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Research on map emotional semantics using deep learning approach / Daping Xi in Cartography and Geographic Information Science, Vol 50 n° 5 (June 2023)  

Public [article]  inCartography and Geographic Information Science > Vol 50 n° 5 (June 2023) . - pp 465 - 480 Titre : Research on map emotional semantics using deep learning approach Type de document : Article/Communication Auteurs : Daping Xi, Auteur ; Xini Hu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 465 - 480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] émotion [Termes IGN] réseau neuronal profond Résumé : (auteur) The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules. Numéro de notice : A2023-235 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2023.2172081 Date de publication en ligne : 21/02/2023 En ligne : https://doi.org/10.1080/15230406.2023.2172081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103594 [article]

Transform paper-based cadastral data into digital systems using GIS and end-to-end deep learning techniques / Joseph Mango in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 5 (May 2023) . - pp 1099 - 1127 Titre : Transform paper-based cadastral data into digital systems using GIS and end-to-end deep learning techniques Type de document : Article/Communication Auteurs : Joseph Mango, Auteur ; Moyang Wang, Auteur ; Senlin Mu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1099 - 1127 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre [Termes IGN] apprentissage profond [Termes IGN] données cadastrales [Termes IGN] numérisation du cadastre [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique Résumé : (auteur) Digital systems storing cadastral data in vector format are considered effective due to their ability of offering interactive services to citizens and other land-related systems. The adoption of such systems is ubiquitous, but when adopted, they create two non-compatible systems with paper-based cadastral systems whose information needs to be digitised. This study proposes a new approach that is fast and accurate for transforming paper-based cadastral data into digital systems. The proposed method involves deep-learning techniques of the LCNN and ResNet-50 for detecting cadastral parcels and their numbers, respectively, from the cadastral plans. It also contains four functions defined to speed up transformations and compilations of the cadastral plan’s data in digital systems. The LCNN is trained and validated with 968 samples. The ResNet-50 is trained and validated with 106,000 samples. The Structural-Average-Precision (sAP10) achieved with the LCNN was 0.9057. The Precision, Recall and F1-Score achieved with the ResNet-50 were 0.9650, 0.9648 and 0.9649, respectively. These results confirmed that the new method is accurate enough for implementation, and we tested it with a huge set of data from Tanzania. Its performance from the experimented data shows that the proposed method could effectively transform paper-based cadastral data into digital systems. Numéro de notice : A2023-212 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2023.2178002 Date de publication en ligne : 22/03/2023 En ligne : https://doi.org/10.1080/13658816.2023.2178002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103139 [article]

Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)  

Public [article]  inSustainable Cities and Society > vol 89 (February 2023) . - n° 104337 Titre : Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities Type de document : Article/Communication Auteurs : Pavlos Tsagkis, Auteur ; Efthimios Bakogiannis, Auteur ; Alexandros Nikitas, Auteur Année de publication : 2023 Article en page(s) : n° 104337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] Corine (base de données) [Termes IGN] croissance urbaine [Termes IGN] données localisées libres [Termes IGN] étalement urbain [Termes IGN] Grèce [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle orienté agent [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward. Numéro de notice : A2023-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104337 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102486 [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]

DeepSim-Nets: Deep Similarity Networks for stereo image matching / Mohamed Ali Chebbi (2023)  

Public Titre : DeepSim-Nets: Deep Similarity Networks for stereo image matching Type de document : Article/Communication Auteurs : Mohamed Ali Chebbi, Auteur ; Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Paul Lopes, Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 2096 - 2104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] chaîne de traitement [Termes IGN] géométrie de l'image [Termes IGN] géométrie épipolaire [Termes IGN] réseau neuronal profond Index. décimale : 35.20 Traitement d'image Résumé : (auteur) We present three multi-scale similarity learning architectures, or DeepSim networks. These models learn pixel-level matching with a contrastive loss and are agnostic to the geometry of the considered scene. We establish a middle ground between hybrid and end-to-end approaches by learning to densely allocate all corresponding pixels of an epipolar pair at once. Our features are learnt on large image tiles to be expressive and capture the scene's wider context. We also demonstrate that curated sample mining can enhance the overall robustness of the predicted similarities and improve the performance on radiometrically homogeneous areas. We run experiments on aerial and satellite datasets. Our DeepSim-Nets outperform the baseline hybrid approaches and generalize better to unseen scene geometries than end-to-end methods. Our flexible architecture can be readily adopted in standard multi-resolution image matching pipelines. The code is available at https://github.com/DaliCHEBBI/DeepSimNets. Numéro de notice : C2023-007 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/html/Chebbi_DeepSim- [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103281

Exploring the potential of deep learning for map generalization / Azelle Courtial (2023)  

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A geometry-aware attention network for semantic segmentation of MLS point clouds / Jie Wan in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)  

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HGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation / Xuefeng Guan in Computers, Environment and Urban Systems, vol 99 (January 2023)  

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A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

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MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)  

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Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])  

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Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia / Lifan Ji in European journal of remote sensing, vol 56 n° 1 (2023)  

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Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data / Yi-Chun Lin in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

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Updating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia / Medria Shekar Rani in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)  

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A whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)  

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