Descripteur
Termes IGN > informatique > intelligence artificielle > apprentissage automatique > apprentissage profond
apprentissage profond |
Documents disponibles dans cette catégorie (234)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Towards expressive graph neural networks : Theory, algorithms, and applications / Georgios Dasoulas (2022)
Titre : Towards expressive graph neural networks : Theory, algorithms, and applications Type de document : Thèse/HDR Auteurs : Georgios Dasoulas, Auteur ; Michalis Vazirgiannis, Directeur de thèse ; Aladin Virmaux, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2022 Note générale : bibliographie
These de doctorat de l’Institut Polytechnique de Paris préparée à l’Ecole Polytechnique, spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] entropie
[Termes IGN] graphe
[Termes IGN] isomorphisme
[Termes IGN] noeud
[Termes IGN] réseau neuronal de graphes
[Termes IGN] théorie des graphesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) As the technological evolution of machine learning is accelerating nowadays, data plays a vital role in building intelligent models, being able to simulate phenomena, predict values and make decisions. In an increasing number of applications, data take the form of networks. The inherent graph structure of network data motivated the evolution of the graph representation learning field. Its scope includes generating meaningful representations for graphs and their components, i.e., the nodes and the edges. The research on graph representation learning was accelerated with the success of message passing frameworks applied on graphs, namely the Graph Neural Networks. Learning informative and expressive representations on graphs plays a critical role in a wide range of real-world applications, from telecommunication and social networks, urban design, chemistry, and biology. In this thesis, we study various aspects from which Graph Neural Networks can be more expressive, and we propose novel approaches to improve their performance in standard graph learning tasks. The main branches of the present thesis include: the universality of graph representations, the increase of the receptive field of graph neural networks, the design of stable deeper graph learning models, and alternatives to the standard message-passing framework. Performing both theoretical and experimental studies, we show how the proposed approaches can become valuable and efficient tools for designing more powerful graph learning models.In the first part of the thesis, we study the quality of graph representations as a function of their discrimination power, i.e., how easily we can differentiate graphs that are not isomorphic. Firstly, we show that standard message-passing schemes are not universal due to the inability of simple aggregators to separate nodes with ambiguities (similar attribute vectors and neighborhood structures). Based on the found limitations, we propose a simple coloring scheme that can provide universal representations with theoretical guarantees and experimental validations of the performance superiority. Secondly, moving beyond the standard message-passing paradigm, we propose an approach for treating a corpus of graphs as a whole instead of examining graph pairs. To do so, we learn a soft permutation matrix for each graph, and we project all graphs in a common vector space, achieving a solid performance on graph classification tasks.In the second part of the thesis, our primary focus is concentrated around the receptive field of the graph neural networks, i.e., how much information a node has in order to update its representation. To begin with, we study the spectral properties of operators that encode adjacency information. We propose a novel parametric family of operators that can adapt throughout training and provide a flexible framework for data-dependent neighborhood representations. We show that the incorporation of this approach has a substantial impact on both node classification and graph classification tasks. Next, we study how considering the k-hop neighborhood information for a node representation can output more powerful graph neural network models. The resulted models are proven capable of identifying structural properties, such as connectivity and triangle-freeness.In the third part of the thesis, we address the problem of long-range interactions, where nodes that lie in distant parts of the graph can affect each other. In this problem, we either need the design of deeper models or the reformulation of how proximity is defined in the graph. Firstly, we study the design of deeper attention models, focusing on graph attention. We calibrate the gradient flow of the model by introducing a novel normalization that enforces Lipschitz continuity. Next, we propose a data augmentation method for enriching the node attributes with information that encloses structural information based on local entropy measures. Note de contenu : 1. Introduction
2. Preliminaries
I- Discrimination power
3. Universal approximation on graphs
4. Learning soft permutations for graph representations
II- Receptive field
5. Learning graph shift operators
6. Increasing the receptive field with multiple hops
III- Beyond local interactions
7. Lipschitz continuity of graph attention
8. Structural symmetries in graphs
9. Conclusions and outlookNuméro de notice : 24076 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de doctorat : Informatique : Palaiseau : 2022 DOI : sans En ligne : https://theses.hal.science/tel-03666690 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102200 Towards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)
Titre : Towards synthetic sensing for smart cities : a machine/deep learning-based approach Type de document : Thèse/HDR Auteurs : Faraz Malik Awan, Auteur ; Noël Crespi, Directeur de thèse ; Roberto Minerva, Directeur de thèse Editeur : Courcouronnes : Télécom SudParis Année de publication : 2022 Importance : 106 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l’Institut Polytechnique de Paris préparée à Telecom SudParis, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] Espagne
[Termes IGN] parking
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] pollution acoustique
[Termes IGN] pollution atmosphérique
[Termes IGN] réseau neuronal récurrent
[Termes IGN] système de transport intelligent
[Termes IGN] trafic routier
[Termes IGN] ville intelligenteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) We worked on one of the most significant research directions in Smart City, i.e., Intelligent Transportation System (ITS). ITS encapsulates several domains, such as electronic vehicles notification systems, traffic information, smart parking, and environment. However, in this thesis, we target two of its important domains; i) Smart Parking, and ii) Road Traffic. We started our research with Smart Parking use case. Performing literature review, we realized that different Machine Learning (ML) and Deep Learning (DL) approaches have been used for smart parking solutions. In most of these proposed approaches, enclosed parking areas were targeted with different feature sets to predict the "occupancy rate" in parking areas. It inspired us to conduct a comparative analysis to answer following questions; Given the parking prediction use case, how do the traditional ML models perform as compared to complex DL models? Provided big data, can less complex, traditional ML models outperform complex DL models? How well these models can perform to predict the availability of the individual on-street