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Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery / Neema Nicodemus Lyimo in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 339-348 Titre : Quality assessment of heterogeneous training data sets for classification of urban area with Landsat imagery Type de document : Article/Communication Auteurs : Neema Nicodemus Lyimo, Auteur ; Fang Luo, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur Année de publication : 2021 Article en page(s) : pp 339-348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] corrélation d'images [Termes IGN] distance euclidienne [Termes IGN] données d'apprentissage [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] données ouvertes [Termes IGN] image Landsat [Termes IGN] incertitude des données [Termes IGN] jeu de données localisées [Termes IGN] qualité des données [Termes IGN] système à base de connaissances [Termes IGN] zone urbaine Résumé : (Auteur) Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification. Numéro de notice : A2021-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.339 date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.339 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97695 [article]

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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 375 - 396 Titre : Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis Type de document : Article/Communication Auteurs : Marta Sapena, Auteur ; Luis Angel Ruiz, Auteur Année de publication : 2021 Article en page(s) : pp 375 - 396 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse discriminante [Termes IGN] analyse spatio-temporelle [Termes IGN] carte d'occupation du sol [Termes IGN] carte d'utilisation du sol [Termes IGN] changement d'occupation du sol [Termes IGN] croissance urbaine [Termes IGN] distance euclidienne [Termes IGN] modèle de croissance végétale [Termes IGN] pondération Résumé : (auteur) The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas. Numéro de notice : A2021-040 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1817463 date de publication en ligne : 08/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1817463 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96752 [article]

Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 14 [15/10/2020] . - pp 1580 - 1602 Titre : Textural classification of remotely sensed images using multiresolution techniques Type de document : Article/Communication Auteurs : Rizwan Ahmed Ansari, Auteur ; Krishna Mohan Buddhiraju, Auteur ; Avik Bhattacharya, Auteur Année de publication : 2020 Article en page(s) : pp 1580 - 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse multirésolution [Termes IGN] analyse texturale [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de contours [Termes IGN] distance euclidienne [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] image RVB [Termes IGN] image satellite [Termes IGN] texture d'image [Termes IGN] transformation en ondelettes Résumé : (auteur) Multiresolution analysis (MRA) methods have been successfully used in texture analysis. Texture analysis is widely discussed in literature, but most of the methods which do not employ multiresolution strategy cannot exploit the fact that texture occurs at various spatial scales. This paper proposes a methodology to identify different classes in satellite images using texture features from newly developed multiresolution methods. The proposed method is tested on remotely sensed optical images and a Pauli RGB decomposed version of synthetic aperture radar image. The textural information is extracted at various scales and in different directions from curvelet and contourlet transforms. The results are compared with wavelet-based features. Accuracy assessment is performed and comparative analysis is carried out using minimum distance to mean, support vector machine and random forest classifiers. It is found that the proposed method shows better class discriminating power and classification capability as compared to existing wavelet-based method. Numéro de notice : A2020-618 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581263 date de publication en ligne : 15/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95994 [article]

Dynamic floating stations model for emergency medical services with a consideration of traffic data / Chih-Hong Sun in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 15 p. Titre : Dynamic floating stations model for emergency medical services with a consideration of traffic data Type de document : Article/Communication Auteurs : Chih-Hong Sun, Auteur ; Chen-Yang Cheng, Auteur ; Cheng-Hui Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse des besoins [Termes IGN] distance kilométrique [Termes IGN] durée de déplacement [Termes IGN] géolocalisation [Termes IGN] gestion urbaine [Termes IGN] migration pendulaire [Termes IGN] planification urbaine [Termes IGN] secours d'urgence [Termes IGN] système d'information géographique [Termes IGN] Taipei (Taïwan) [Termes IGN] temps de trajet [Termes IGN] trafic routier Résumé : (auteur) To equally distribute the workload and minimize the travel distance for fire departments, we developed a new dynamic floating stations model (DFSM) to target traffic-related emergency medical services (EMS) during peak hours. This study revealed that traffic-related EMS incidents have different characteristics to other EMS incidents. The number of floating stations was determined by the number of available ambulances at a given time. The optimum floating station location was identified by using the given capacity to establish the smallest service radius. In DFSM simulations using floating stations with a capacity of 100 and 150 EMS incidents, the result shows significant improvements in comparison to the current situation. Numéro de notice : A2020-296 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050336 date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.3390/ijgi9050336 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95134 [article]

