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Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1217 - 1235 Titre : Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1217 - 1235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification de Dempster-Shafer [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] fond marin [Termes descripteurs IGN] Google Earth [Termes descripteurs IGN] habitat d'espèce [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] précision infrapixellaire [Termes descripteurs IGN] récif corallien Résumé : (auteur) This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping. Numéro de notice : A2021-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637466 date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97803 [article]

Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 174 - 193 Titre : Detecting high-temperature anomalies from Sentinel-2 MSI images Type de document : Article/Communication Auteurs : Yongxue Liu, Auteur ; Zhi Weifeng, Auteur ; Bihua Xu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 174 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] anomalie thermique [Termes descripteurs IGN] éruption volcanique [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] incendie [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] risque technologique [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température au sol Résumé : (Auteur) High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites. Numéro de notice : A2021-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.008 date de publication en ligne : 23/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97808 [article]

Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Canada [Termes descripteurs IGN] centre urbain [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] crue [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] inondation [Termes descripteurs IGN] profondeur [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] système d'aide à la décision [Termes descripteurs IGN] traitement d'image [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

A framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1276 - 1291 Titre : A framework for classification of volunteered geographic data based on user’s need Type de document : Article/Communication Auteurs : Nazila Mohammadi, Auteur ; Amin Sedaghat, Auteur Année de publication : 2021 Article en page(s) : pp 1276 - 1291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] indicateur de qualité [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] zone urbaine Résumé : (auteur) VGI is an attractive source of data, but the quality assurance limits its usages. This study proposes a framework to estimate the quality of the VGI and to classify them based on the user’s need. For this purpose, a set of properties is defined to describe the data in various aspects. The principal component analysis (PCA) method is applied to reach a new set of uncorrelated indicators (UI). Volunteered data is classified based on the user’s need and takes a quality index (QI). UI and QI values are used to train the ANN. Finally, the trained ANN determines the output of the network in a way that returns QI using the UI as inputs. The proposed method was applied to estimate the quality classes of VGI in a part of an urban area. According to the results of the confusion matrix, the total accuracy of the proposed framework was 81.6%. Numéro de notice : A2021-436 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1641562 date de publication en ligne : 16/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1641562 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97806 [article]

A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases / Chun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 38 - 56 Titre : A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases Type de document : Article/Communication Auteurs : Chun Yang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 38 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] classification automatique d'objets [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] jointure [Termes descripteurs IGN] objet géographique [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] utilisation du sol Résumé : (Auteur) Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels. The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics. Numéro de notice : A2021-370 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.022 date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97774 [article]

Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility / Qingqing Chen in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics / Somayeh Dodge in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

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A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers / N. Orouji in GPS solutions, vol 25 n° 3 (July 2021)  

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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation / Bing Liu in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

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The presence of shade-intolerant conifers facilitates the regeneration of Quercus petraea in mixed stands / Jeremy Borderieux in Forest ecology and management, vol 491 (1 July 2021)  

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Towards generating network of bikeways from Mapillary data / Xuan Ding in Computers, Environment and Urban Systems, vol 88 (July 2021)  

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Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)  

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3D reconstruction of bridges from airborne laser scanning data and cadastral footprints / Steffen Goebbels in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)  

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