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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)  

Public [article]  inComputers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921 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 urbaine Résumé : (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 [article]

Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach / Shenglong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 195 (January 2023) . - pp 129 - 152 Titre : Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach Type de document : Article/Communication Auteurs : Shenglong Chen, Auteur ; Yoshiki Ogawa, Auteur ; Chenbo Zhao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 129 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couleur (variable spectrale) [Termes IGN] détection du bâti [Termes IGN] distribution de Gauss [Termes IGN] image à haute résolution [Termes IGN] mosaïquage d'images [Termes IGN] segmentation sémantique Résumé : (auteur) Building footprint is a primary dataset of an urban geographic information system (GIS) database. Therefore, it is essential to establish a robust and automated framework for large-scale building extraction. However, the characteristic of remote sensing images complicates the application of the instance segmentation method based on the Mask R-CNN model, which ought to be improved toward extracting and fusing multi-scale features. Moreover, open-source satellite image datasets with wider spatial coverage and temporal resolution than high-resolution images may exhibit different coloration and resolution. This study proposes a large-scale building extraction framework based on super-resolution (SR) and instance segmentation using a relatively lower-resolution (>0.6 m) open-sourced dataset. The framework comprises four steps: color normalization and image super-resolution, scene classification, building extraction, and scene mosaicking. We took Hyogo Prefecture, Japan (19,187 km2) as a test area and extracted 1,726,006 (29.12 km2) of the 3,301,488 buildings (32.46 km2), where the number of buildings and footprint area increased by 3.0 % and 5.0 % respectively. The result indicated that the color normalization and image super-resolution could improve the visual quality of open-source satellite images and contribute to building extraction accuracy. Moreover, the improved Mask R-CNN based on Multi-Path Vision Transformer (MPViT) backbone achieved F1 scores of 0.71, 0.70, 0.81, and 0.67 for non-built-up, rural, suburban, and urban areas, respectively, which is better than those of the baseline model and other mainstream instance segmentation approaches. This study demonstrates the potential of acquiring acceptable building footprint maps from open-source satellite images, which has significant practical implications. Numéro de notice : A2023-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.11.006 Date de publication en ligne : 30/11/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.11.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102214 [article]

Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Linear building pattern recognition in topographical maps combining convex polygon decomposition Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Su Ding, Auteur ; Lu Cheng, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte topographique [Termes IGN] construction [Termes IGN] décomposition [Termes IGN] détection du bâti [Termes IGN] forme linéaire [Termes IGN] généralisation cartographique automatisée [Termes IGN] Ordnance Survey (UK) [Termes IGN] polygone [Termes IGN] reconnaissance de formes Résumé : (auteur) Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach. Numéro de notice : A2022-263 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2055794 Date de publication en ligne : 27/03/2022 En ligne : https://doi.org/10.1080/10106049.2022.2055794 Format de la ressource électronique : 27/03/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100260 [article]

Foreground-aware refinement network for building extraction from remote sensing images / Zhang Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 11 (November 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 11 (November 2022) . - pp 731 - 738 Titre : Foreground-aware refinement network for building extraction from remote sensing images Type de document : Article/Communication Auteurs : Zhang Yan, Auteur ; Wang Xiangyu, Auteur ; Zhang Zhongwei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 731 - 738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de régions [Termes IGN] détection du bâti [Termes IGN] extraction de traits caractéristiques [Termes IGN] image RVB [Termes IGN] jeu de données Résumé : (auteur) To extract buildings accurately, we propose a foreground-aware refinement network for building extraction. In particular, in order to reduce the false positive of buildings, we design the foreground-aware module using the attention gate block, which effectively suppresses the features of nonbuilding and enhances the sensitivity of the model to buildings. In addition, we introduce the reverse attention mechanism in the detail refinement module. Specifically, this module guides the network to learn to supplement the missing details of the buildings by erasing the currently predicted regions of buildings and achieves more accurate and complete building extraction. To further optimize the network, we design hybrid loss, which combines BCE loss and SSIM loss, to supervise network learning from both pixel and structure layers. Experimental results demonstrate the superiority of our network over state-of-the-art methods in terms of both quantitative metrics and visual quality. Numéro de notice : A2022-842 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00081R2 Date de publication en ligne : 01/11/2022 En ligne : https://doi.org/10.14358/PERS.21-00081R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102055 [article]

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Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 78 - 94 Titre : Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Qian Shen, Auteur ; Jiru Huang, Auteur ; Min Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 78 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] données qualitatives [Termes IGN] estimation quantitative [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] jeu de données [Termes IGN] réseau neuronal siamois Résumé : (auteur) In the field of remote sensing applications, semantic change detection (SCD) simultaneously identifies changed areas and their change types by jointly conducting bitemporal image classification and change detection. It facilitates change reasoning and provides more application value than binary change detection (BCD), which offers only a binary map of the changed/unchanged areas. In this study, we propose a multitask Siamese network, named the semantic feature-constrained change detection (SFCCD) network, for building change detection in bitemporal high-spatial-resolution (HSR) images. SFCCD conducts feature extraction, semantic segmentation and change detection simultaneously, where change detection and semantic segmentation are the main and auxiliary tasks, respectively. For the segmentation task, ResNet50 is used to conduct image feature extraction, and the extracted semantic features are provided to execute the change detection task via a series of jump connections. For the change detection task, a global channel attention (GCA) module and a multiscale feature fusion (MSFF) module are designed, where high-level features offer training guidance to the low-level feature maps, and multiscale features are fused with multiple convolutions that possess different receptive fields. In bitemporal HSR images with different view angles, high-rise buildings have different directional height displacements, which generally cause serious false alarms for common change detection methods. However, known public building change detection datasets often lack buildings with height displacement. We thus create the Nanjing Dataset (NJDS) and design the aforementioned network structures and modules to target this issue. Experiments for method validation and comparison are conducted on the NJDS and two additional public datasets, i.e., the WHU Building Dataset (WBDS) and Google Dataset (GDS). Ablation experiments on the NJDS show that the joint utilization of the GCA and MSFF modules performs better than several classic modules, including atrous spatial pyramid pooling (ASPP), efficient spatial pyramid (ESP), channel attention block (CAB) and global attention upsampling (GAU) modules, in dealing with building height displacement. Furthermore, SFCCD achieves higher accuracy in terms of the OA, recall, F1-score and mIoU measures than several state-of-the-art change detection methods, including deeply supervised image fusion network (DSIFN), the dual-task constrained deep Siamese convolutional network (DTCDSCN), and multitask U-Net (MTU-Net). Numéro de notice : A2022-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.001 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100762 [article]

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Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)

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Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])  

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Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)  

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