Descripteur
Documents disponibles dans cette catégorie (2096)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Accuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets / Kanta Sayuda in Transactions in GIS, vol 26 n° 7 (November 2022)
[article]
Titre : Accuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets Type de document : Article/Communication Auteurs : Kanta Sayuda, Auteur ; Euijung Hong, Auteur ; Yuki Akiyama, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3003 - 3027 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] distribution spatiale
[Termes IGN] Extreme Gradient Machine
[Termes IGN] géocodage
[Termes IGN] immobilier (secteur)
[Termes IGN] Japon
[Termes IGN] logementRésumé : (auteur) In Japan, the rise in vacant housing has created the need to develop quick, effective, and inexpensive methods to detect the spatial distribution of vacant housing at the municipal level. However, due to incomplete and inaccessible data, the change in the accuracy of the vacant housing detection model must be evaluated while accounting for the limited data. Therefore, this study compares the performance of vacant housing detection models for different data combinations (Basic Resident Register; building registration, water usage, and national census) by considering Wakayama City, Japan, as the case study setting. Three main findings emerged: (1) the contribution of the data to the accuracy varies with the combination of datasets and metrics; (2) even if specific municipal data are unavailable, it is possible to acquire a similar accuracy by combining other data; and (3) the missing value contributes to the vacant housing detection rather than the feature value itself. Numéro de notice : A2022-887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12992 Date de publication en ligne : 31/10/2022 En ligne : https://doi.org/10.1111/tgis.12992 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102217
in Transactions in GIS > vol 26 n° 7 (November 2022) . - pp 3003 - 3027[article]Change alignment-based image transformation for unsupervised heterogeneous change detection / Kuowei Xiao in Remote sensing, vol 14 n° 21 (November-1 2022)
[article]
Titre : Change alignment-based image transformation for unsupervised heterogeneous change detection Type de document : Article/Communication Auteurs : Kuowei Xiao, Auteur ; Yuli Sun, Auteur ; Lin Lei, Auteur Année de publication : 2022 Article en page(s) : n° 5622 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] décomposition d'image
[Termes IGN] détection de changement
[Termes IGN] données hétérogènes
[Termes IGN] masqueRésumé : (auteur) Change detection (CD) with heterogeneous images is currently attracting extensive attention in remote sensing. In order to make heterogeneous images comparable, the image transformation methods transform one image into the domain of another image, which can simultaneously obtain a forward difference map (FDM) and backward difference map (BDM). However, previous methods only fuse the FDM and BDM in the post-processing stage, which cannot fundamentally improve the performance of CD. In this paper, a change alignment-based change detection (CACD) framework for unsupervised heterogeneous CD is proposed to deeply utilize the complementary information of the FDM and BDM in the image transformation process, which enhances the effect of domain transformation, thus improving CD performance. To reduce the dependence of the transformation network on labeled samples, we propose a graph structure-based strategy of generating prior masks to guide the network, which can reduce the influence of changing regions on the transformation network in an unsupervised way. More importantly, based on the fact that the FDM and BDM are representing the same change event, we perform change alignment during the image transformation, which can enhance the image transformation effect and enable FDM and BDM to effectively indicate the real change region. Comparative experiments are conducted with six state-of-the-art methods on five heterogeneous CD datasets, showing that the proposed CACD achieves the best performance with an average overall accuracy (OA) of 95.9% on different datasets and at least 6.8% improvement in the kappa coefficient. Numéro de notice : A2022-855 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215622 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.3390/rs14215622 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102103
in Remote sensing > vol 14 n° 21 (November-1 2022) . - n° 5622[article]Cross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening / Jiahui Qu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)
[article]
Titre : Cross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening Type de document : Article/Communication Auteurs : Jiahui Qu, Auteur ; Tongzhen Zhang, Auteur ; Wenqian Dong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5543114 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image hyperspectrale
[Termes IGN] image panchromatique
[Termes IGN] lissage de données
[Termes IGN] pansharpening (fusion d'images)Résumé : (auteur) Hyperspectral (HS) image pansharpening is of great importance in improving the spatial resolution for many commercial platforms and remote sensing tasks. Convolutional neural network (CNN) has recently been applied in pansharpening. However, most existing CNN-based pansharpening models followed an early-fusion/late-fusion strategy, which integrates the low-level/high-level features of panchromatic (PAN) and HS streams at the input-output of the network. It is difficult to learn more complex combinations between PAN and HS streams. This article proposes a novel end-to-end residual hyperdense pansharpening network with a cross-guided pyramid attention (called RHDcgpaNet). The overall architecture of the proposed method is a residual hyperdense network, which extends the definition of dense connections to two-stream pansharpening problem. The proposed RHDcgpaNet allows guidance from the state of the preceding layers to all the layers in- between PAN and HS streams in a feed-forward manner, significantly increasing the learning representation. A cross-guided pyramid attention is designed and embedded to the proposed residual hyperdense network to yield more useful spatial–spectral feature transfer in network. Extensive experiments on widely used datasets demonstrate that the proposed RHDcgpaNet achieves favorable performance in comparison to the state-of-the-art methods. Numéro de notice : A2022-852 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1109/TGRS.2022.3220079 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3220079 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102098
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 11 (November 2022) . - n° 5543114[article]Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)
[article]
Titre : Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data Type de document : Article/Communication Auteurs : Yanan Zhou, Auteur ; Wei Wu, Auteur ; Hongbin Liu, Auteur Année de publication : 2022 Article en page(s) : n° 5571 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] composition des sols
