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CIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica, vol 26 n° 1 (January 2022)
[article]
Titre : CIME: Context-aware geolocation of emergency-related posts Type de document : Article/Communication Auteurs : Gabriele Scalia, Auteur ; Chiara Francalanci, Auteur ; Barbara Pernici, Auteur Année de publication : 2022 Article en page(s) : pp 125 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] cartographie d'urgence
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploration de données
[Termes IGN] géolocalisation
[Termes IGN] géoréférencement
[Termes IGN] Grande-Bretagne
[Termes IGN] implémentation (informatique)
[Termes IGN] inondation
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] prise en compte du contexte
[Termes IGN] tempête
[Termes IGN] TwitterRésumé : (auteur) Information extracted from social media has proven to be very useful in the domain of emergency management. An important task in emergency management is rapid crisis mapping, which aims to produce timely and reliable maps of affected areas. During an emergency, the volume of emergency-related posts is typically large, but only a small fraction is relevant and help rapid mapping effectively. Furthermore, posts are not useful for mapping purposes unless they are correctly geolocated and, on average, less than 2% of posts are natively georeferenced. This paper presents an algorithm, called CIME, that aims to identify and geolocate emergency-related posts that are relevant for mapping purposes. While native geocoordinates are most often missing, many posts contain geographical references in their metadata, such as texts or links that can be used by CIME to filter and geolocate information. In addition, social media creates a social network and each post can be enhanced with indirect information from the post’s network of relationships with other posts (for example, a retweet can be associated with other geographical references which are useful to geolocate the original tweet). To exploit all this information, CIME uses the concept of context, defined as the information characterizing a post both directly (the post’s metadata) and indirectly (the post’s network of relationships). The algorithm was evaluated on a recent major emergency event demonstrating better performance with respect to the state of the art in terms of total number of geolocated posts, geolocation accuracy and relevance for rapid mapping. Numéro de notice : A2022-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-021-00446-x Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1007/s10707-021-00446-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100011
in Geoinformatica > vol 26 n° 1 (January 2022) . - pp 125 - 157[article]Classification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)
[article]
Titre : Classification of mediterranean shrub species from UAV point clouds Type de document : Article/Communication Auteurs : Juan Pedro Carbonell-Rivera, Auteur ; Jesus Torralba, Auteur ; Javier Estornell, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 199 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] arbuste
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] Espagne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] forêt méditerranéenne
[Termes IGN] image captée par drone
[Termes IGN] incendie de forêt
[Termes IGN] indice de végétation
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de terrain
[Termes IGN] parc naturel
[Termes IGN] photogrammétrie aérienne
[Termes IGN] semis de pointsRésumé : (auteur) Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models. Numéro de notice : A2022-057 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14010199 En ligne : https://doi.org/10.3390/rs14010199 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99462
in Remote sensing > vol 14 n° 1 (January-1 2022) . - n° 199[article]Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)
[article]
Titre : Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China Type de document : Article/Communication Auteurs : Huijuan Zhang, Auteur ; Yingxu Song, Auteur ; Shiluo Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aléa
[Termes IGN] apprentissage automatique
[Termes IGN] base de données localisées
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] effondrement de terrain
[Termes IGN] modèle de simulation
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression logistique
[Termes IGN] risque naturel
[Termes IGN] Trois Gorges, barrage desRésumé : (auteur) This study aims to investigate the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation. Wanzhou section of the Three Gorges Reservoir area, China, frequently suffering numerous landslides, is chosen as an example. The class-weighted algorithm focuses on the class-imbalanced issue of landslide and non-landslide samples, and it can turn the class-imbalanced issue into a cost-sensitive machine learning by setting unequal weights for different classes, which contribute to improving the accuracy of landslide susceptibility evaluation. The landslide inventory database was produced by field investigation and remote sensing images derived from Google Earth. Of the 233 landslides in the inventory, 40% were used for validation, and the remaining 60% were used for training purposes. Twelve environmental parameters (elevation, slope, aspect, curvature, distance to river, NDVI, NDWI, rainfall, seismic intensity, land use, TRI, lithology) were treated as inputs of the models to produce a landslide susceptibility map (LSM). The AUC value, Balanced accuracy, and Geometric mean score were utilized to estimate the quality of models. The result shows that the weighted models (weighted logistic regression (WLR), weighted LightGBM (WLightGBM), weighted random forest (WRF) have higher AUC values, Balanced accuracy, and Geometric mean scores than those of unweighted methods, which demonstrates that the weighted models exhibit better than unweighted models, with the WRF model having the best performance. The landslide susceptibility map of the Wanzhou section displays that the high and very high landslide susceptibility zones are mainly distributed on both sides of the river. The insights from this research will be useful for ameliorating the landslide susceptibility mapping and the prevention and mitigation for the Wanzhou section. Numéro de notice : A2022-029 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104966Get rights and content Date de publication en ligne : 27/10/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104966Get rights and content Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99268
