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Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6494 - 6518 Titre : Raster-based method for building selection in the multi-scale representation of two-dimensional maps Type de document : Article/Communication Auteurs : Yilang Shen, Auteur ; Tinghua Ai, Auteur ; Rong Zhao, Auteur Année de publication : 2022 Article en page(s) : pp 6494 - 6518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] bâtiment [Termes IGN] densité du bâti [Termes IGN] distribution spatiale [Termes IGN] données matricielles [Termes IGN] représentation cartographique 2D [Termes IGN] représentation multiple [Termes IGN] segmentation [Termes IGN] superpixel [Termes IGN] triangulation de Delaunay [Vedettes matières IGN] Généralisation Résumé : (auteur) In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method for raster building selection is proposed. In this method, to preserve the distribution range, a new homogeneous linear spectral clustering (HLSC) superpixel segmentation method is developed for the relatively homogeneous spatial division of building groups. Then, to preserve the relative distribution density, multi-level spatial division is performed according to the local number of buildings. Finally, to preserve the local geometric, attributive and geographical characteristics, four selection strategies, namely, the minimum centroid distance, minimum boundary distance, maximum area and considering geographical element strategies, are designed to generate selection results. To evaluate the proposed method, dispersed buildings in a suburban area are utilized to perform selection tasks. The experimental results indicate that the proposed method can effectively select dispersed irregular buildings at different levels of detail while maintaining the original distribution range and relative distribution density. In addition, the use of multiple selection strategies considering various geometric, attributive and geographical characteristics provides multiple options for cartography. Numéro de notice : A2022-727 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1943007 Date de publication en ligne : 29/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1943007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101673 [article]

An estimation method to reduce complete and partial nonresponse bias in forest inventory / James A. Westfall in European Journal of Forest Research, vol 141 n° 5 (October 2022)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 5 (October 2022) . - pp 901 - 907 Titre : An estimation method to reduce complete and partial nonresponse bias in forest inventory Type de document : Article/Communication Auteurs : James A. Westfall, Auteur Année de publication : 2022 Article en page(s) : pp 901 - 907 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] enquête [Termes IGN] erreur systématique [Termes IGN] estimateur [Termes IGN] estimation statistique [Termes IGN] Etats-Unis [Termes IGN] incertitude des données [Termes IGN] inventaire forestier étranger (données) [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de simulation [Termes IGN] placette d'échantillonnage [Termes IGN] post-stratification de données [Termes IGN] propriété foncière [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Survey practitioners commonly encounter various types of nonresponse and strive to implement methods that mitigate any resulting bias when reporting results. In national forest inventories (NFI), complete or partial nonresponse usually results from hazardous conditions or lack of plot access permission. While many factors may be related to nonresponse, the two primary factors in the NFI of the USA are public/private land ownership and office/field plot status. To ameliorate potential nonresponse bias, these factors should be accounted for in the estimation process. An estimation method is presented where response homogeneity groups (RHGs) account for differential nonresponse rates between forest/nonforest plots. In a post-stratified estimation context, ratio-to-size estimators are used in RHGs within post-strata to avoid potential bias in variance estimates arising from partial plot nonresponse. Combining RHGs within post-strata requires a complex variance estimator that includes four sources of uncertainty. Testing of the estimation method on a synthetic population showed the approach is essentially unbiased. Application to NFI data from 10 states in the USA consistently showed the RHG method produced state-level estimates of forestland area that were 0.1%–3.6% larger than the current post-stratified estimation procedure. It is suggested that these differences are indicative of the nonresponse bias present when plots having differential nonresponse rates are not accounted for. Numéro de notice : A2022-759 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1007/s10342-022-01480-6 Date de publication en ligne : 14/07/2022 En ligne : https://doi.org/10.1007/s10342-022-01480-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101770 [article]

Application of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2009-2034 Titre : Application of a graph convolutional network with visual and semantic features to classify urban scenes Type de document : Article/Communication Auteurs : Yongyang Xu, Auteur ; Shuai Jin, Auteur ; Zhanlong Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2009-2034 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] matrice de co-occurrence [Termes IGN] OpenStreetMap [Termes IGN] Pékin (Chine) [Termes IGN] point d'intérêt [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau routier [Termes IGN] scène urbaine Résumé : (auteur) Urban scenes consist of visual and semantic features and exhibit spatial relationships among land-use types (e.g. industrial areas are far away from the residential zones). This study applied a graph convolutional network with neighborhood information (henceforth, named the neighbour supporting graph convolutional neural network), to learn spatial relationships for urban scene classification. Furthermore, a co-occurrence analysis with visual and semantic features proceeded to improve the accuracy of urban scene classification. We tested the proposed method with the fifth ring road of Beijing with an overall classification accuracy of 0.827 and a Kappa coefficient of 0.769. In comparison with other methods, such as support vector machine, random forest, and general graph convolutional network, the case study showed that the proposed method improved about 10% in urban scene classification. Numéro de notice : A2022-740 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2048834 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2048834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101717 [article]

