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Building fuzzy areal geographical objects from point sets / Jifa Guo in Transactions in GIS, vol 25 n° 6 (December 2021)
[article]
Titre : Building fuzzy areal geographical objects from point sets Type de document : Article/Communication Auteurs : Jifa Guo, Auteur ; Shihong Du, Auteur Année de publication : 2021 Article en page(s) : pp 3067 - 3087 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] habitat animal
[Termes IGN] habitat d'espèce
[Termes IGN] objet flou
[Termes IGN] objet géographique zonal
[Termes IGN] réserve naturelleRésumé : (Auteur) Representations of fuzzy geographic objects and analyses of their spatial relationships have received considerable attention in the GIS and spatial database domains over the past 30 years. However, building fuzzy geographical objects from real data is still a challenge. Simple fuzzy areal object models are too restrictive for many applications, and general fuzzy areal models may not be restrictive enough; as a result, the extent of fuzzy regions that satisfy the relevant conditions may be too large to affect the location description and spatial analysis. A condition by which the number of cores is not greater than one is added for the general object model, and an operable method for constructing fuzzy objects from the point set is proposed. Two peak and pass sets are determined for the membership surface by the fuzzy morphometric analysis method. The first set is used to initially divide the footprint of the fuzzy surface into smaller subfootprints, and the second set is used to merge insignificant fuzzy objects with their nearest significant fuzzy objects; thus, unreasonable division is avoided. Cross-validation is adopted to evaluate the generated fuzzy objects. An experiment is provided to verify the effectiveness of the proposed method. Numéro de notice : A2021-933 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12808 Date de publication en ligne : 10/10/2021 En ligne : https://doi.org/10.1111/tgis.12808 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99454
in Transactions in GIS > vol 25 n° 6 (December 2021) . - pp 3067 - 3087[article]A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)
[article]
Titre : A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping Type de document : Article/Communication Auteurs : Khalil Valizadeh Kamran, Auteur ; Bakhtiar Feizizadeh, Auteur ; Behnam Khorrami, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 837 - 851 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] fonction de base radiale
[Termes IGN] Iran
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] occupation du sol
[Termes IGN] pente
[Termes IGN] risque naturel
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (auteur) Landslides are among the most destructive natural hazards with severe socio-economic ramifications all around the world. Understanding the critical combination of geoenvironmental factors involved in the occurrence of landslides can mitigate the adverse impacts ascribed to them. Among the several scenarios for studying and investigating this phenomenon, landslide susceptibility mapping (LSM) is the most prominent method. Applying the machine learning (ML) algorithms integrated with the geographic information systems (GIS) has become a trending means for accurate and rapid landslide mapping practices in the scientific community. Support vector machine (SVM) has been the most commonly applied ML algorithm for LSM in recent years. The current study aims to implement different SVM kernel functions including polynomial kernel function (PKF) (degree 1 to 5), radial basis function (RBF), sigmoid, and linear kernels, for a GIS-based LSM over the Tabriz Basin (TB). To this end, a total number of 9 conditioning parameters being involved in the occurrence of the landslide events were determined and utilized. The LSM maps of the TB were generated based on the different SVM kernels and were statistically validated according to the landslide inventory. The findings revealed that the polynomial-degree-2 (PKF-2) model (AUC = 0.9688) outperforms the rest of the utilized kernels. According to the SLM map generated through PKF-2, the northernmost parts of the TB are extremely susceptible to slope failures than the rest; therefore, the developmental policies over these parts have to be taken into account with privileged priority to hinder any humanitarian as well as environmental catastrophes. Numéro de notice : A2021-858 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00393-0 Date de publication en ligne : 28/08/2021 En ligne : https://doi.org/10.1007/s12518-021-00393-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99066
in Applied geomatics > vol 13 n° 4 (December 2021) . - pp 837 - 851[article]Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])
[article]
Titre : Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images Type de document : Article/Communication Auteurs : Mohammad Hossein Gamshadzaei, Auteur ; Majid Rahimzadegan, Auteur Année de publication : 2021 Article en page(s) : pp 2264 - 2278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multibande
[Termes IGN] analyse spectrale
[Termes IGN] Arménie
[Termes IGN] bande infrarouge
[Termes IGN] cartographie thématique
[Termes IGN] détection d'objet
[Termes IGN] eau de surface
