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Evaluating narrative in geoportals for territorial public policies / Luis Manuel Batista in Cartographica, vol 56 n° 4 (winter 2021)  

Public [article]  inCartographica > vol 56 n° 4 (winter 2021) . - pp 303 - 319 Titre : Evaluating narrative in geoportals for territorial public policies Type de document : Article/Communication Auteurs : Luis Manuel Batista, Auteur ; Ana Figueiras, Auteur Année de publication : 2021 Article en page(s) : pp 303 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse comparative [Termes IGN] carte thématique [Termes IGN] collectivité territoriale [Termes IGN] communication cartographique [Termes IGN] compréhension de l'image [Termes IGN] conception cartographique [Termes IGN] géoportail [Termes IGN] plan local d'urbanisme [Termes IGN] politique territoriale [Termes IGN] Portugal [Termes IGN] web mapping Résumé : (auteur) To do territorial planning, we need several maps referring to different layers of information necessary to represent the territory according to a vast set of variables. At the Portuguese municipal level, the municipal master plan (PDM – plano diretor municipal) is the territorial management tool responsible for long-term territorial planning and ordering. Since the PDM will constrain the citizens’ lives, it should be of easy access and interpretation. However, due to its large amount of information, it is often hard for them to understand what is presented using only static cartographic elements. Comparing 60 Web sites that present geographical information for a wider public, we found that narrative, visual, and interactive elements and data presentation in flexible portions with the necessary information for partitioning, browsing, or querying made the data more engaging. This flexibility is particularly important with large data sets, where decreasing the level of complexity is vital for proper understanding and analysis. We found that geoportals dedicated to territorial public policies lack narrative elements that would improve comprehension. Since it is difficult for the general public to understand the strategy the municipality wants to implement, the desired public participation, although possible, will return poor results. Numéro de notice : A2021-887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2021-0023 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.3138/cart-2021-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99227 [article]

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Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])  

Public [article]  inGeocarto international > vol 36 n° 18 [01/10/2021] . - pp 2065 - 2084 Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] chromatopsie [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] échantillonnage d'image [Termes IGN] filtrage numérique d'image [Termes IGN] image captée par drone [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 Date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602 [article]

Semantic hierarchy emerges in deep generative representations for scene synthesis / Ceyuan Yang in International journal of computer vision, vol 129 n° 5 (May 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 5 (May 2021) . - pp 1451 - 1466 Titre : Semantic hierarchy emerges in deep generative representations for scene synthesis Type de document : Article/Communication Auteurs : Ceyuan Yang, Auteur ; Yujun Shen, Auteur ; Bolei Zhou, Auteur Année de publication : 2021 Article en page(s) : pp 1451 - 1466 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] apprentissage profond [Termes IGN] compréhension de l'image [Termes IGN] représentation des connaissances [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation hiérarchique [Termes IGN] segmentation sémantique [Termes IGN] synthèse d'image Résumé : (auteur) Despite the great success of Generative Adversarial Networks (GANs) in synthesizing images, there lacks enough understanding of how photo-realistic images are generated from the layer-wise stochastic latent codes introduced in recent GANs. In this work, we show that highly-structured semantic hierarchy emerges in the deep generative representations from the state-of-the-art GANs like StyleGAN and BigGAN, trained for scene synthesis. By probing the per-layer representation with a broad set of semantics at different abstraction levels, we manage to quantify the causality between the layer-wise activations and the semantics occurring in the output image. Such a quantification identifies the human-understandable variation factors that can be further used to steer the generation process, such as changing the lighting condition and varying the viewpoint of the scene. Extensive qualitative and quantitative results suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize various concepts in a hierarchical manner: the early layers tend to determine the spatial layout, the middle layers control the categorical objects, and the later layers render the scene attributes as well as the color scheme. Identifying such a set of steerable variation factors facilitates high-fidelity scene editing based on well-learned GAN models without any retraining (code and demo video are available at https://genforce.github.io/higan). Numéro de notice : A2021-408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-020-01429-5 Date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1007/s11263-020-01429-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97725 [article]

Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n° 2 (March 2021) . - n° 52 Titre : Recognition of varying size scene images using semantic analysis of deep activation maps Type de document : Article/Communication Auteurs : Shikha Gupta, Auteur ; A.D. Dileep, Auteur ; Veena Thenkanidiyoor, Auteur Année de publication : 2021 Article en page(s) : n° 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] compréhension de l'image [Termes IGN] méthode fondée sur le noyau [Termes IGN] modèle conceptuel de données [Termes IGN] reconnaissance de formes [Termes IGN] segmentation sémantique Résumé : (auteur) Understanding the complex semantic structure of scene images requires mapping the image from pixel space to high-level semantic space. In semantic space, a scene image is represented by the posterior probabilities of concepts (e.g., ‘car,’ ‘chair,’ ‘window,’ etc.) present in it and such representation is known as semantic multinomial (SMN) representation. SMN generation requires a concept annotated dataset for concept modeling which is infeasible to generate manually due to the large size of databases. To tackle this issue, we propose a novel approach of building the concept model via pseudo-concepts. Pseudo-concept acts as a proxy for the actual concept and gives the cue for its presence instead of actual identity. We propose to use filter responses from deeper convolutional layers of convolutional neural networks (CNNs) as pseudo-concepts, as filters in deeper convolutional layers are trained for different semantic concepts. Most of the prior work considers fixed-size (≈227×227) images for semantic analysis which suppresses many concepts present in the images. In this work, we preserve the true-concept structure in images by passing in their original resolution to convolutional layers of CNNs. We further propose to prune the non-prominent pseudo-concepts, group the similar one using kernel clustering and later model them using a dynamic-based support vector machine. We demonstrate that resulting SMN representation indeed captures the semantic concepts better and results in state-of-the-art classification accuracy on varying size scene image datasets such as MIT67 and SUN397. Numéro de notice : A2021-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01168-8 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00138-021-01168-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97898 [article]

Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development / Kendra Munn in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 2 (March 2021) . - pp 105 - 123 Titre : Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development Type de document : Article/Communication Auteurs : Kendra Munn, Auteur ; Suzana Dragićević, Auteur Année de publication : 2021 Article en page(s) : pp 105 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] combinaison linéaire ponderée [Termes IGN] compréhension de l'image [Termes IGN] croissance urbaine [Termes IGN] densification [Termes IGN] hauteur du bâti [Termes IGN] logement [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] planification urbaine [Termes IGN] urbanisme [Termes IGN] Vancouver (Colombie britannique) Résumé : (Auteur) Urban densification is often seen as a process that aims to limit the negative environmental impacts of urban sprawl in rapidly growing cities by prioritizing planning policies stimulating vertical growth (or high-rise development) over expansion along the urban fringe. Densification of major Canadian urban areas has led to the proliferation of high-rises with an increasing proportion of residents occupying these buildings rather than traditional individual housing. Thus, there is a need for analytical methods that can evaluate the suitability of different residential units in vertical urban developments based on unique criteria for different stakeholders such as prospective residents, developers, or municipal planners. Multi-criteria evaluation (MCE) analysis with weighted linear combination (WLC) is frequently implemented in geographic information systems (GIS) to identify the appropriate solution(s) for a decision problem. However, there are currently no available MCE methods for spatial analysis that can provide evaluation in a three-dimensional (3D) GIS environment, such as for urban vertical development. Therefore, the main objective of this study is to propose a 3D WLC-MCE suitability analysis method for suitability of high-rise residential units in a dense urban area. Five preference scenarios were developed and applied to data from City of Vancouver, Canada. The results indicate that south-facing units and units on higher floors generally exhibit higher levels of suitability as they are less affected by the noise and pollution of the urban road network and receive more sunlight and ocean views. The proposed 3D MCE approach can be used for urban planning and property tax assessment. Numéro de notice : A2021-096 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1845981 Date de publication en ligne : 03/12/2020 En ligne : https://doi.org/10.1080/15230406.2020.1845981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97020 [article]

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Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

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Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)  

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Deep-learning for 3D reconstruction / Fabio Tosi (2021)  

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From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)  

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Slope engineering / Ali Ismet Kanlı (2021)  

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X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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