Centre dedocumentation
scientifique

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]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 274 - 291 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 urbaine Résumé : (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 [article]

Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier / Mahmoud Mohamed in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier Type de document : Article/Communication Auteurs : Mahmoud Mohamed, Auteur ; Salem Morsy, Auteur ; Adel El-Shazly, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] zone urbaine Mots-clés libres : cylindrical neighbourhood = voisinage cylindrique Résumé : (auteur) 3D road mapping is essential for intelligent transportation system in smart cities. Road environment receives its data from mobile laser scanning (MLS) systems in the format of LiDAR point clouds, which are distinguished with their accuracy and high density. In this paper, a mobile LiDAR data classification method based on machine learning (ML) is presented. First, data subsampling and slicing are applied, followed by cylindrical neighbourhood selection method to determine the neighbourhood of each point. Second, a new LiDAR-based point feature namely Zmodis introduced, and used along with existing fifteen geometric features as input for a ML algorithm. Finally, Random Forest classifier is applied to a part of (Paris-Lille-3D) MLS point clouds belonging to NPM3D Benchmark. The dataset is about 1.5 km long road in Lille, France with more than 98 million points. The use of Zmod improved the accuracy from 90.26% to 95.23% and achieved sufficient results for classes with low points' portion in the dataset. In addition, the Zmod is the third important feature in the classification process among the sixteen features with about 14.63%. Moreover, using the first six important features achieved almost the maximum overall accuracy with about 60% reduction in the processing time. Numéro de notice : A2022-622 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102218 Date de publication en ligne : 21/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101357 [article]

A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration / Tarek Hassan in Journal of applied geodesy, vol 17 n° 1 (January 2023)  

Public [article]  inJournal of applied geodesy > vol 17 n° 1 (January 2023) . - pp 65 - 77 Titre : A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration Type de document : Article/Communication Auteurs : Tarek Hassan, Auteur ; Tamer Fath-Allah, Auteur ; Mohamed Elhabiby, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 65 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] capteur à balayage [Termes IGN] centrale inertielle [Termes IGN] gyroscope [Termes IGN] lidar mobile [Termes IGN] odomètre [Termes IGN] panne [Termes IGN] positionnement par GNSS [Termes IGN] système de transport intelligent [Termes IGN] temps réel [Termes IGN] véhicule automobile [Termes IGN] zone urbaine Résumé : (auteur) Real-time positioning in suburban and urban environments has been a challenging task for many Intelligent Transportation Systems (ITS) applications. In these environments, positioning using Global Navigation Satellite Systems (GNSS) cannot provide continuous solutions due to the blockage of signals in harsh scenarios. Consequently, it is intrinsic to have an independent positioning system capable of providing accurate and reliable positional solutions over GNSS outages. This study exploits the integration of Light Detection and Ranging (LiDAR), gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. Real field data, collected by a moving land vehicle, is used to test the presented algorithm. Three simulated GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results show that using the proposed algorithm can achieve a promising navigation performance in urban environments. In addition, it is shown that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The horizontal errors over the first outage, with less density of surroundings, reached 7.74 m (0.43%) error with a mean value of 3.15 m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 4.97 m (0.28%) and 3.99 m (0.23%), with mean values of 2.25 m and 1.89 m, respectively. Numéro de notice : A2023-110 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2022-0022 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.1515/jag-2022-0022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102469 [article]

High-precision positioning using plane-constrained RTK method in urban environments / Chen Zhuang in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

Public [article]  inNavigation : journal of the Institute of navigation > vol 69 n° 4 (Fall 2022) . - n° 540 Titre : High-precision positioning using plane-constrained RTK method in urban environments Type de document : Article/Communication Auteurs : Chen Zhuang, Auteur ; Hongbo Zhao, Auteur ; Yuli He, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 540 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] ambiguïté entière [Termes IGN] antenne GNSS [Termes IGN] Chine [Termes IGN] estimateur [Termes IGN] filtre de Kalman [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement par GNSS [Termes IGN] Receiver Autonomous Integrity Monitoring [Termes IGN] résolution d'ambiguïté [Termes IGN] véhicule [Termes IGN] zone urbaine Résumé : (auteur) High-precision positioning methods have drawn great attention in recent years due to the rapid development of smart vehicles as well as automatics driving technology. The Real-Time Kinematic (RTK) technique is a mature tool to achieve centimeter-level positioning accuracy in open-sky areas. However, the users who drive under dense urban conditions are always confronted with harsh global navigation satellite system (GNSS) environments. Skyscrapers and overpasses block the signals and reduce the number of visible satellites, making it difficult to achieve continuous and precise positioning. Considering that the road is relatively smooth in most urban areas, vehicles are expected to travel on the same plane when they are close to each other. The road plane information is a promising candidate to enhance the performance of the RTK method in constrained environments. In this paper, we propose a plane-constrained RTK (PCRTK) method using the positioning information from cooperative vehicles. In a vehicle-to-vehicle (V2V) network, the positions of cooperative vehicles are used to fit a road plane for the target vehicle. The parameters of the plane fitting are treated as new measurements to enhance the performance of the float estimator. The relationship between the plane parameters and the state of the estimator is derived in our study. To validate the performance of the proposed method, several experiments with a four-vehicle fleet were carried out in open-sky areas and dense urban areas in Beijing, China. Simulations and experimental results show that the proposed method can take advantage of the plane constraint and obtain more accurate positioning results compared to the traditional RTK method. Numéro de notice : A2020-917 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.33012/navi.540 Date de publication en ligne : 14/07/2022 En ligne : https://doi.org/10.33012/navi.540 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102444 [article]

A semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)  

Permalink

Urban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])  

Permalink

Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])  

Permalink

A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])  

Permalink

Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Permalink

Comparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Permalink

Estimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Permalink

Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Permalink

Machine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)  

Permalink

Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)  

Permalink