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Google Earth
[Termes IGN] image à haute résolution
[Termes IGN] image satellite
[Termes IGN] indice d'humidité
[Termes IGN] Iran
[Termes IGN] occupation du sol
[Termes IGN] optimisation par essaim de particules
[Termes IGN] polygoneRésumé : (auteur) Various spectral indices have been introduced to detect water extent from satellite images with different performances in various regions. The aim of this study is to provide an efficient index using particle swarm optimization (PSO) algorithm to detect water spread areas from satellite images with similar performance in different regions. This index is introduced for images containing water absorption bands from visible to middle infrared wavelengths. Eleven images were prepared from different satellites and water bodies with various environmental conditions. In addition, 40 pixels from water and 40 pixels from non-water regions were selected as training data for PSO algorithm. Results were evaluated using digitized polygons of water bodies on high-resolution images of Google Earth. The best results in PSO-based water index (PSOWI) were obtained by the combination of two bands (red and middle infrared). PSOWI represented proper performance in the selected various land covers and satellite images. Numéro de notice : A2021-831 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1700554 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/10106049.2019.1700554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99004
in Geocarto international > vol 36 n° 20 [01/12/2021] . - pp 2264 - 2278[article]Utility-pole detection based on interwoven column generation from terrestrial mobile Laser scanner data / Siamak Talebi Nahr in Photogrammetric record, Vol 36 n° 176 (December 2021)
[article]
Titre : Utility-pole detection based on interwoven column generation from terrestrial mobile Laser scanner data Type de document : Article/Communication Auteurs : Siamak Talebi Nahr, Auteur ; Mohammad Saadatseresht, Auteur Année de publication : 2021 Article en page(s) : pp 402 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] équipement collectif
[Termes IGN] exactitude des données
[Termes IGN] exhaustivité des données
[Termes IGN] lidar mobile
[Termes IGN] mur
[Termes IGN] objet géographique complexe
[Termes IGN] objet géographique urbain
[Termes IGN] partitionnement par bloc
[Termes IGN] qualité des données
[Termes IGN] réseau électrique
[Termes IGN] scène urbaineRésumé : (Auteur) Mobile lidar scanning is one of the recent technologies that is used to map street scenes rapidly. Among street objects, utility-poles are more critical to energy companies to monitor regularly through time. This paper presents a novel approach to detect utility-poles from mobile lidar data in complex city scenes. After removing ground points, the scene is gridded into blocks based on a shared-partitioning algorithm. Next, an interwoven column generation algorithm is used to create columns. Finally, each of these columns is considered to be a utility-pole or not. The proposed algorithm is tested on two test areas. The algorithm achieved Completeness, Correctness and Quality of 92.8%, 97.5% and 90.6% in Area 1, and 92.8%, 92.2% and 86.1% in Area 2. The total number of utility-poles in both areas was 265. The algorithm shows promising results in utility-pole detection in complex city scenes with attached walls. Numéro de notice : A2021-916 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12394 Date de publication en ligne : 10/12/2021 En ligne : https://doi.org/10.1111/phor.12394 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99331
in Photogrammetric record > Vol 36 n° 176 (December 2021) . - pp 402 - 424[article]Exploring fuzzy local spatial information algorithms for remote sensing image classification / Anjali Madhu in Remote sensing, vol 13 n° 20 (October-2 2021)
[article]
Titre : Exploring fuzzy local spatial information algorithms for remote sensing image classification Type de document : Article/Communication Auteurs : Anjali Madhu, Auteur ; Anil Kumar, Auteur ; Peng Jia, Auteur Année de publication : 2021 Article en page(s) : n° 4163 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification pixellaire
[Termes IGN] distance euclidienne
[Termes IGN] erreur moyenne quadratique
[Termes IGN] Inde
[Termes IGN] matrice d'erreur
[Termes IGN] occupation du sol
[Termes IGN] théorie des possibilitésRésumé : (auteur) Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes. Numéro de notice : A2021-806 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204163 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.3390/rs13204163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98864
in Remote sensing > vol 13 n° 20 (October-2 2021) . - n° 4163[article]Complexity-based matching between image resolution and map scale for multiscale image-map generation / Qian Peng in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)PermalinkFlood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model / Gaurav Talukdar in Natural Hazards, vol 109 n° 1 (October 2021)PermalinkA typification method for linear building groups based on stroke simplification / Xiao Wang in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkDigital building-height preparation from satellite stereo images / P.S. 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