parking spots rather than predicting the overall occupancy rate of an enclosed parking area. To answer these questions, we choose five well-known classical ML algorithms (K-Nearest Neighbours, Random Forest, Decision Tree) and DL algorithm (Multilayer Perceptron). To take our investigation into depth, we train Ensemble Learning Model, in which we combine all the above-mentioned ML and DL models. A huge parking dataset of city of Santander, Spain, has been used which consists of around 25 million records. We also propose to recommend available parking spots based on the current location of the driver. Moving forward with our research goals, we performed literature review on road traffic and found road traffic associated with air pollution and noise pollution often. However, to the best of our knowledge, air pollution & noise pollution have never been use d in traffic prediction problem. In this part of our research, firstly we used air pollution (CO, NO, NO2, NOx, and O3) along with the atmospheric variables, such as wind speed, wind direction, temperature, and pressure to improve the traffic forecasting in the city of Madrid. This successful experiment motivated us to extend our investigation to another factor, which is also strongly correlated with road traffic i.e., noise pollution. Hence, as an extension of our previous work, in this part of our research, we use noise pollution to improve the traffic prediction in the city of Madrid. Note de contenu : 1- Introduction
2- Parking space prediction using classical ML and deep learning models
3- Road traffic prediction improvement using air pollution and atmospheric data
4- Using noise pollution to improve traffic prediction
5- Conclusion and future workNuméro de notice : 20025 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Telecom SudParis : 2022 Organisme de stage : SAMOVAR DOI : sans En ligne : https://tel.hal.science/tel-03722891/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101825 Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)
[article]
Titre : Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images Type de document : Article/Communication Auteurs : Xiao Li, Auteur ; Huan Ning, Auteur ; Xiao Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 32 - 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] carrefour
[Termes IGN] cartographie urbaine
[Termes IGN] couche thématique
[Termes IGN] exploration d'images
[Termes IGN] feu de circulation
[Termes IGN] image Streetview
[Termes IGN] Mapillary
[Termes IGN] réseau routier
[Termes IGN] segmentation d'image
[Termes IGN] signalisation routièreRésumé : (auteur) Auditing and mapping traffic infrastructure is a crucial task in urban management. For example, signalized intersections play an essential role in transportation management; however, effectively identifying these intersections remains unsolved. Traditionally, signalized intersection data are manually collected through field audits or checking street view images (SVIs), which is time-consuming and labor-intensive. This study proposes an effective protocol to identify signalized intersections using road networks and SVIs. First, we propose a six-step geoprocessing model to generate an intersection feature layer from road networks. Second, we utilize up to three nearest SVIs to capture streetscapes at each intersection. Then, a deep learning-based image segmentation model is adopted to recognize traffic light-related pixels from each SVI. Last, we design a post-processing step to generate new features characterizing SVIs’ segmentation results at each intersection and build a decision tree model to determine the traffic control type. Results demonstrate that the proposed protocol can effectively identify signalized intersections with an overall accuracy of 97.05%. It also proves the effectiveness of SVIs for auditing urban infrastructures. This study can directly benefit transportation agencies by providing a ready-to-use smart audit and mapping solution for large-scale identification and mapping of signalized intersections. Numéro de notice : A2022-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1080/15230406.2021.1992299 Date de publication en ligne : 16/11/2021 En ligne : https://doi.org/10.1080/15230406.2021.1992299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99148
in Cartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 32 - 49[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Efficient occluded road extraction from high-resolution remote sensing imagery / Dejun Feng in Remote sensing, vol 13 n° 24 (December-2 2021)
[article]
Titre : Efficient occluded road extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Dejun Feng, Auteur ; Xingyu Shen, Auteur ; Yakun Xie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4974 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de partie cachée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] reconstruction de routeRésumé : (auteur) Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas. Numéro de notice : A2021-889 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13244974 Date de publication en ligne : 07/12/2021 En ligne : https://doi.org/10.3390/rs13244974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99243
in Remote sensing > vol 13 n° 24 (December-2 2021) . - n° 4974[article]Automatic extraction of indoor spatial information from floor plan image: A patch-based deep learning methodology application on large-scale complex buildings / Hyunjung Kim in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : Automatic extraction of indoor spatial information from floor plan image: A patch-based deep learning methodology application on large-scale complex buildings Type de document : Article/Communication Auteurs : Hyunjung Kim, Auteur ; Seongyong Kim, Auteur ; Kiyun Yu, Auteur Année de publication : 2021 Article en page(s) : n° 828 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] indoorGML
[Termes IGN] positionnement en intérieur
[Termes IGN] reconstruction 3D du bâtiRésumé : (auteur) Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded. Numéro de notice : A2021-926 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120828 Date de publication en ligne : 10/12/2021 En ligne : https://doi.org/10.3390/ijgi10120828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99289
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 828[article]Building detection with convolutional networks trained with transfer learning / Simon Šanca in Geodetski vestnik, vol 65 n° 4 (December 2021 - February 2022)PermalinkDeep 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)PermalinkDiResNet: Direction-aware residual network for road extraction in VHR remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)PermalinkA hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds / Yetao Yang in Computers & geosciences, vol 157 (December 2021)PermalinkImproving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network / Yajie Shi in International Journal of Remote Sensing IJRS, vol 42 n° 23 (1-10 December 2021)PermalinkLithological mapping based on fully convolutional network and multi-source geological data / Ziye Wang in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkUnderstanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)PermalinkCrop rotation modeling for deep learning-based parcel classification from satellite time series / Félix Quinton in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkA comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkDownscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)Permalink