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Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101834 Titre : Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators Type de document : Article/Communication Auteurs : Luis Izquierdo-Horna, Auteur ; Miker Damazo, Auteur ; Deyvis Yanayaco, Auteur Année de publication : 2022 Article en page(s) : n° 101834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] déchet [Termes IGN] densité de population [Termes IGN] données socio-économiques [Termes IGN] Pérou [Termes IGN] régression logistique [Termes IGN] zone urbaine Résumé : (auteur) In the last decades, the accumulation of municipal solid waste in urban areas has become a latent concern in our society due to its implications for the exposed population and the possible health and environmental issues it may cause. In this sense, this research study contributes to the timely identification of these sectors according to the anthropogenic characteristics of their residents as dictated by 10 social indicators (i.e., age, education, income, among others) sorted into three assessment categories (sociodemographic, sociocultural, and socioeconomic). Then, the data collected was processed and analyzed using two machine learning algorithms (random forest (RF) and logistic regression (LR)). The primary information that fed the machine learning model was collected through field visits and local/national reports. For this research, the Puente Piedra and Chaclacayo districts, both located in the province of Lima, Peru, were selected as case studies. Results suggest that the most relevant social indicators that help identifying these sectors are monthly income, consumption patterns, age, and household population density. The experiments showed that the RF algorithm has the best performance, since it efficiently identified 63% of the possible solid waste accumulation zones. In addition, both models were capable of determining different classes (AUC – RF = 0.65, AUC – LR = 0.71). Finally, the proposed approach is applicable and reproducible in different sectors of the national Peruvian territory. Numéro de notice : A2022-512 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101834 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101052 [article]

A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101807 Titre : A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Bo Zhou, Auteur ; Shuai Jin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] arbre aléatoire minimum [Termes IGN] distribution spatiale [Termes IGN] noeud [Termes IGN] Pékin (Chine) [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] réseau neuronal de graphes [Termes IGN] taxinomie [Termes IGN] trafic routier [Termes IGN] triangulation de Delaunay [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Land-use classification plays an important role in urban planning and resource allocation and had contributed to a wide range of urban studies and investigations. With the development of crowdsourcing technology and map services, points of interest (POIs) have been widely used for recognizing urban land-use types. However, current research methods for land-use classifications have been limited to extracting the spatial relationship of POIs in research units. To close this gap, this study uses a graph-based data structure to describe the POIs in research units, with graph convolutional networks (GCNs) being introduced to extract the spatial context and urban land-use classification. First, urban scenes are built by considering the spatial context of POIs. Second, a graph structure is used to express the scenes, where POIs are treated as graph nodes. The spatial distribution relationship of POIs is considered to be the graph's edges. Third, a GCN model is designed to extract the spatial context of the scene by aggregating the information of adjacent nodes within the graph and urban land-use classification. Thus, the land-use classification can be treated as a classification on a graphic level through deep learning. Moreover, the POI spatial context can be effectively extracted during classification. Experimental results and comparative experiments confirm the effectiveness of the proposed method. Numéro de notice : A2022-460 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101807 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100622 [article]

Graph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)  

Public [article]  inRemote sensing of environment > vol 274 (June 2022) . - n° 112993 Titre : Graph-based block-level urban change detection using Sentinel-2 time series Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Wei Li, Auteur ; Ran Tao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multivariée [Termes IGN] bâtiment [Termes IGN] Chine [Termes IGN] détection de changement [Termes IGN] espace vert [Termes IGN] extraction de traits caractéristiques [Termes IGN] graphe [Termes IGN] image Sentinel-MSI [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] segmentation d'image [Termes IGN] série temporelle [Termes IGN] zone urbaine Résumé : (auteur) Remote sensing technology has been frequently used to obtain information on changes in urban land cover because of its vast spatial coverage and timeliness of observation. Block-level change detection with high temporal resolution image data provides fine detail of urban changes, is suitable for urban management, and has gradually received widespread attention. High-dimensional features are required to express the heterogeneous structure of the blocks. High-dimensional high-frequency time series, namely, multivariate time series, are formed by arranging high-dimensional features chronologically. Classic change detection methods treat multivariate time series as univariate time series one by one. Few studies have analyzed the change in a multivariate time series by considering all variables as an entirety. Therefore, a graph-based segmentation for multivariate time series algorithm (MTS-GS) is proposed in this paper. Specifically, 1) we construct a similarity matrix to explore the changing patterns of multivariate time series for seasonal change, trend change, abrupt change, and noise disturbance; 2) a multivariate time series graph is defined based on the changing patterns; and 3) the corresponding graph segmentation algorithm is proposed in the paper to detect the abrupt and trend changes under noise and seasonal disturbances. Sentinel-2 images of the rapidly developing third-tier city of Luoyang, Henan province, China, are adopted to validate the algorithm. The F1-score in the spatial domain is 84.1%; the producer's and the user's accuracy in the temporal dimension are 81.8% and 80.1%, respectively. Seven change types are defined and extracted, showing the development pattern and the efficiency of land use in the city. Furthermore, the proposed MTS-GS can be used for pixel-level change detection and performs well under various time intervals and cloud covers. Numéro de notice : A2022-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112993 Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100699 [article]