[Termes IGN] données multitemporelles
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] limon
[Termes IGN] qualité du sol
[Termes IGN] réflectance spectrale
[Termes IGN] texture du solRésumé : (auteur) Soil texture is a key soil property driving physical, chemical, biological, and hydrological processes in soils. The rapid development of remote sensing techniques shows great potential for mapping soil properties. This study highlights the effectiveness of multitemporal remote sensing data in identifying soil textural class by using retrieved vegetation properties as proxies of soil properties. The impacts of sensors, modeling resolutions, and modeling techniques on the accuracy of soil texture classification were explored. Multitemporal Landsat-8 and Sentinel-2 images were individually acquired at the same time periods. Three satellite-based experiments with different inputs, i.e., Landsat-8 data, Sentinel-2 data (excluding red-edge parameters), and Sentinel-2 data (including red-edge parameters) were conducted. Modeling was carried out at three spatial resolutions (10, 30, 60 m) using five machine-learning (ML) methods: random forest, support vector machine, gradient-boosting decision tree, categorical boosting, and super learner that combined the four former classifiers based on the stacking concept. In addition, a novel SHapley Addictive Explanation (SHAP) technique was introduced to explain the outputs of the ML model. The results showed that the sensors, modeling resolutions, and modeling techniques significantly affected the prediction accuracy. The models using Sentinel-2 data with red-edge parameters performed consistently best. The models usually gave better results at fine (10 m) and medium (30 m) modeling resolutions than at a coarse (60 m) resolution. The super learner provided higher accuracies than other modeling techniques and gave the highest values of overall accuracy (0.8429), kappa (0.7611), precision (0.8378), recall rate (0.8393), and F1-score (0.8398) at 30 m with Sentinel-2 data involving red-edge parameters. The SHAP technique quantified the contribution of each variable for different soil textural classes, revealing the critical roles of red-edge parameters in separating loamy soils. This study provides comprehensive insights into the effective modeling of soil properties on various scales using multitemporal optical images. Numéro de notice : A2022-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215571 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.3390/rs14215571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102104
in Remote sensing > vol 14 n° 21 (November-1 2022) . - n° 5571[article]Features predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (November-1 2022)
[article]
Titre : Features predisposing forest to bark beetle outbreaks and their dynamics during drought Type de document : Article/Communication Auteurs : M. Müller, Auteur ; P.O. Olsson, Auteur ; Lars Eklundh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des risques
[Termes IGN] canopée
[Termes IGN] caractérisation
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données météorologiques
[Termes IGN] humidité du sol
[Termes IGN] peuplement mélangé
[Termes IGN] Picea abies
[Termes IGN] Scolytinae
[Termes IGN] sécheresse
[Termes IGN] Suède
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Climate change is estimated to increase the risk of the bark beetle (Ips typographus L.) mass outbreaks in Norway Spruce (Picea abies (L.) Karst) forests. Habitats that are thermally suitable for bark beetles may expand, and an increase in the frequency and intensity of droughts can promote drought stress on host trees. Drought affects tree vigor and in unison with environmental features it influences the local predisposition risk of forest stands to bark beetle attacks. We aimed to study how various environmental features influence the risk of bark beetle attacks during a drought year and the following years with more normal weather conditions but with higher bark beetle populations. We included features representing local forest stand attributes, topography, soil type and wetness, the proximity of clear-cuts and previous bark beetle attacks, and a machine learning algorithm (random forest) was applied to study the variation of predisposition risk across a 48,600 km2 study area in SE Sweden. Forest stands with increased risk of bark beetle attack were distinguished with high accuracy both during drought and in normal weather conditions. The results show that during both study periods, spruce and mixed coniferous forests had elevated risk of attack, while forests with a mix of deciduous and coniferous trees had a lower risk. Forests with high average canopy height were strongly predisposed to bark beetle attacks. However, during the drought year risk was more similar between stands with lower and higher canopy height, suggesting that during drought periods younger trees can be predisposed to bark beetle attacks. The importance of soil moisture and position within the local landscape were highlighted as important features during the drought year. Identifying areas with increased risk, supported by information on how environmental features control the predisposition risk during drought, could aid adaptation strategies and forest management intervention efforts. We conclude that geospatial data and machine learning have the potential to further support the digitalization of the forest industry, facilitating development of methods capable to quantify importance and dynamics of
environmental features controlling the risk in local context. Corresponding methods could help to direct management actions more effectively and offer information for decision-making in changing climate.Numéro de notice : A2022-731 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120480 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120480 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101687
in Forest ecology and management > vol 523 (November-1 2022) . - n° 120480[article]Foreground-aware refinement network for building extraction from remote sensing images / Zhang Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 11 (November 2022)PermalinkGA-Net: A geometry prior assisted neural network for road extraction / Xin Chen in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)PermalinkImproving deep learning on point cloud by maximizing mutual information across layers / Di Wang in Pattern recognition, vol 131 (November 2022)PermalinkImproving image segmentation with boundary patch refinement / Xiaolin Hu in International journal of computer vision, vol 130 n° 11 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkMachine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)PermalinkMeasuring visual walkability perception using panoramic street view images, virtual reality, and deep learning / Yunqin Li in Sustainable Cities and Society, vol 86 (November 2022)PermalinkMulti-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)PermalinkTidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)Permalink