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Affiner la recherche Interroger des sources externesDeep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)
Titre : Deep learning for wildfire progression monitoring using SAR and optical satellite image time series Type de document : Thèse/HDR Auteurs : Puzhao Zhang, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 100 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-91-7873-935-6 Note générale : bibliographie
Doctoral Thesis in GeoinformaticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alberta (Canada)
[Termes IGN] apprentissage profond
[Termes IGN] bande C
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] détection de changement
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie de forêt
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] zone sinistréeRésumé : (auteur) Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time. Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale. The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire. For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals. For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors. SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results. On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future. Note de contenu : 1- Introduction
2- Literature review
3- Study areas and data
4- Metodology
5- Results and discussionNuméro de notice : 28309 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : RTK Stockholm : 2021 DOI : sans En ligne : http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1557429 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98130
Titre : Model and reality: Connecting BIM and the built environment Type de document : Thèse/HDR Auteurs : Gustaf Uggla, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 79 p. Format : 21 x 30 cm Note générale : bibliographie
Doctoral Thesis in Geodesy and Geoinformatics, KTH Royal Institute of Technology, StockholmLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données localisées 3D
[Termes IGN] format d'échange
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] géoréférencement
[Termes IGN] métadonnées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] projection Universal Transverse Mercator
[Termes IGN] qualité des données
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The adoption of building information modeling (BIM) in the architecture, engineering, and construction (AEC) industry is changing the way information regarding the built environment is created, stored, and exchanged. In short, documents are replaced with databases, processes are automated, and timelines become more circular with an emphasis on managing the life cycles of all manufactured objects. This has both direct and indirect consequences for the fields of geodesy and geographic information. Although geodesy and surveying have played a vital role in the construction process for a long time, new data standards and higher degrees of prefabrication and automation in the actual construction means that the topic of georeferencing must be revisited. In addition, using object oriented data structures means that semantic information must be inferred from geodata such as point clouds and images in order to adequately document existing assets. This thesis addresses the handling of 3D spatial information by analyzing different georeferencing methods and metadata used to describe the quality and characteristics of geodata. The outcomes include a recommendation for how the open BIM standard Industry Foundation Classes (IFC) could be extended to support more robust georeferencing, a suggestion that all standards and exchange formats used forthe built environment should include metadata for tolerance and uncertainty, and a framework that can describe characteristics of 3D spatial data that are not covered by conventional geographic metadata. On the semantic side, this thesis proposes an image-based method for identifying roadside objects in mobile laser scanning (MLS) point clouds, and it also explores the possibilities to train neural networks for point cloud segmentation by creating training data from 3D mesh models used in infrastructure design. Overall, the thesis describes the connection between model and reality, the importance of geodesy and geodetic surveying in this context, and makes contributions to both the geometric and semantic aspects of modeling the built environment. Note de contenu : 1- Introduction
2- Basis of knowledge and methods
3- Results
4- Summary of papersNuméro de notice : 28668 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geodesy and Geoinformatics : KTH, Stockholm : 2021 DOI : sans En ligne : http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294087 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99878
Titre : Road extraction based on snakes and sophisticated line extraction Type de document : Mémoire Auteurs : Ivan Laptev, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 1997 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliography
Master of Science, Royal Institute of Technology, StockholmLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme snake
[Termes IGN] détection de contours
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau routier
[Termes IGN] saillanceRésumé : (auteur) The extraction of roads from aerial and satellite images is an important task within cartography and planning of new road networks. The automation of this task is highly motivated by the expected increase of the speed and the precision of extraction. This work considers automatic road extraction from single aerial images of high resolution. It is based on two previously developed approaches: The first one is the differential geometric approach for extraction of linear features; The second - contour extraction based on Active Contour Models, also called snakes. Whereas the first approach is fully-automatic, the second was previously mostly used for semi-automatic tasks which require the control of a human operator. This work combines both techniques and adapt them in the method for fully-automatic road extraction. According to the used strategy, the hypotheses for most salient roads in images of rural scenes are generated and verified first. Then, based on the ends of extracted roads the hypotheses for other roads are stated. The developed snake-based technique for the verification of these hypotheses enables recognition of partially occluded and shadowed roads as well as some roads passing through road crossings. The presented results of the developed approach show that the reliable extraction of roads which images are disturbed by surrounding objects is in many cases possible without the explicit knowledge about these objects. This is a big advantage since the automatic recognition of buildings, vegetation etc., is a very complicated problem by itself. Note de contenu : Introduction
1 - Survey of Related Work
2 - Objectives and Limitations
3 - Theory of Snakes
4 - Road Extraction Loop
5 - Matching of the Road Model
6 - Results
ConclusionNuméro de notice : 21689 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire DEA divers Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90926
Titre : Computational methods for generalization of cartographic data in a raster environment Type de document : Thèse/HDR Auteurs : Lars Schylberg, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 1993 Collection : Fotogrammetriska meddelanden, ISSN 0071-8068 num. 60 Importance : 137 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de DoctoratLangues : Anglais (eng) Descripteur : [Termes IGN] 1:50.000
[Termes IGN] base de connaissances
[Termes IGN] base de données orientée objet
[Termes IGN] base de données topographiques
[Termes IGN] données maillées
[Termes IGN] données topographiques
[Termes IGN] généralisation cartographique
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] Suède
[Termes IGN] système d'information géographique
[Vedettes matières IGN] GénéralisationRésumé : (auteur) This thesis deals with the issue of area generalization of topographic map data for the purpose of creating a cartographic raster data base. The data base in question is a raster data base describing topographic map data. Map data is scanned with 5 m ground resolution from map originals. Facts and rules about manual cartographic methods are collected to form the basis for how and when the automatic methods should be applied. A methodology is proposed that consists of the simplification, deletion and amalgamation generalization operators. The method presented is based on low and intermediate level image processing operations on a segmented raster data base. Information is gathered about an object's characteristics and its neighbouring objects. A knowledge base is used to decide on what actions to perform. Examples show how this methodology is implemented in a standard raster based GIS. A prototype system has been built to test the methodology. The limitations of this methodology are discussed on the bases of experiments using topographic map data from the Swedish map series on a scale of 1:50 000 as input data. Note de contenu : 1- Introduction
2- Previous work and generalization
3- Knowledge representation and acquisition
4- Data and basic processing techniques
5- Amalgamation
6- Simplification operation
7- deletion of objects
8- Combination of operators
9- Discussion and concluding remarksNuméro de notice : 24572 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : : Stockholm : 1993 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92118 Exemplaires (1)
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Titre : Reports and collected reprints 1988-1991 Type de document : Monographie Auteurs : Kennert Torlegard, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 1992 Collection : Photogrammetric reports, ISSN 0071-8068 num. 56 Importance : 300 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] analyse d'image numérique
[Termes IGN] base de connaissances
[Termes IGN] correction radiométrique
[Termes IGN] image SPOT
[Termes IGN] photogrammétrie métrologique
[Termes IGN] système d'information géographique
[Termes IGN] télédétection spatialeNuméro de notice : 60461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=44185 Réservation
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