensing, to the best of our knowledge there is no such method to tackle multi-class change segmentation that directly processes raw 3D PCs. Thereby, based on advances in deep learning for change detection in 2D images and for analysis of 3D point clouds, we propose a deep Siamese KPConv network that deals with raw 3D PCs to perform change detection and categorization in a single step. Experimental results are conducted on synthetic and real data of various kinds (LiDAR, multi-sensors). Tests performed on simulated low density LiDAR and multi-sensor datasets show that our proposed method can obtain up to 80% of mean of IoU over classes of changes, leading to an improvement ranging from 10% to 30% over the state-of-the-art. A similar range of improvements is attainable on real data. Then, we show that pre-training Siamese KPConv on simulated PCs allows us to greatly reduce (more than 3,000
) the annotations required on real data. This is a highly significant result to deal with practical scenarios. Finally, an adaptation of Siamese KPConv is realized to deal with change classification at PC scale. Our network overtakes the current state-of-the-art deep learning method by 23% and 15% of mean of IoU when assessed on synthetic and public Change3D datasets, respectively. The code is available at the following link: https://github.com/IdeGelis/torch-points3d-SiameseKPConv.Numéro de notice : A2023-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.02.001 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.02.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102805
in ISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 274 - 291[article]A spatiotemporal data model and an index structure for computational time geography / Bi Yu Chen in International journal of geographical information science IJGIS, vol 37 n° 3 (March 2023)
[article]
Titre : A spatiotemporal data model and an index structure for computational time geography Type de document : Article/Communication Auteurs : Bi Yu Chen, Auteur ; Yu-Bo Luo, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 550 - 583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] approche hiérarchique
[Termes IGN] données massives
[Termes IGN] données spatiotemporelles
[Termes IGN] modèle conceptuel de données spatio-temporelles
[Termes IGN] requête spatiotemporelle
[Termes IGN] stockage de données
[Termes IGN] Time-geographyRésumé : (auteur) The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. Numéro de notice : A2023-153 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2128192 Date de publication en ligne : 03/10/2023 En ligne : https://doi.org/10.1080/13658816.2022.2128192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102836
in International journal of geographical information science IJGIS > vol 37 n° 3 (March 2023) . - pp 550 - 583[article]A unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)PermalinkValidation of Island 3D-mapping based on UAV spatial point cloud optimization: a case study in Dongluo Island of China / Jian Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 3 (March 2023)PermalinkWho owns the map? Data sovereignty and government spatial data collection, use, and dissemination / Peter A. Johnson in Transactions in GIS, vol 27 n° 1 (February 2023)PermalinkAnalysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)PermalinkComparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)PermalinkMulti-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services / Mingyue Xu in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)PermalinkMulti-nomenclature, multi-resolution joint translation: an application to land-cover mapping / Luc Baudoux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)PermalinkNonparametric upscaling of bark beetle infestations and management from plot to landscape level by combining individual-based with Markov chain models / Bruno Walter Pietzsch in European Journal of Forest Research, vol 142 n° 1 (February 2023)PermalinkPSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkWhere am I now? modelling disorientation in pan-scalar maps / Guillaume Touya in ISPRS International journal of geo-information, vol 12 n° 2 (February 2023)Permalink