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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]Change detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach / Joachim Gehrung in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
[article]
Titre : Change detection in street environments based on mobile laser scanning: A fuzzy spatial reasoning approach Type de document : Article/Communication Auteurs : Joachim Gehrung, Auteur ; Marcus Hebel, Auteur ; Michael Arens, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] Inférence floue
[Termes IGN] information sémantique
[Termes IGN] logique floue
[Termes IGN] milieu urbain
[Termes IGN] représentation spatiale
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (auteur) Automated change detection based on urban mobile laser scanning data is the foundation for a whole range of applications such as building model updates, map generation for autonomous driving and natural disaster assessment. The challenge with mobile LiDAR data is that various sources of error, such as localization errors, lead to uncertainties and contradictions in the derived information. This paper presents an approach to automatic change detection using a new category of generic evidence grids that addresses the above problems. Said technique, referred to as fuzzy spatial reasoning, solves common problems of state-of-the-art evidence grids and also provides a method of inference utilizing fuzzy Boolean reasoning. Based on this, logical operations are used to determine changes and combine them with semantic information. A quantitative evaluation based on a hand-annotated version of the TUM-MLS data set shows that the proposed method is able to identify confirmed and changed elements of the environment with F1-scores of 0.93 and 0.89. Numéro de notice : A2022-663 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100019 En ligne : https://doi.org/10.1016/j.ophoto.2022.100019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101524
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) . - n° 100019[article]Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization / Seda Şalap-Ayça in Transactions in GIS, vol 26 n° 4 (June 2022)
[article]
Titre : Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization Type de document : Article/Communication Auteurs : Seda Şalap-Ayça, Auteur Année de publication : 2022 Article en page(s) : pp 1718 - 1734 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] analyse de sensibilité
[Termes IGN] carte de Kohonen
[Termes IGN] représentation spatiale
[Termes IGN] réseau neuronal artificiel
[Termes IGN] visualisation cartographique
[Termes IGN] voisinage (relation topologique)
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Spatial global sensitivity analysis (SGSA) reveals and ranks the input–output relation in spatial models. The SGSA output is twofold: (1) first-order effects which are the linear relations of every input layer with the output; and (2) high-order effects where the nonlinear interaction among input layers is depicted. The resulting sensitivity maps are twice the number of input layers which is challenging to visualize, considering the limitations of the human cognitive system or visual representations. Finding similar patterns and projecting that similarity into a 2D surface will help to tackle this voluminous visual load. This article presents the implementation of self-organizing maps (SOM), a type of artificial neural network, as a dimension reduction approach for SGSA visualization. SOM is also used for feature selection to identify the most relevant feature for model uncertainty. The winning neurons at SOM are projected as the influence map and the results are compared with conventional visualization techniques. Numéro de notice : A2022-532 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12963 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.1111/tgis.12963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101080
in Transactions in GIS > vol 26 n° 4 (June 2022) . - pp 1718 - 1734[article]Spatial thinking in cartography teaching for schoolchildren / Sonia Maria Vanzella Castellar in International journal of cartography, vol 7 n° 3 (October 2021)
[article]
Titre : Spatial thinking in cartography teaching for schoolchildren Type de document : Article/Communication Auteurs : Sonia Maria Vanzella Castellar, Auteur ; Barbara Gomes Flaire Jordão, Auteur Année de publication : 2021 Article en page(s) : pp 304 - 316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale
[Termes IGN] apprentissage (cognition)
[Termes IGN] formation
[Termes IGN] information cartographique
[Termes IGN] lecture de carte
[Termes IGN] raisonnement spatial
[Termes IGN] représentation cartographique
[Termes IGN] représentation mentale spatiale
[Termes IGN] représentation spatiale
[Vedettes matières IGN] CartologieRésumé : (auteur) We present data and analyses on school teaching practises that relate spatial thinking with the learning of maps from a cartographic education perspective. This study intends to support further research in the field of school cartography, given the increasing availability of digital cartographic resources in both the formal and the informal environments frequented by the individual. This increase in information demands a careful and critical reading of the cartographic data, although this requires a clear understanding of cartographic elements on the part of the individual. This reinforces the role of teachers in the classroom, given that they can contribute to the development of critical reasoning though the analysis of current geographic problems during learning activities in school cartography. The results presented here emphasize the importance of these contributions, but also demand substantial and consistent teacher training, in particular on the theories of spatial thinking. Numéro de notice : A2021-743 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2021.1969716 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/23729333.2021.1969716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98670
in International journal of cartography > vol 7 n° 3 (October 2021) . - pp 304 - 316[article]Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps / Xiongfeng Yan in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)
[article]
Titre : Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Type de document : Article/Communication Auteurs : Xiongfeng Yan, Auteur ; Tinghua Ai, Auteur ; Min Yang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 490 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] codage
[Termes IGN] données vectorielles
[Termes IGN] graphe
[Termes IGN] mesure géométrique
[Termes IGN] modélisation du bâti
[Termes IGN] représentation cognitive
[Termes IGN] représentation spatialeRésumé : (auteur) The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching. Numéro de notice : A2021-166 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768260 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768260 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97100
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 490 - 512[article]Exemplaires(1)
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