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Spatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic / Nicolas Francos in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : Spatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic Type de document : Article/Communication Auteurs : Nicolas Francos, Auteur ; Asa Gholizadeh, Auteur ; Eyal Ben-Dor, Auteur Année de publication : 2022 Article en page(s) : pp 610 - 620 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] autocorrélation spatiale
[Termes IGN] contamination
[Termes IGN] distribution spatiale
[Termes IGN] image infrarouge
[Termes IGN] interpolation spatiale
[Termes IGN] krigeage
[Termes IGN] plomb
[Termes IGN] qualité du sol
[Termes IGN] République TchèqueRésumé : (auteur) For decades, the Příbram district in the Czech Republic has been affected by industrial and mining activities, which are the main sources of heavy metal pollutants and negatively affect soil quality. A recent study examined visible–near-infrared (VNIR), shortwave-infrared (SWIR), and X-ray fluorescence (XRF) spectroscopy to model soil lead (Pb) content in a selected area located in Příbram. Following that study, and using the data, we examined the spatial distribution of Pb content in the soil, with a combination of traditional techniques (Moran’s I, hotspot analysis, and Kriging). One of the novel points of this work is the use of the Getis–Ord hotspot analysis before the execution of Kriging interpolation to better emphasize clustering patterns. The results indicated that Pb was a spatially dependent soil property and through extensive in-situ sampling, it was possible to generate an accurate interpolation model. The high-Pb hotspots coincided with topographic obstacles that were modeled using topographic profiles extracted from Google Earth, indicating that Pb content does not always exhibit a direct relationship with topographic height as a result of runoff, due to the contribution of topographic steps. This observation provides a new perspective on the relationship between Pb content and topographic patterns. Numéro de notice : A2022-872 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2039786 Date de publication en ligne : 23/02/2022 En ligne : https://doi.org/10.1080/19475705.2022.2039786 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102166
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 610 - 620[article]Unsupervised generative models for data analysis and explainable artificial intelligence / Mohanad Abukmeil (2022)
Titre : Unsupervised generative models for data analysis and explainable artificial intelligence Type de document : Thèse/HDR Auteurs : Mohanad Abukmeil, Auteur ; Vincenzo Piuri, Directeur de thèse Editeur : Milan [Italie] : Università di Milano Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat spécialité Informatique, Université de MilanLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] allocation de Dirichlet latente
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] modèle stochastique
[Termes IGN] navigation autonome
[Termes IGN] reconstruction d'image
[Termes IGN] réseau antagoniste génératif
[Termes IGN] séparation aveugle de sourceRésumé : (auteur) For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reduction play a substantial role in decomposing the exploratory factors and learning the hidden structures of data, which encompasses the significant features that characterize the categories and trends among data samples in an ordered manner. That is by extracting patterns, differentiating trends, and testing hypotheses to identify anomalies, learning compact knowledge, and performing many different machine learning (ML) tasks such as classification, detection, and prediction. Unsupervised generative learning (UGL) methods are a class of ML characterized by their possibility of analyzing and decomposing latent data, reducing dimensionality, visualizing the manifold of data, and learning representations with limited levels of predefined labels and prior assumptions. Furthermore, explainable artificial intelligence (XAI) is an emerging field of ML that deals with explaining the decisions and behaviors of learned models. XAI is also associated with UGL models to explain the hidden structure of data, and to explain the learned representations of ML models. However, the current UGL models lack large-scale generalizability and explainability in the testing stage, which leads to restricting their potential in ML and XAI applications. To overcome the aforementioned limitations, this thesis proposes innovative methods that integrate UGL and XAI to enable data factorization and dimensionality reduction to improve the generalizability of the learned ML models. Moreover, the proposed methods enable visual explainability in modern applications as anomaly detection and autonomous driving systems. The main research contributions are listed as follows:
* A novel overview of UGL models including blind source separation (BSS), manifold learning (MfL), and neural networks (NNs). Also, the overview considers open issues and challenges among each UGL method.
* An innovative method to identify the dimensions of the compact feature space via a generalized rank in the application of image dimensionality reduction.
* An innovative method to hierarchically reduce and visualize the manifold of data to improve the generalizability in limited data learning scenarios, and computational complexity reduction applications.
* An original method to visually explain autoencoders by reconstructing an attention map in the application of anomaly detection and explainable autonomous driving systems.
