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Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211 Titre : Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence Type de document : Article/Communication Auteurs : Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte ancienne [Termes IGN] cartographie historique [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données anciennes [Termes IGN] matrice de co-occurrence [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. Numéro de notice : A2023-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.01.021 Date de publication en ligne : 14/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804 [article]

PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 32 - 44 Titre : PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes Type de document : Article/Communication Auteurs : Weixiao Gao, Auteur ; Liangliang Nan, Auteur ; Bas Boom, Auteur ; Hugo Ledoux, Auteur Année de publication : 2023 Article en page(s) : pp 32 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse de scène 3D [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] contour [Termes IGN] maillage [Termes IGN] Perceptron multicouche [Termes IGN] réseau neuronal de graphes [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique Résumé : (Auteur) We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planarity-sensible over-segmentation followed by semantic classification. The over-segmentation step generates an initial set of mesh segments that capture the planar and non-planar regions of urban scenes. In the subsequent classification step, we construct a graph that encodes the geometric and photometric features of the segments in its nodes and the multi-scale contextual features in its edges. The final semantic segmentation is obtained by classifying the segments using a graph convolutional network. Experiments and comparisons on two semantic urban mesh benchmarks demonstrate that our approach outperforms the state-of-the-art methods in terms of boundary quality, mean IoU (intersection over union), and generalization ability. We also introduce several new metrics for evaluating mesh over-segmentation methods dedicated to semantic segmentation, and our proposed over-segmentation approach outperforms state-of-the-art methods on all metrics. Our source code is available at https://github.com/WeixiaoGao/PSSNet. Numéro de notice : A2023-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.020 Date de publication en ligne : 02/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102399 [article]

Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 598 Titre : Geographic named entity recognition by employing natural language processing and an improved BERT model Type de document : Article/Communication Auteurs : Liufeng Tao, Auteur ; Zhong Xie, Auteur ; Dexin Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Chine [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données issues des réseaux sociaux [Termes IGN] données publiques [Termes IGN] jeu de données [Termes IGN] reconnaissance de caractères [Termes IGN] reconnaissance de noms [Termes IGN] test de performance [Termes IGN] toponyme [Termes IGN] traitement du langage naturel Résumé : (auteur) Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models. Numéro de notice : A2022-945 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11120598 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.3390/ijgi11120598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102178 [article]

Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])  

Public [article]  inGeocarto international > vol 37 n° 18 [01/09/2022] . - pp 5433 - 5452 Titre : Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 5433 - 5452 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] acquisition d'images [Termes IGN] apprentissage profond [Termes IGN] approche participative [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couleur (variable spectrale) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] étiquette [Termes IGN] image multibande [Termes IGN] OpenStreetMap [Termes IGN] pixel [Termes IGN] plateforme collaborative [Termes IGN] texture d'image [Termes IGN] Worldview Résumé : (auteur) In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels ​​of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved. Numéro de notice : A2022-702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1917006 Date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1917006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101561 [article]

Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs / Douglas Stefanello Facco in Geocarto international, vol 37 n° 16 ([15/08/2022])  

Public [article]  inGeocarto international > vol 37 n° 16 [15/08/2022] . - pp 4762 - 4783 Titre : Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs Type de document : Article/Communication Auteurs : Douglas Stefanello Facco, Auteur ; Laurindo Antonio Guasselli, Auteur ; Luis Fernando Chimelo Ruiz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 4762 - 4783 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image orientée objet [Termes IGN] bande spectrale [Termes IGN] Brésil [Termes IGN] centrale hydroélectrique [Termes IGN] classification bayesienne [Termes IGN] classification dirigée [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat-OLI [Termes IGN] segmentation d'image [Termes IGN] turbidité des eaux Résumé : (auteur) Our goal is to compare the performance of Classification and Regression Tree, Naive Bayes and Random Forest algorithms, from supervised image classification, and approaches on Pixel-Based Image analysis (PBIA) and Geographic Object-Based Image Analysis (GEOBIA), to classify turbidity in reservoirs. Tod do so, we use Landsat 8 image and bands and spectral indices, as predictive parameters, as well as the classification algorithms based on PBIA and GEOBIA. The Brazilian Itaipu reservoir was adopted, as a case study. Our results show that the RF classifier obtained the highest accuracy in both classification approaches, followed by CART and NB. The KA and OA indices of the GEOBIA classifications were superior to the PBIA classifications in both algorithms. This study contributes with an approach to quickly and accurately delineating turbidity spectral limits in reservoirs. Numéro de notice : A2022-668 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1899302 Date de publication en ligne : 22/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1899302 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101519 [article]

Weakly supervised semantic segmentation of airborne laser scanning point clouds / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)  

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Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)

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Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image / Sinan Kaptan in Geocarto international, vol 37 n° 5 ([01/03/2022])  

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Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])  

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Attributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)  

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Construction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique / Houssayn Meriche (2022) 

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Détection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022) 

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

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Hourly rainfall forecast model using supervised learning algorithm / Qingzhi Zhao in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)  

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