in Computers & geosciences > vol 158 (January 2022) . - n° 104966[article]Construction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique / Houssayn Meriche (2022)
Titre : Construction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique Type de document : Mémoire Auteurs : Houssayn Meriche, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 51 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] carte thématique
[Termes IGN] classification dirigée
[Termes IGN] format GeoTIFF
[Termes IGN] ilot thermique urbain
[Termes IGN] module d'extension
[Termes IGN] Montréal (Québec)
[Termes IGN] Python (langage de programmation)
[Termes IGN] QGIS
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) L’Institut des Sciences de l’Environnement (ISE) est une unité multi départementale de l’Université du Québec à Montréal (UQAM) dans laquelle les sciences de l’environnement ont pour objets les problématiques environnementales découlant des interactions entre processus biologiques, physiques, sociaux et humains. Montréal étant connue dans le monde de la recherche pour sa productivité scientifique dans le domaine de l’Intelligence Artificielle, on retrouve au sein de l’UQAM bon nombre d’étudiants mêlant cette discipline à d’autres champs scientifiques dont l’environnement. C’est ainsi que je suis amené à concevoir un plugin qui, couplé à série d’algorithmes faisant intervenir de l’apprentissage profond, permettrait à une étudiante en maîtrise de Géographie de générer des cartes de prédiction d’îlots de chaleur urbains de la ville de Montréal. Cet ensemble d’algorithmes est réalisé à partir du langage de programmation Python, avec pour support du plugin le logiciel QGIS. Celui-ci est destiné à traiter des images au format exclusif GeoTIFF, et nécessite également des connaissances en fabrication de masque (image binaire constituée de 0 et de 1 renseignant sur la pertinence d’exploitation des pixels de l’image GeoTIFF). Note de contenu : Introduction
1. Contexte du projet
1.1 L’Université du Québec à Montréal (UQAM)
1.2 L’environnement de travail
2. Analyse de l’existant
2.1 Autour des îlots de chaleur en milieu urbain
2.2 L’apprentissage automatique appliqué à la Télédétection
3. Construction du plugin
3.1 Côté Plugin
3.2 Côté Classification
3.3 Résultats et discussions
ConclusionNuméro de notice : 26869 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : Laboratoire de télédétection et de SIG du département de Géographie (Université du Québec à Montréal) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101696 Documents numériques
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Construction d’un Plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique - pdf auteurAdobe Acrobat PDF Contribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)
Titre : Contribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery Type de document : Thèse/HDR Auteurs : Calimanut-Ionut Cira, Auteur Editeur : Madrid [Espagne] : Universidad politécnica de Madrid Année de publication : 2022 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat en Topographie, Géodésie et cartographie, Universidad politécnica de MadridLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal artificiel
[Termes IGN] route
[Termes IGN] segmentation sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. Remote sensing experts have been actively using deep neural networks to solve object extraction tasks in high-resolution aerial imagery by means of supervised operations. However, the extraction operation is imperfect, due to the nature of remotely sensed images (noise, obstructions, etc.), the limitations of sensing resolution, or the occlusions often present in the scenes. The road network plays an important part in transportation and, nowadays, one of the main related challenges is keeping the existent cartographic support up to date. This task can be considered very challenging due to the complex nature of the geospatial object (continuous, with irregular geometry, and significant differences in width). We also need to take into account that secondary roads represent the largest part of the road transport network, but due to the absence of clearly defined edges, and the different spectral signatures of the materials used for pavement, monitoring, and mapping them represents a great effort for public administration, and their extraction is often omitted altogether. We believe that recent advancements in machine vision can enable a successful extraction of the road structures from high-resolution, remotely sensed imagery and a greater automation of the road mapping operation. In this PhD thesis, we leverage recent computer vision advances and propose a deep learning-based end-to-end solution, capable of efficiently extracting the surface area of roads at a large scale. The novel approach is based on a disjoint execution of three different image processing operations (recognition, semantic segmentation, and post-processing with conditional generative learning) within a common framework. We focused on improving the state-of-the-art results for each of the mentioned components, before incorporating the resulting models into the proposed solution architecture. For the recognition operation, we proposed two framework candidates based on convolutional neural networks to classify roads in openly available aerial orthoimages divided in tiles of 256×256 pixels, with a spatial resolution of 0.5 m. The frameworks are based on ensemble learning and transfer learning and combine weak classifiers to leverage the strengths of different state-of-the-art models that we heavily modified for computational efficiency. We evaluated their performance on unseen test data and compared the results with those obtained by the state-of-the-art convolutional neural networks trained for the same task, observing improvements in performance metrics of 2-3%. Secondly, we implemented hybrid semantic segmentation models (where the default backbones are replaced by neural network specialised in image segmentation) and trained them with high-resolution remote sensing imagery and their correspondent ground-truth masks. Our models achieved mean increases in performance metrics of 2.7-3.5%, when compared to the original state-of-the-art semantic segmentation architectures trained