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A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 249-262 Titre : A novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Ting Bai, Auteur ; Kaimin Sun, Auteur ; Wenzhuo Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 249-262 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] classe d'objets [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] segmentation multi-échelle Résumé : (Auteur) A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data. Numéro de notice : A2021-332 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.249 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.249 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97528 [article]

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L'oeil de l'espace / Anonyme in Géomètre, n° 2190 (avril 2021)

Public [article]  inGéomètre > n° 2190 (avril 2021) . - pp 45 - 45 Titre : L'oeil de l'espace Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2021 Article en page(s) : pp 45 - 45 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] acquisition d'images [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] droit foncier [Termes descripteurs IGN] image aérienne Résumé : (Auteur) Plus rien n'échappe à la télédétection. S'il est envisageable de se cacher derrière une clôture, ce n'est plus possible depuis le ciel ou l'espace. Numéro de notice : A2021-325 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 07/04/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97483 [article]

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Characterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 10 - 23 Titre : Characterizing urban land changes of 30 global megacities using nighttime light time series stacks Type de document : Article/Communication Auteurs : Qiming Zheng, Auteur ; Qihao Weng, Auteur ; Ke Wang, Auteur Année de publication : 2021 Article en page(s) : pp 10 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aménagement foncier [Termes descripteurs IGN] analyse harmonique [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] éclairage public [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image VIIRS [Termes descripteurs IGN] mégalopole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Worldwide urbanization has brought about diverse types of urban land use and land cover (LULC) changes. The diversity of urban land changes, however, have been greatly under studied, since the major focus of past research has been on urban growth. In this study, we proposed a framework to characterize diverse urban land changes of 30 global megacities using monthly nighttime light time series from VIIRS data. First, we developed a Logistic-Harmonic model to fit VIIRS time series. Second, by leveraging the uniqueness of urban land change and nighttime light data, we incorporated temporal information of VIIRS time series and proposed a new classification scheme to produce monthly maps of built-up areas and to disentangle urban land changes into five categories. Third, we provided an in-depth analysis and comparison of urban land change patterns of the selected megacities. Results demonstrated that the Logistic-Harmonic model yielded a robust performance in fitting VIIRS time series. Temporal features based classification can not only significantly improve the mapping accuracy of built-up areas, especially for regions with heterogeneous built-up and non-built-up landscapes, but also promoted temporal consistency and classification efficiency. Urban land changes occurred in 51% of the built-up pixels of the megacities. Compared with urban growth, other types of urban land change, particularly land use intensification, contributed to an unexpectedly large proportion of the changes (83%). The findings of this study offer an insightful understanding on global urbanization processes in megacities, and evoke further investigation on the environmental and ecological implications of urban land changes. Numéro de notice : A2021-101 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.002 date de publication en ligne : 16/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.002 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96873 [article]

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Automatic filtering and 2D modeling of airborne laser scanning building point cloud / Fayez Tarsha-Kurdi in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 164 - 188 Titre : Automatic filtering and 2D modeling of airborne laser scanning building point cloud Type de document : Article/Communication Auteurs : Fayez Tarsha-Kurdi, Auteur ; Mohammad Awrangjeb, Auteur ; Nosheen Munir, Auteur Année de publication : 2021 Article en page(s) : pp 164 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] modélisation 2D [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] toit Résumé : (Auteur) This article suggests a new approach to automatic building footprint modeling using exclusively airborne LiDAR data. The first part of the suggested approach is the filtering of the building point cloud using the bias of the Z‐coordinate histogram. This operation aims to detect the points of roof class from the building point cloud. Hence, eight rules for histogram interpretation are suggested. The second part of the suggested approach is the roof modeling algorithm. It starts by detecting the roof planes and calculating their adjacency matrix. Hence, the roof plane boundaries are classified into four categories: (1) outer boundary; (2) inner plane boundaries; (3) roof detail boundaries; and (4) boundaries related to the missing planes. Finally, the junction relationships of roof plane boundaries are analyzed for detecting the roof vertices. With regard to the resulting accuracy quantification, the average values of the correctness and the completeness indices are employed in both approaches. In the filtering algorithm, their values are respectively equal to 97.5 and 98.6%, whereas they are equal to 94.0 and 94.0% in the modeling approach. These results reflect the high efficacy of the suggested approach. Numéro de notice : A2021-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12685 date de publication en ligne : 11/09/2020 En ligne : https://doi.org/10.1111/tgis.12685 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97154 [article]

Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 1 (January 2021) . - pp 33 - 42 Titre : Building extraction from Lidar data using statistical methods Type de document : Article/Communication Auteurs : Haval Abdul-Jabbar Sadeq, Auteur Année de publication : 2021 Article en page(s) : pp 33 - 42 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] semis de points Résumé : (Auteur) In this article, a straightforward, intuitive method for lidar data classification and building extraction, based on statistical analysis, is presented. The classification of the point cloud into ground and nonground is begun by individually testing each point within the point cloud using the statistical mean height. In this operation, various window sizes are specified, and the mean is obtained at each size. The points that are above the mean are saved and divided by the number of windows to obtain the proportion. Points are considered non-ground if their proportion is higher than the assigned threshold, and otherwise ground. An algorithm for classifying the obtained nonground point cloud into buildings and trees is also illustrated in this article. First the nonground points are labeled, then each label is tested individually. The process begins with segmenting each label. Then comes testing of whether each segment of points can be fitted within a specific plane. The label of the point cloud is considered a building if the number of segments considered as planes is larger than those considered as nonplanes; otherwise it is classified as a tree. Numéro de notice : A2021-055 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.14358/PERS.87.1.33 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96760 [article]

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Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

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Building change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)  

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Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

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Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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Outlier detection and robust plane fitting for building roof extraction from LiDAR data / Emon Kumar Dey in International Journal of Remote Sensing IJRS, vol 41 n°16 (01-10 May 2020)  

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Building Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples / Ka Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)  

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An improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)  

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Learning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)  

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Remote sensing based building extraction / Mohammad Awrangjeb (2020)  

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