Centre de documentation
scientifique

Cost distances and least cost paths respond differently to cost scenario variations: a sensitivity analysis of ecological connectivity modeling / Paul Savary in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 8 (August 2022) . - pp 1652-1676 Titre : Cost distances and least cost paths respond differently to cost scenario variations: a sensitivity analysis of ecological connectivity modeling Type de document : Article/Communication Auteurs : Paul Savary, Auteur ; Jean-Christophe Foltête, Auteur ; Stéphane Garnier, Auteur Année de publication : 2022 Article en page(s) : pp 1652-1676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] chemin le moins coûteux, algorithme du [Termes IGN] connexité (topologie) [Termes IGN] coût Résumé : (auteur) Biodiversity conservation measures designed to ensure ecological connectivity depend on the reliable modeling of species movements. Least-cost path modeling makes it possible to identify the most likely dispersal paths within a landscape and provide two items of ecological relevance: (i) the spatial location of these least-cost paths (LCPs) and (ii) the accumulated cost along them (’cost distance’, CD). This spatial analysis requires that cost values be assigned to every type of land cover. The sensitivity of both LCPs and CDs to the cost scenarios has not been comprehensively assessed across realistic landscapes and diverging cost scenarios. We therefore assessed it in diverse landscapes sampled over metropolitan France and with widely diverging cost scenarios. The spatial overlap of the LCPs was more sensitive to the cost scenario than the CD values were. In addition, highly correlated CD matrices can be derived from very different cost scenarios. Although the range of the cost values and the properties of each cost scenario significantly influenced the outputs of LCP modeling, landscape composition and configuration variables also explained their variations. Accordingly, we provide guidelines for the use of LCP modeling in ecological studies and conservation planning. Numéro de notice : A2022-614 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2014852 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2014852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101368 [article]

A geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 215 - 235 Titre : A geographically weighted artificial neural network Type de document : Article/Communication Auteurs : Julian Haguenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2022 Article en page(s) : pp 215 - 235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse de sensibilité [Termes IGN] Autriche [Termes IGN] coût [Termes IGN] évaluation foncière [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] régression géographiquement pondérée [Termes IGN] relation spatiale [Termes IGN] réseau neuronal artificiel Résumé : (auteur) While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting. Numéro de notice : A2022-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1871618 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1871618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99785 [article]

Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing / Zhi Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2262 - 2286 Titre : Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing Type de document : Article/Communication Auteurs : Zhi Zhang, Auteur ; Jing Li, Auteur ; Fung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2262 - 2286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] coût [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] marché foncier [Termes IGN] Pékin (Chine) [Termes IGN] régression géographiquement pondérée Résumé : (auteur) Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered. Numéro de notice : A2021-758 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1912348 Date de publication en ligne : 12/05/2021 En ligne : https://doi.org/10.1080/13658816.2021.1912348 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98773 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2021111	SL	Revue	Centre de documentation	Revues en salle	Disponible

A novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm / Sara Khanbani in Applied geomatics, vol 13 n° 1 (May 2021)  

Public [article]  inApplied geomatics > vol 13 n° 1 (May 2021) . - pp 89 - 105 Titre : A novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm Type de document : Article/Communication Auteurs : Sara Khanbani, Auteur ; Ali Mohammadzadeh, Auteur ; Milad Janalipour, Auteur Année de publication : 2021 Article en page(s) : pp 89 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alaska (Etats-Unis) [Termes IGN] algorithme génétique [Termes IGN] changement temporel [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] classification par nuées dynamiques [Termes IGN] coût [Termes IGN] détection de changement [Termes IGN] seuillage Résumé : (auteur) Change Detection (CD) problem from remotely sensed images is a popular topic among researchers. Because of the diversity in the problem of change detection and the complexity of the study areas it cannot be claimed that there is an appropriate and prevalent algorithm which is more effective for different types of the case study. As a fundamental investigation, it is critical to recognize the weaknesses of the state of artworks in change detection. Also, those examined weaknesses have to be improved aptly to develop a new strong method. This paper presents a thresholding algorithm improved by the Genetic Algorithm (GA) in CD problems, which focuses on minimizing a novel cost function. The suggested cost function can be adopted for local and global change variations in difference images without any prior assumptions. The presented algorithm was tested on two data sets (i.e., Alaska region and Uremia Lake) to validate its effectiveness. Experimental results demonstrated that the proposed algorithm in this work has improved the accuracy of change detection (changed pixel accuracy term) in the Alaska region about 8%–12% and also in Uremia Lake approximately between 8%–13% in comparison with other conventional methods including Fuzzy C- Means (FCM), Otsu thresholding, K-Means, and K-Medoid. Numéro de notice : A2021-237 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00323-6 Date de publication en ligne : 22/06/2020 En ligne : https://doi.org/10.1007/s12518-020-00323-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97246 [article]

Rapport d'activité 2020 de l'Institut National de l'Information Géographique et Forestière IGN, 2. Performance et responsabilité / Institut national de l'information géographique et forestière (2012 -) (2021)    

Public Titre de série : Rapport d'activité 2020 de l'Institut National de l'Information Géographique et Forestière IGN, 2 Titre : Performance et responsabilité Type de document : Rapport Auteurs : Institut national de l'information géographique et forestière (2012 -), Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2021 Importance : 27 p. Format : 21 x 30 cm Langues : Français (fre) Descripteur : [Vedettes matières IGN] Histoire IGN [Termes IGN] bilan comptable [Termes IGN] performance [Termes IGN] ressources humaines Index. décimale : 42.40 Histoire IGN Note de contenu : - Edito d'Emmanuelle Prada-Bordenave - Les missions et activités de l'IGN - Prise de parole de Jeanne Strausz secrétaire générale de l'IGN - Bilan financier et comptable 2020 - Interview de Anne Harlé directrice des ressources humaines de l'IGN Numéro de notice : 28621B Affiliation des auteurs : IGN (2020- ) Nature : Rapport d'activité DOI : sans En ligne : https://www.ign.fr/sites/default/files/2021-06/livret_ra_rh_2020.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99522

Voir aussi

• Rapport d'activité 2020 de l'Institut National de l'Information Géographique et Forestière IGN, 1. Activité / Institut national de l'information géographique et forestière (2012 -) (2021)    

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
28621-01B	42.40	Livre	Centre de documentation	Histoire	Disponible

Documents numériques

en open access Performance et responsabilité IGN 2020 Adobe Acrobat PDF

Reducing multipath effect of low-cost GNSS receivers for monitoring by considering temporal correlations / Li Zhang in Journal of applied geodesy, vol 14 n° 2 (April 2020)  

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Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system / Elahe S. Abdolkarimi in GPS solutions, vol 24 n° 2 (April 2020)  

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Learning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)  

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Tempête Xynthia à la Faute-sur-Mer : une analyse a posteriori de l’impact des « zones noires » et des alternatives possibles / Axel Creach in Norois, n° 251 ([01/01/2019])  

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Valorisation des données au sein des entreprises / Oumaima Ait Abbou (2019) 

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Spatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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Structural health monitoring using a GPS sensor network / Nicolas Manzini (2018)  

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The use of low-cost, single-frequency GNSS receivers in mapping surveys / M. Tsakiri in Survey review, vol 50 n° 358 (January 2018)  

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Economics of mapping using small manned and unmanned aerial vehicles / Orrin H. Thomas in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)  

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The cost(s) of geospatial open data / Peter A. Johnson in Transactions in GIS, vol 21 n° 3 (June 2017)  

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