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Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Linear building pattern recognition in topographical maps combining convex polygon decomposition Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Su Ding, Auteur ; Lu Cheng, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte topographique [Termes IGN] construction [Termes IGN] décomposition [Termes IGN] détection du bâti [Termes IGN] forme linéaire [Termes IGN] généralisation cartographique automatisée [Termes IGN] Ordnance Survey (UK) [Termes IGN] polygone [Termes IGN] reconnaissance de formes Résumé : (auteur) Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach. Numéro de notice : A2022-263 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2055794 Date de publication en ligne : 27/03/2022 En ligne : https://doi.org/10.1080/10106049.2022.2055794 Format de la ressource électronique : 27/03/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100260 [article]

Structured binary neural networks for image recognition / Bohan Zhuang in International journal of computer vision, vol 130 n° 9 (September 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2081 - 2102 Titre : Structured binary neural networks for image recognition Type de document : Article/Communication Auteurs : Bohan Zhuang, Auteur ; Chunhua Shen, Auteur ; Mingkui Tan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2081 - 2102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] décomposition [Termes IGN] détection d'objet [Termes IGN] implémentation (informatique) [Termes IGN] logique binaire [Termes IGN] segmentation sémantique Résumé : (auteur) In this paper, we propose to train binarized convolutional neural networks (CNNs) that are of significant importance for deploying deep learning to mobile devices with limited power capacity and computing resources. Previous works on quantizing CNNs often seek to approximate the floating-point information of weights and/or activations using a set of discrete values. Such methods, termed value approximation here, typically are built on the same network architecture of the full-precision counterpart. Instead, we take a new “structured approximation” view for network quantization — it is possible and valuable to exploit flexible architecture transformation when learning low-bit networks, which can achieve even better performance than the original networks in some cases. In particular, we propose a “group decomposition” strategy, termed GroupNet, which divides a network into desired groups. Interestingly, with our GroupNet strategy, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. We also propose to learn effective connections among groups to improve the representation capability. To improve the model capacity, we propose to dynamically execute sparse binary branches conditioned on input features while preserving the computational cost. More importantly, the proposed GroupNet shows strong flexibility for a few vision tasks. For instance, we extend the GroupNet for accurate semantic segmentation by embedding the rich context into the binary structure. The proposed GroupNet also shows strong performance on object detection. Experiments on image classification, semantic segmentation, and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature. Moreover, the speedup and runtime memory cost evaluation comparing with related quantization strategies is analyzed on GPU platforms, which serves as a strong benchmark for further research. Numéro de notice : A2022-637 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-022-01638-0 Date de publication en ligne : 22/06/2022 En ligne : https://doi.org/10.1007/s11263-022-01638-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101443 [article]

Emissions of CO2 from downed logs of different species and the surrounding soil in temperate forest / Ewa Błońska in Annals of forest research, Vol 65 n° 2 (July - December 2022)  

Public [article]  inAnnals of forest research > Vol 65 n° 2 (July - December 2022) . - pp 47 - 56 Titre : Emissions of CO2 from downed logs of different species and the surrounding soil in temperate forest Type de document : Article/Communication Auteurs : Ewa Błońska, Auteur ; Wojciech Piaszczyk, Auteur ; Jaroslaw Lasota, Auteur Année de publication : 2022 Article en page(s) : pp 47 - 56 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba [Termes IGN] Alnus glutinosa [Termes IGN] bois mort [Termes IGN] Carpinus betulus [Termes IGN] décomposition [Termes IGN] dioxyde de carbone [Termes IGN] écosystème forestier [Termes IGN] forêt tempérée [Termes IGN] Pologne [Termes IGN] Populus tremula [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) The decomposition of deadwood plays a very important role in the functioning of the forest ecosystem. The present study was conducted with the objectives to: (1) determine the amount of deadwood respiration depending on species and degree of decomposition; (2) determine the extent of the impact of decomposing wood on the amount of respiration in surrounding soil; (3) find a relationship between the amount of respiration and the chemical fractional composition of soil organic matter. Our research has shown that respiration of decaying wood samples was 2-3 times lower compared to soil, regardless of the type of wood and the degree of wood decomposition. The conducted analyses confirmed the influence of the species of wood and the degree of decomposition on the respiration rate in wood samples. More decomposed wood (4th and 5th degree of decomposition) releases more CO2 compared to less decomposed wood and the highest CO2 emissions were recorded for aspen and alder wood. Better understanding of the mechanisms and factors affecting CO2 emissions in forest ecosystem can help reduce climate change. Numéro de notice : A2022-906 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.15287/afr.2022.2386 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.15287/afr.2022.2386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102322 [article]

Self-shadowing of a spacecraft in the computation of surface forces : An example in planetary geodesy / Georges Balmino in Artificial satellites, vol 53 n° 1 (March 2018)  

Public [article]  inArtificial satellites > vol 53 n° 1 (March 2018) . - pp 1 - 27 Titre : Self-shadowing of a spacecraft in the computation of surface forces : An example in planetary geodesy Type de document : Article/Communication Auteurs : Georges Balmino, Auteur ; J.C. Marty, Auteur Année de publication : 2018 Article en page(s) : pp 1 - 27 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales [Termes IGN] décomposition [Termes IGN] détection d'ombre [Termes IGN] engin spatial [Termes IGN] Mars (planète) [Termes IGN] problème inverse [Termes IGN] surface (géométrie) Résumé : (auteur) We describe in details the algorithms used in modelling the self-shadowing between spacecraft components, which appears when computing the surface forces as precisely as possible and especially when moving parts are involved. This becomes necessary in planetary geodesy inverse problems using more and more precise orbital information to derive fundamental parameters of geophysical interest. Examples are given with two Mars orbiters, which show significant improvement on drag and solar radiation pressure model multiplying factors, a prerequisite for improving in turn the determination of other global models. Numéro de notice : A2018-173 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.2478/arsa-2018-0002 Date de publication en ligne : 24/03/2018 En ligne : https://doi.org/10.2478/arsa-2018-0002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89813 [article]

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling : A case study in environmental remote sensing / Jing Gao in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 110 - 121 Titre : Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling : A case study in environmental remote sensing Type de document : Article/Communication Auteurs : Jing Gao, Auteur ; James E. Burt, Auteur Année de publication : 2017 Article en page(s) : pp 110 - 121 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] classification pixellaire [Termes IGN] décomposition [Termes IGN] données environnementales [Termes IGN] erreur absolue [Termes IGN] erreur systématique [Termes IGN] image Landsat [Termes IGN] précision de l'estimation [Termes IGN] surface imperméable [Termes IGN] test de performance [Termes IGN] variance Résumé : (Auteur) This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0–100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation – training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD’s strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments. Numéro de notice : A2017-731 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88429 [article]

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