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Correcting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France / Michaela Nováková in Remote sensing of environment, vol 280 (October 2022)  

Public [article]  inRemote sensing of environment > vol 280 (October 2022) . - n° 113210 Titre : Correcting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France Type de document : Article/Communication Auteurs : Michaela Nováková, Auteur ; Michal Gallay, Auteur ; Jozef Šupinský, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113210 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] amélioration du contraste [Termes IGN] Ariège (09) [Termes IGN] cartographie géologique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage du bruit [Termes IGN] grotte [Termes IGN] intensité lumineuse [Termes IGN] lithologie [Termes IGN] roche [Termes IGN] semis de points [Termes IGN] télémétrie laser terrestre Résumé : (auteur) Active remote sensing by laser scanning (LiDAR) has markedly improved the mapping of a cave environment with an unprecedented level of accuracy and spatial detail. However, the use of laser intensity simultaneously recorded during the scanning of caves remains unexplored despite it having promising potential for lithological mapping as it has been demonstrated by many applications in open-sky conditions. The appropriate use of laser intensity requires calibration and corrections for influencing factors, which are different in caves as opposed to the above-ground environments. Our study presents an efficient and complex workflow to correct the recorded intensity, which takes into consideration the acquisition geometry, micromorphology of the cave surface, and the specific atmospheric influence previously neglected in terrestrial laser scanning. The applicability of the approach is demonstrated on terrestrial LiDAR data acquired in the Gouffre Georges, a cave located in the northern Pyrenees in France. The cave is unique for its geology and lithology allowing for observation, with a spectacular continuity without any vegetal cover, of the contact between marble and lherzolite rocks and tectonic structures that characterize such contact. The overall accuracy of rock surface classification based on the corrected laser intensity was over 84%. The presence of water or a wet surface introduced bias of the intensity values towards lower values complicating the material discrimination. Such conditions have to be considered in applications of the recorded laser intensity in mapping underground spaces. The presented method allows for putting geological observations in an absolute spatial reference frame, which is often very difficult in a cave environment. Thus, laser scanning of the cave geometry assigned with the corrected laser intensity is an invaluable tool to unravel the complexity of such a lithological environment. Numéro de notice : A2022-775 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113210 Date de publication en ligne : 10/08/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113210 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101807 [article]

Assessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])  

Public [article]  inGeocarto international > vol 37 n° 10 [01/06/2022] . - pp 2795 - 2820 Titre : Assessing and mapping landslide susceptibility using different machine learning methods Type de document : Article/Communication Auteurs : Osman Orhan, Auteur ; Suleyman Sefa Bilgilioglu, Auteur ; Zehra Kaya, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2795 - 2820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] effondrement de terrain [Termes IGN] lithologie [Termes IGN] pente [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] séparateur à vaste marge [Termes IGN] Turquie Résumé : (auteur) The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. Numéro de notice : A2022-594 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1837258 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1837258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101298 [article]

Lithological mapping based on fully convolutional network and multi-source geological data / Ziye Wang in Remote sensing, vol 13 n° 23 (December-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 23 (December-1 2021) . - n° 4860 Titre : Lithological mapping based on fully convolutional network and multi-source geological data Type de document : Article/Communication Auteurs : Ziye Wang, Auteur ; Renguang Zuo, Auteur ; Hao Liu, Auteur Année de publication : 2021 Article en page(s) : n° 4860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] carte géologique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données géologiques [Termes IGN] fusion de données multisource [Termes IGN] Himalaya [Termes IGN] lithologie [Termes IGN] segmentation sémantique Résumé : (auteur) Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping. Numéro de notice : A2021-878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13234860 Date de publication en ligne : 30/11/2021 En ligne : https://doi.org/10.3390/rs13234860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99146 [article]

Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([01/05/2021])  