from scratch for the same task. The best-performing model was integrated on a web platform that handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format, and the generation of GeoTIFF results (compatible with geospatial databases). Thirdly, the road surface area extraction task is generally carried out via semantic segmentation over remotely sensed imagery—however, this supervised learning task can be considered very costly because it requires remote sensing images labelled at pixel level and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). We consider that unsupervised learning (not requiring labelled data) can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. For this reason, we also approached the post-processing of the road surface areas obtained with the best performing segmentation model to improve the initial segmentation predictions. In this line, we proposed two post-processing operations based on conditional generative learning for deep inpainting and image-to-image translation operations and trained the networks to learn the distribution of the road network present in official cartography, using a novel dataset covering representative areas of Spain. The first proposed conditional Generative Adversarial Network (cGAN) model was trained for deep inpainting operation and obtained improvements in performance metrics of maximum 1.3%. The second cGAN model was trained for image-to-image translation, is based on a popular model heavily modified for computational efficiency (a 92.4% decrease in the number of parameters in the generator network and a 61.3% decrease in the discriminator network), and achieved a maximum increase of 11.6% in performance metrics. We also conducted a qualitative comparison to visually assess the effectiveness of the generative operations and observed great improvements with respect to the initial semantic segmentation predictions. Lastly, we proposed an end-to-end processing strategy that combines image classification, semantic segmentation, and post-processing operations to extract containing road surface area extraction from high-resolution aerial orthophotography. The training of the model components was carried out on a large-scale dataset containing more than 537,500 tiles, covering approximately 20,800 km2 of the Spanish territory, manually tagged at pixel level. The consecutive execution of the resulting deep learning models delivered higher quality results when compared to state-of-the-art implementations trained for the same task. The versatility and flexibility of the solution given by the disjointed execution of the three separate sub-operations proved its effectiveness and economic efficiency and enables the integration of a web application that alleviates the manipulation of geospatial data, while allowing for an easy integration of future models and algorithms. Resuming, applying the proposed models resulted from this PhD thesis translates to operations aimed to check if the latest existing aerial orthoimages contains the studied continuous geospatial element, to obtain an approximation of its surface area using supervised learning and to improve the initial segmentation results with post-processing methods based on conditional generative learning. The results obtained with the proposed end-to-end-solution presented in this PhD thesis improve the state-of-the-art in the field of road extraction with deep learning techniques and prove the appropriateness of applying the proposed extraction workflow for a more robust and more efficient extraction operation of the road transport network. We strongly believe that the processing strategy can be applied to enhance other similar extraction tasks of continuous geospatial elements (such as the mapping of riverbeds, or railroads), or serve as a base for developing additional extraction workflows of geospatial objects from remote sensing images. Note de contenu : 1- Introduction
2- Methodology
3- Theoretical framework
4- Litterature review
5- Road recognition: A framework based on nestion of convolutional neuronal networks and transfer learning to regognise road elements
6- Road segmentation: An approach based on hybrid semantic segmentation models to extract the surface area of rod elements from aerial orthoimagery
7- Post-processing of semantic segmentation predictions I: A conditional generative adversial network to improve the extraction of road surface areas via deep inpainting operations
8- Post-processing of semantic segmentation predictions II: A lightweight conditional generative adversial network to improve the extraction of road surface areas via image-to-image translation
9- An end-to-end road extraction solution based on regonition, segmentation, and post-processing operations for a large-scale mapping of the road transport network from aerial orthophotography
10- ConclusionsNuméro de notice : 24069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Topographie, Géodésie et cartographie : Universidad politécnica de Madrid : 2022 DOI : 10.20868/UPM.thesis.70152 En ligne : https://doi.org/10.20868/UPM.thesis.70152 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102113 PermalinkCultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 2022)PermalinkDeep image translation with an affinity-based change prior for unsupervised multimodal change detection / Luigi Tommaso Luppino in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPermalinkDeep learning based 2D and 3D object detection and tracking on monocular video in the context of autonomous 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Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)PermalinkNumérique versus symbolique : dialogue ontologique entre deux approches / Hélène Mathian in Revue internationale de géomatique, vol 31 n° 1-2 (janvier - juin 2022)PermalinkOptimization of deep neural networks: A functional perspective with applications in image classification / Simon Roburin (2022)PermalinkPhotogrammetric point clouds: quality assessment, filtering, and change detection / Zhenchao Zhang (2022)PermalinkPredicting AIS reception using tropospheric propagation forecast and machine learning / Zackary Vanche (2022)PermalinkA prediction model for surface deformation caused by underground mining based on spatio-temporal associations / Min Ren in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkProceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021. 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