An OD flow clustering method based on vector constraints: a case study for Beijing taxi origin-destination data / Xiaogang Guo in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 2 (February 2020) Titre : An OD flow clustering method based on vector constraints: a case study for Beijing taxi origin-destination data Type de document : Article/Communication Auteurs : Xiaogang Guo, Auteur ; Zhijie Xu, Auteur ; Jianqin Zhang, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par nuées dynamiques [Termes IGN] distance euclidienne [Termes IGN] données de flux [Termes IGN] données vectorielles [Termes IGN] erreur moyenne quadratique [Termes IGN] origine - destination [Termes IGN] Pékin (Chine) [Termes IGN] regroupement de données [Termes IGN] taxi [Termes IGN] trafic routier [Termes IGN] zone urbaine Résumé : (auteur) Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling relationship of urban subspace and dynamic causes. The existing flow clustering methods are limited by the spatial constraints of OD points, rely on the spatial similarity of geographical points, and lack in-depth analysis of high-dimensional flow characteristics, and therefore it is difficult to find irregular flow clusters. In this paper, we propose an OD flow clustering method based on vector constraints (ODFCVC), which defines OD flow event point and OD flow vector to express the spatial location relationship and geometric flow behavior characteristics of OD flow. First, the OD flow vector coordinate system is normalized by the Euclidean distance-based OD flow event point spatial clustering, and then the OD flow clusters with similar flow patterns are mined using adjusted cosine similarity-based OD flow vector feature clustering. The transformation of OD data from point set space to vector space is realized by constraining the vector coordinate system and vector similarity through two-step clustering, which simplifies the calculation of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space. Due to the OD flow cluster property, the k-means algorithm is selected as the basic clustering logic in the two-step clustering method, and a sum of squared error perceptually important points algorithm considering silhouette coefficients (SSEPIP) is adopted to automatically extract the optimal cluster number without defining any parameters. Tested by origin-destination flow data in Beijing, China, new traffic flow communities based on traffic hubs are obtained by using the ODFCVC method, and irregular traffic flow clusters (including cluster mode, divergence mode, and convergence mode) with representative travel trends are found. Numéro de notice : A2020-114 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9020128 date de publication en ligne : 22/02/2020 En ligne : https://doi.org/10.3390/ijgi9020128 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94720 [article]

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n° 1 (2020)  

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Embedding road networks and travel time into distance metrics for urban modelling / Henry Crosby in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

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Arpent : un prototype de haute exactitude pour les mesures de grande distance / Maylis Teyssendier de la Serve in XYZ, n° 154 (mars - mai 2018)

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Safe separation distance score : a new metric for evaluating wildland firefighter safety zones using lidar / Michael J. Campbell in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

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An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)  

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Continuously generalizing buildings to built-up areas by aggregating and growing / Dongliang Peng (2017)  

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Correction of terrestrial LiDAR intensity channel using Oren–Nayar reflectance model: An application to lithological differentiation / Dario Carrea in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)  

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Impact de l’anisotropie du milieu dans les études de profilage géographique / Marie Trotta in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)  

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An alternative method to constructing time cartograms for the visual representation of scheduled movement data / Rehmat Ullah in Journal of maps, vol 11 n° 4 ([01/08/2015])  

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Very high resolution image matching based on local features and k-means clustering / Amin Sedaghat in Photogrammetric record, vol 30 n° 150 (June - August 2015)  

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