Summarizing large scale 3D mesh for urban navigation / Imeen Ben Salah in Robotics and autonomous systems, vol 152 (June 2022)  

Public [article]  inRobotics and autonomous systems > vol 152 (June 2022) . - n° 104037 Titre : Summarizing large scale 3D mesh for urban navigation Type de document : Article/Communication Auteurs : Imeen Ben Salah, Auteur ; Sébastien Kramm, Auteur ; Cédric Demonceaux, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104037 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme ICP [Termes IGN] carte en 3D [Termes IGN] données lidar [Termes IGN] entropie [Termes IGN] image hémisphérique [Termes IGN] image RVB [Termes IGN] information sémantique [Termes IGN] localisation basée vision [Termes IGN] maillage [Termes IGN] navigation autonome [Termes IGN] précision géométrique (imagerie) [Termes IGN] précision radiométrique [Termes IGN] profondeur [Termes IGN] Rouen [Termes IGN] saillance [Termes IGN] zone urbaine Résumé : (auteur) Cameras have become increasingly common in vehicles, smartphones, and advanced driver assistance systems. The areas of application of these cameras in the world of intelligent transportation systems are becoming more and more varied: pedestrian detection, line crossing detection, navigation, …A major area of research currently focuses on mapping that is essential for localization and navigation. However, this step generates an important problem of memory management. Indeed, the memory space required to accommodate the map of a small city is measured in tens gigabytes. In addition, several providers today are competing to produce High-Definition (HD) maps. These maps offer a rich and detailed representation of the environment for highly accurate localization. However, they require a large storage capacity and high transmission and update costs. To overcome these problems, we propose a solution to summarize this type of map by reducing the size while maintaining the relevance of the data for navigation based on vision only. The summary consists in a set of spherical images augmented by depth and semantic information and allowing to keep the same level of visibility in every directions. These spheres are used as landmarks to offer guidance information to a distant agent. They then have to guarantee, at a lower cost, a good level of precision and speed during navigation. Some experiments on real data demonstrate the feasibility for obtaining a summarized map while maintaining a localization with interesting performances. Numéro de notice : A2022-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.robot.2022.104037 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1016/j.robot.2022.104037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100335 [article]

True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 178 (June 2022) . - pp 161 - 184 Titre : True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points Type de document : Article/Communication Auteurs : Mojdeh Ebrahimikia, Auteur ; Ali Hosseininaveh, Auteur Année de publication : 2022 Article en page(s) : pp 161 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie [Termes IGN] apprentissage profond [Termes IGN] détection de contours [Termes IGN] détection du bâti [Termes IGN] distorsion d'image [Termes IGN] graphe [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] orthophotographie [Termes IGN] orthophotoplan numérique [Termes IGN] photogrammétrie aérienne [Termes IGN] pixel de contour [Termes IGN] structure-from-motion [Termes IGN] zone urbaine Résumé : (auteur) After considering state-of-the-art algorithms, this paper presents a novel method for generating true orthophotos from unmanned aerial vehicle (UAV) images of urban areas. The procedure consists of four steps: 2D edge detection in building regions, 3D edge graph generation, digital surface model (DSM) modification and, finally, true orthophoto and orthomosaic generation. The main contribution of this paper is concerned with the first two steps, in which deep-learning approaches are used to identify the structural edges of the buildings and the estimated 3D edge points are added to the point cloud for DSM modification. Running the proposed method as well as four state-of-the-art methods on two different datasets demonstrates that the proposed method outperforms the existing orthophoto improvement methods by up to 50% in the first dataset and by 70% in the second dataset by reducing true orthophoto distortion in the structured edges of the buildings. Numéro de notice : A2022-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12409 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1111/phor.12409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101065 [article]

Exploring digital twin adaptation to the urban environment: comparison with CIM to avoid silo-based approaches / Adeline Deprêtre in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

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Calibration of a light hemispherical radiance field imaging system / Manchun Lei in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  

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Effects of climate and drought on stem diameter growth of urban tree species / Vjosa Dervishi in Forests, vol 13 n° 5 (May 2022)  

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The role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa / Xueqin Li in Sustainable Cities and Society, vol 80 (May 2022)  

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Clustering with implicit constraints: A novel approach to housing market segmentation / Xiaoqi Zhang in Transactions in GIS, vol 26 n° 2 (April 2022)  

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Detecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)  

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Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)  

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Simulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 (April 2022)  

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Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/03/2022])  

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Emerging technologies for smart cities’ transportation: Geo-information, data analytics and machine learning approaches / Li-Minn Ang in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

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