Public [article]  inGeocarto international > vol 36 n° 8 [01/05/2021] . - pp 888 - 902 Titre : Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed Type de document : Article/Communication Auteurs : H.S. Virupaksha, Auteur ; K.N. Lokesh, Auteur Année de publication : 2021 Article en page(s) : pp 888 - 902 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aquifère [Termes IGN] bassin hydrographique [Termes IGN] carte des pentes [Termes IGN] carte hydrogéologique [Termes IGN] eau souterraine [Termes IGN] géomorphologie locale [Termes IGN] Karnataka (Inde) [Termes IGN] lithologie [Termes IGN] occupation du sol [Termes IGN] potentiel hydrogène [Termes IGN] précipitation [Termes IGN] résistivité [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol Résumé : (auteur) Electrical resistivity method and RS & GIS techniques are very much useful in identification of potential aquifer zones for exploitation, management and recharge of groundwater. Vertical Electrical Soundings are conducted at 35 locations in Gurpur watershed using Schlumberger array. The thematic layers like porosity, transmissivity and hydraulic conductivity are prepared using electrical resistivity data. Total of 13 thematic layers are used for vector integration and identification of Groundwater Potential Zones (GWPZ). The numerical weights and ranks are assigned to the themes based on their relationship with groundwater. The findings shows that the depth to bedrock varies from 9.1 to 44.4 m and most of the mid land and low land region shows moderate to high depths of about 25–44 m. The GWPZ are classified into five classes namely, Very Good (≈21.02 km2), Good (≈231.35 km2), Moderate (≈420.76 km2), Poor (≈185.05 km2) and Very Poor (≈19.56 km2). The Good and Moderate categories cover ≈75% of total area. Numéro de notice : A2021-483 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624986 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624986 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97442 [article]

Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([15/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 3 [15/02/2021] . - pp 262 - 280 Titre : Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification Type de document : Article/Communication Auteurs : Fei Chen, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 262 - 280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] carte d'utilisation du sol [Termes IGN] Chine [Termes IGN] classification et arbre de régression [Termes IGN] désertification [Termes IGN] données spatiotemporelles [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] karst [Termes IGN] lithologie Résumé : (auteur) Karst Rocky Desertification (KRD) has become the most serious ecological disaster in Southwest China. We used the data of Thematic Mapper (TM) images from 1990, 1995, 2000, 2004, and 2011 and the 2016 Operational Land Imager (OLI) image. These sensing images were pre-processed by removing non-karst areas based on lithology and cutting away the land types impossibly generating KRD from land use maps. Then, we used a Classification And Regression Tree (CART) to classify the KRD. We want to improve the interpretation accuracy of KRD through the above steps. The results were as follows: (1) The KRD experiences the evolution process of ‘first deterioration and then improvement’. The rate is −4.94 km2.a−1 over a period of 1990 to 2004, and the rate is 36.48 km2.a−1 from 2004 to 2016; (2) The most influential factors causing KRD formation are the lithology and the resident population density, with contribution rates of 30.17% and 25.86%, respectively. Numéro de notice : A2021-140 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595175 Date de publication en ligne : 18/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595175 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97036 [article]

Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India / Subrata Mondal in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction / Torstein Fjeldstad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Cartographie de l’aléa érosif dans le bassin sud du Litani-Liban / Hussein El Hage Hassan in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)  

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Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])  

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Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran / Morteza Safari in Geocarto international, vol 33 n° 11 (November 2018)  

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Assessing image processing techniques for geological mapping: a case study in Eljufra, Libya / N.M. Saadi in Geocarto international, vol 24 n° 3 (June - July 2009)  

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N-FindR method versus independent component analysis for lithological identification in hyperspectral imagery / C. Gomez in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)  

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Usage of ERS SAR data over the Singhbhum shear zone, India for structural mapping and tectonic studies / S.K. Pal in Geocarto international, vol 22 n° 4 (December 2007 - January 2008)  

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Identifying erosion areas at basin scale using remote sensing data and GIS: a case study in a geologically complex mountain basin in the Spanish Pyrenees / S. Begueria in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)  

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Elaboration d'un plan d'occupation des sols dans un secteur du Tigray (Ethiopie) / Muriel Nouguier (2006)

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