The novel methods introduced in this thesis are benchmarked on publicly available datasets, and they outperformed the state-of-the-art methods considering different evaluation metrics. Furthermore, superior results were obtained with respect to the state-of-the-art to confirm the feasibility of the proposed methodologies concerning the computational complexity, availability of learning data, model explainability, and high data reconstruction accuracy.Note de contenu : 1- Introduction
2- State of the art of unsupervised generative learning (UGL) models
3- Research challenges and open issues of UGL models
4- UGL models for dimensionality reduction and XAI
5- Conclusion and future worksNuméro de notice : 15307 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de doctorat : Informatique : Milan : 2022 DOI : 10.13130/abukmeil-mohanad_phd2022-01-24 En ligne : http://dx.doi.org/10.13130/abukmeil-mohanad_phd2022-01-24 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99965 Deep learning for toponym resolution: Geocoding based on pairs of toponyms / Jacques Fize in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : Deep learning for toponym resolution: Geocoding based on pairs of toponyms Type de document : Article/Communication Auteurs : Jacques Fize, Auteur ; Ludovic Moncla , Auteur ; Bruno Martins, Auteur Année de publication : 2021 Article en page(s) : n° 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] échantillonnage
[Termes IGN] géocodage
[Termes IGN] matrice de co-occurrence
[Termes IGN] site wiki
[Termes IGN] toponyme
[Termes IGN] zone d'intérêtRésumé : (auteur) Geocoding aims to assign unambiguous locations (i.e., geographic coordinates) to place names (i.e., toponyms) referenced within documents (e.g., within spreadsheet tables or textual paragraphs). This task comes with multiple challenges, such as dealing with referent ambiguity (multiple places with a same name) or reference database completeness. In this work, we propose a geocoding approach based on modeling pairs of toponyms, which returns latitude-longitude coordinates. One of the input toponyms will be geocoded, and the second one is used as context to reduce ambiguities. The proposed approach is based on a deep neural network that uses Long Short-Term Memory (LSTM) units to produce representations from sequences of character n-grams. To train our model, we use toponym co-occurrences collected from different contexts, namely textual (i.e., co-occurrences of toponyms in Wikipedia articles) and geographical (i.e., inclusion and proximity of places based on Geonames data). Experiments based on multiple geographical areas of interest—France, United States, Great-Britain, Nigeria, Argentina and Japan—were conducted. Results show that models trained with co-occurrence data obtained a higher geocoding accuracy, and that proximity relations in combination with co-occurrences can help to obtain a slightly higher accuracy in geographical areas with fewer places in the data sources. Numéro de notice : A2021-927 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article DOI : 10.3390/ijgi10120818 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.3390/ijgi10120818 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99293
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 818[article]The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)
[article]
Titre : The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space Type de document : Article/Communication Auteurs : Renato César Dos santos, Auteur ; Mauricio Galo, Auteur ; André C. Carrilho, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 499 - 513 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme de Otsu
[Termes IGN] analyse de groupement
[Termes IGN] Brésil
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données multitemporelles
[Termes IGN] espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] planéité
[Termes IGN] semis de points
[Termes IGN] seuillageRésumé : (auteur) Building change detection techniques are essential for several urban applications. In this context, multi-temporal airborne LiDAR data has been considered an effective alternative since it has some advantages over conventional photogrammetry. Despite several works in the literature, the automatic class definition with great accuracy and performance remains a challenge in change detection. The developed strategies usually explore training samples or empirical thresholds to discriminate the classes. To overcome this limitation, we proposed an automatic building change detection method based on Otsu algorithm and median planarity attribute computed from eigenvalues. The main contribution corresponds to the automatic and unsupervised identification of building changes. The experiments were conducted using airborne LiDAR data from two epochs: 2012 and 2014. From qualitative and quantitative analysis, the robustness of the proposed method in detecting building changes in urban areas was evaluated, presenting completeness and correctness around 99% and 76%, respectively. Numéro de notice : A2021-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-021-00371-6 Date de publication en ligne : 24/04/2021 En ligne : https://doi.org/10.1007/s12518-021-00371-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99062
in Applied geomatics > vol 13 n° 4 (December 2021) . - pp 499 - 513[article]Access to urban parks: Comparing spatial accessibility measures using three GIS-based approaches / Siqin Wang in Computers, Environment and Urban Systems, vol 90 (November 2021)
[article]
Titre : Access to urban parks: Comparing spatial accessibility measures using three GIS-based approaches Type de document : Article/Communication Auteurs : Siqin Wang, Auteur ; Mingshu Wang, Auteur ; Yan Liu, Auteur Année de publication : 2021 Article en page(s) : n° 101713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] accessibilité
[Termes IGN] analyse en composantes principales
[Termes IGN] Australie
[Termes IGN] corrélation
[Termes IGN] distance
[Termes IGN] interaction spatiale
[Termes IGN] parc urbain
[Termes IGN] Pays-Bas
[Termes IGN] planification urbaine
[Termes IGN] transport urbainRésumé : (auteur) Urban parks are essential components of urban ecosystems, providing recreation and relaxation places to residents. Measuring the spatial accessibility to urban parks serves as an initial step in urban planning and developing urban development strategies to improve social and environmental justice. This study aims to evaluate measures of spatial accessibility to urban parks by comparing three geographic information systems (GIS)-based approaches, accounting for network complexity, transport modes, distance thresholds, and destination choices. Taking Ipswich City (Australia) and Enschede (the Netherlands) as two testbeds, we examine the spatial patterns of a total of 21 accessibility measures in the two cities and conduct a correlation and principal component analysis to unravel the interrelationship between these measures. The results suggest that among all measures under the three approaches, the selection of distance thresholds and transport modes matter more to accessibility measures than the destination choices. Furthermore, when distance threshold and transport mode are held constant, the network-based and entrance-based methods provide more realistic accessibility measures than other methods. We also discuss the generality of the entrance-based method we propose and suggest ways to choose the most appropriate accessibility measure for use in different contexts. Numéro de notice : A2021-698 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101713 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101713 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98558
in Computers, Environment and Urban Systems > vol 90 (November 2021) . - n° 101713[article]A quantitative comparison of regionalization methods / Orhun Aydun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkSpatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkExploring fuzzy local spatial information algorithms for remote sensing image classification / Anjali Madhu in Remote sensing, vol 13 n° 20 (October-2 2021)PermalinkA feature based change detection approach using multi-scale orientation for multi-temporal SAR images / R. Vijaya Geetha in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkSpatial structure system of land use along urban rail transit based on GIS spatial clustering / Yu Gao in European journal of remote sensing, vol 54 sup 2 (2021)PermalinkUnsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])PermalinkSpatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland / Eva Bianchi in Forest ecology and management, vol 494 (August-15 2021)PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkLeaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkTowards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)Permalink