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Urban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method / Qiang Chen in Remote sensing, vol 13 n° 1 (January-1 2021)
[article]
Titre : Urban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method Type de document : Article/Communication Auteurs : Qiang Chen, Auteur ; Qianhao Cheng, Auteur ; Jinfei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 158 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse multicritère
[Termes IGN] analyse spectrale
[Termes IGN] construction
[Termes IGN] déchet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] gestion urbaine
[Termes IGN] image à très haute résolution
[Termes IGN] morphologie
[Termes IGN] Pékin (Chine)
[Termes IGN] segmentation hiérarchique
[Termes IGN] urbanisationRésumé : (auteur) With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation. Numéro de notice : A2021-073 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010158 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010158 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96809
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 158[article]Automated labeling of schematic maps by optimization with knowledge acquired from existing maps / Tian Lan in Transactions in GIS, Vol 24 n° 6 (December 2020)
[article]
Titre : Automated labeling of schematic maps by optimization with knowledge acquired from existing maps Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Zhilin Li, Auteur ; Qian Peng, Auteur ; Xinyu Gong, Auteur Année de publication : 2020 Article en page(s) : pp 1722 - 1739 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] automatisation
[Termes IGN] calcul d'itinéraire
[Termes IGN] Chine
[Termes IGN] données cartographiques
[Termes IGN] Hong-Kong
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau métropolitain
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Schematic maps are simplified representations of line networks, aiming to help people quickly and accurately perform route planning and orientation tasks. The automated generation of such maps is generally treated as an optimization problem. Most researchers prefer to optimize network layouts and name labels separately, because optimizing them simultaneously is still intractable. It is found that optimizing network layouts is extensively studied, while optimizing name labels is rarely considered. In the optimization of name labels, constraints can be established with rules from cartographic experts, literature (e.g., specification and technical documents), and/or existing maps. However, some rules from experts and literature cannot be explicitly and mathematically expressed. This study aims to develop an automated labeling method with rules from existing maps. We first acquire the rules (i.e., the potential positions and the preferences of these positions) from some existing schematic maps and then integrate them into an optimization algorithm. Experimental evaluation is conducted by a questionnaire in terms of “ease level of finding name labels,” “congestion level,” and “satisfaction level” using Tianjin and Hong Kong metro schematic maps and the labels of our method. The results show that the proposed method can automatically generate effective labels. Numéro de notice : A2020-769 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12671 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1111/tgis.12671 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96661
in Transactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1722 - 1739[article]Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)
[article]
Titre : Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale Type de document : Article/Communication Auteurs : Chen Yang, Auteur ; Qingming Zhan, Auteur ; Sihang Gao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 327 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] climat urbain
[Termes IGN] géomorphologie locale
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-OLI
[Termes IGN] image Terra-MODIS
[Termes IGN] image thermique
[Termes IGN] morphologie urbaine
[Termes IGN] processus gaussien
[Termes IGN] regroupement de données
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] Wuhan (Chine)
[Termes IGN] zonage (urbanisme)Résumé : (auteur) Land Surface Temperature (LST) derived from space-borne Thermal-infrared (TIR) sensors is a key parameter of urban climate studies. Current studies are inefficient to capture the spatial and temporal variations of LST for only one snapshot adopted at one time. Focusing on the characterization of the spatial and temporal of LST variations at local scales, the latent patterns, and morphological characteristics are extracted in this study. Technically, sixteen MODerate-resolution Imaging Spectroradiometer (MODIS) eight-day synthesized LST products (MYD11A2) in 2002, 2007, 2012, and 2017 are employed. First, the non-parametric Multi-Task Gaussian Process Model (MTGP) is used to extract the smooth and continuous Latent LST (LLST) patterns using one LST subset and its temporally adjacent images. Second, the Multi-Scale Shape Index (MSSI) is then applied to quantify the morphological characteristics at the optimal scale. Then, the LLST patterns and MSSI maps are clustered into multiple spatial categories. The specific clusters with the highest LLST and MSSI values are considered as local LLST hotspots. The Hotspots Weighted Mean Center (HSWMC) and standard deviation ellipse are adopted to further investigate the spatiotemporal change of hotspots orientation, direction, and trajectories. Results revealed that Impervious Surfaces (IS) composition is the most significant external forcing of local LST anomalies. The configuration factors (e.g., shape index, aggregation index) also have a noticeable local warming effect. This study represents a latent pattern and morphology-based framework for LST hotspots spatial and temporal variations characterization, catering to the zoning and grading strategies in urban planning. Numéro de notice : A2020-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1834882 Date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1834882 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96550
in Geo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 327 - 340[article]How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)
[article]
Titre : How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data Type de document : Article/Communication Auteurs : Chaogui Kang, Auteur ; Li Shi, Auteur ; Fahui Wang, Auteur ; Yu Liu, Auteur Année de publication : 2020 Article en page(s) : pp 1504 - 1525 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Chine
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données spatiotemporelles
[Termes IGN] ethnographie
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] matrice de co-occurrence
[Termes IGN] production participative
[Termes IGN] réseau social
[Termes IGN] site urbain
[Termes IGN] téléphonie mobile
[Termes IGN] urbanismeRésumé : (Auteur) This research attempts to build a unified framework for distinguishing the spatiotemporal visit patterns of urban places by different social groups using mobile phone data in Harbin, China. Social groups are detected by their social ties in the ego‐to‐ego mobile phone call network and are embedded in physical space according to their home locations. Popular urban places are detected from user‐generated content as the basic spatial analysis unit. Coupling subscribers’ footprints and urban places in physical space, the spatiotemporal visit patterns of urban places by distinct social groups are uncovered and interpreted by non‐negative matrix factorization. The proposed framework enables us to answer several critical questions from three perspectives: (1) How to model popular urban places in terms of vague boundary, land use, and semantic features based on crowdsourcing data?; (2) How to evaluate interaction between individuals for inspecting the relationship between spatial proximity and social ties based on spatiotemporal co‐occurrence?; and (3) How to distinguish urban place visit preferences for social groups associated with different socio‐demographic characteristics? Our research could assist urban planners and municipal managers to identify critical urban places frequented by different population groups according to their roles and social/cultural characteristics for improvement of urban facility allocation. Numéro de notice : A2020-767 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12654 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1111/tgis.12654 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96658
in Transactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1504 - 1525[article]A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
[article]
Titre : A novel intelligent classification method for urban green space based on high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhiyu Xu, Auteur ; Yi Zhou, Auteur ; Shixin Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3845 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace vert
[Termes IGN] image à haute résolution
[Termes IGN] image Gaofen
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pékin (Chine)
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] urbanismeRésumé : (auteur) The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Numéro de notice : A2020-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223845 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/rs12223845 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96565
in Remote sensing > vol 12 n° 22 (December-1 2020) . - n° 3845[article]STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkBuilding facade reconstruction using crowd-sourced photos and two-dimensional maps / Wu Jie in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)PermalinkSpatio-temporal evolution, future trend and phenology regularity of net primary productivity of forests in Northeast China / Chunli Wang in Remote sensing, vol 12 n° 21 (November 2020)PermalinkUnfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkMonitoring population dynamics in the Pearl River Delta from 2000 to 2010 / Sisi Yu in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkChoosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (October 2020)PermalinkCoupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)PermalinkA 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)PermalinkMapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkNetwork-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)Permalink