Assessing logging residues availability for energy production by using forest management plans data and geographic information system (GIS) / Luca Nonini in European Journal of Forest Research, vol 141 n° 5 (October 2022)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 5 (October 2022) . - pp 959 - 977 Titre : Assessing logging residues availability for energy production by using forest management plans data and geographic information system (GIS) Type de document : Article/Communication Auteurs : Luca Nonini, Auteur ; Calogero Schillaci, Auteur ; Marco Fiala, Auteur Année de publication : 2022 Article en page(s) : pp 959 - 977 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] données localisées [Termes IGN] estimation statistique [Termes IGN] exploitation forestière [Termes IGN] gestion forestière [Termes IGN] Italie [Termes IGN] planification [Termes IGN] système d'information géographique Résumé : (auteur) The aim of the work was to quantify the mass of logging residues (branches and tops; t yr−1 dry matter, DM) for energy generation starting from Forest Management Plans (FMP) data. The methodology was applied to public stands of an Italian district (area: 3.60 × 104 ha; period: 2009–2018). Compared to the previous preliminary analysis, the potentially available residues were computed considering forest accessibility and road traversability, by combining FMPs data with a geographic information system (GIS). New issues that were assessed here were: (i) representation of stands consisting of multiple disconnected parts; (ii) calculation of producible residues by using different values of biomass expansion factors (Scenario 1, S1; Scenario 2, S2). The potentially available residues computed for the analyzed period were used to quantify the current sustainable supply. Then, the potentially generated heat (thermal energy, TE; GJ yr−1) and electricity (EE; GJ yr−1), and the potentially avoided CO2 emissions into the atmosphere (EM; t yr−1 CO2) related to the final combustion process were computed by assuming that the current supply of residues was used as woodchips in a local centralized heating plant currently operating. For both S1 and S2, the large difference between the potentially producible and the potentially available residues demonstrated that geodata are essential for reliable estimations. Moreover, as the required information for the GIS analysis can be easily found in databases made available by forestry authorities, the proposed approach can be applied also to other areas; this could be helpful to support local decision-makers in defining sustainable practices for residues recovery. Numéro de notice : A2022-760 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s10342-022-01484-2 Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1007/s10342-022-01484-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101772 [article]

Augmented reality for scene text recognition, visualization and reading to assist visually impaired people / Imene Ouali in Procedia Computer Science, vol 207 (2022)  

Public [article]  inProcedia Computer Science > vol 207 (2022) . - pp 158 - 167 Titre : Augmented reality for scene text recognition, visualization and reading to assist visually impaired people Type de document : Article/Communication Auteurs : Imene Ouali, Auteur ; Mohamed Ben Halima, Auteur ; Ali Wali, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] enquête [Termes IGN] personne malvoyante [Termes IGN] réalité augmentée [Termes IGN] reconnaissance de caractères [Termes IGN] signalisation routière [Termes IGN] visualisation Résumé : (auteur) Reading traffic signs while driving a car for visually impaired people and people with visual problems is a very difficult task for them. This task is encountered every day, sometimes incorrect reading of traffic signs can lead to very serious results. In particular, the Arabic language is very difficult, making recognizing and viewing Arabic text a difficult task. In this context, we are looking for an effective solution to remove errors and results that can sometimes end someone's life. This article aims to correctly read traffic signs with Arabic text using augmented reality technology. Our system is composed of three modules. The first is text detection and recognition. The second is Text visualization. The third is Text to speech methods conversion. With this system, the user can have two different results. The first result is visual with much-improved text and enhancement. The second result is sound, he can hear the text aloud. This system is very applicable and effective for daily life. To assess the effectiveness of our work, we offer a survey to a group of visually impaired people to give their opinion on the use of our application. The results have been good for most people. Numéro de notice : A2023-010 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1016/j.procs.2022.09.048 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.procs.2022.09.048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102119 [article]

Canopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality / Jordi Margalef- Marrase in Forest ecology and management, vol 521 (October-1 2022)  

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Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images : A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])  

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Challenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)  

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Comparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)  

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Detecting overmature forests with airborne laser scanning (ALS) / Marc Fuhr in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)  

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A determination of the motion based on GNSS observations between 2000 and 2021 using the IGS points in the polar regions / Atinç Pirti in Geodesy and cartography, vol 48 n° 3 (October 2022)  

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Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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Estimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

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Estimation of ionospheric total electron content using GNSS observations derived from a smartphone / Li Xu in GPS solutions, vol 26 n° 4 (October 2022)  

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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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GNSS best integer equivariant estimation combining with integer least squares estimation: an integrated ambiguity resolution method with optimal integer aperture test / Liye Ma in GPS solutions, vol 26 n° 4 (October 2022)  

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Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)  

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Incremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)  

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Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)  

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Investigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)  

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Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

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Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)  

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Monitoring spatiotemporal soil moisture changes in the subsurface of forest sites using electrical resistivity tomography (ERT) / Julian Fäth in Journal of Forestry Research, vol 33 n° 5 (October 2022)  

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Multi‑constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography / Wei Zhou in GPS solutions, vol 26 n° 4 (October 2022)  

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