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Multiresolution analysis pansharpening based on variation factor for multispectral and panchromatic images from different times / Peng Wang in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 61 n° 3 (March 2023) . - n° 5401217 Titre : Multiresolution analysis pansharpening based on variation factor for multispectral and panchromatic images from different times Type de document : Article/Communication Auteurs : Peng Wang, Auteur ; Hongyu Yao, Auteur ; Bo Huang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 5401217 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multirésolution [Termes IGN] données multitemporelles [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] pouvoir de résolution géométrique Résumé : (auteur) Most pansharpening methods refer to the fusion of the original low-resolution multispectral (MS) and high-resolution panchromatic (PAN) images acquired simultaneously over the same area. Due to its good robustness, multiresolution analysis (MRA) has become one of the important categories of pansharpening methods. However, when only MS and PAN images acquired at different times can be provided, the fusion results from current MRA methods are often not ideal due to the failure to effectively analyze multitemporal misalignments between MS and PAN images from different times. To solve this issue, MRA pansharpening based on variation factor for MS and PAN images from different times is proposed. The MRA pansharpening based on dual-scale regression model is first established, and the variation factor is then introduced to effectively analyze the multitemporal misalignments by using the alternating direction method of multipliers (ADMM), yielding the final fusion results. Experiments with synthetic and real datasets show that the proposed method exhibits significant performance improvement compared to the traditional pansharpening methods, as well as the state-of-the-art MRA methods. Visual comparisons demonstrate that the variation factor introduces encouraging improvements in the compensation of multitemporal misalignments in ground objects and advances pansharpening applications for MS and PAN images acquired at different times. Numéro de notice : A2023-184 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2023.3252001 En ligne : https://doi.org/10.1109/TGRS.2023.3252001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102956 [article]

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

Public [article]  inGeoinformatica > vol 27 n° 1 (January 2023) . - pp 77 - 105 Titre : MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction Type de document : Article/Communication Auteurs : Du Yin, Auteur ; Renhe Jiang, Auteur ; Jiewen Deng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 77 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] données multitemporelles [Termes IGN] données spatiotemporelles [Termes IGN] flux [Termes IGN] gestion de trafic [Termes IGN] origine - destination [Termes IGN] réseau neuronal de graphes [Termes IGN] système de transport intelligent [Termes IGN] trafic urbain [Termes IGN] transport public [Termes IGN] utilisateur Résumé : (auteur) The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods. Numéro de notice : A2023-113 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-022-00466-1 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1007/s10707-022-00466-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102478 [article]

Decadal surface changes and displacements in Switzerland / Valentin Tertius Bickel in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 24 Titre : Decadal surface changes and displacements in Switzerland Type de document : Article/Communication Auteurs : Valentin Tertius Bickel, Auteur ; Andrea Manconi, Auteur Année de publication : 2022 Article en page(s) : n° 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] corrélation d'images [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données multitemporelles [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie locale [Termes IGN] glacier [Termes IGN] Liechtenstein [Termes IGN] modèle numérique de terrain [Termes IGN] stéréophotogrammétrie [Termes IGN] Suisse Résumé : (auteur) Multi-temporal, high-resolution, and homogeneous geospatial datasets acquired by space- and/or airborne sensors provide unprecedented opportunities for the characterization and monitoring of surface changes on very large spatial scales. Here, we demonstrate how an off-the-shelf, open-source image correlation algorithm can be combined with SwissALTI3D LiDAR-derived elevation data from different tracking periods to create country-scale surface displacement and vertical change maps of Switzerland, including Liechtenstein, with minimal computational effort. The results show that glacier displacement and ablation make up the most significant fraction of the detected surface changes in the last two decades. In addition, we identify numerous landslides and other geomorphic features, as well as manmade changes such as construction sites and landfills. All produced maps and data products are available online, free of charge. Numéro de notice : A2022-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41651-022-00119-9 Date de publication en ligne : 01/08/2022 En ligne : https://doi.org/10.1007/s41651-022-00119-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102019 [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)  

Public [article]  inRemote sensing > vol 14 n° 21 (November-1 2022) . - n° 5571 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 sol Résumé : (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 [article]

Towards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101794 Titre : Towards the automated large-scale reconstruction of past road networks from historical maps Type de document : Article/Communication Auteurs : Johannes H. Uhl, Auteur ; Stefan Leyk, Auteur ; Yao-Yi Chiang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] analyse de sensibilité [Termes IGN] carte ancienne [Termes IGN] carte routière [Termes IGN] carte topographique [Termes IGN] classification par nuées dynamiques [Termes IGN] données multitemporelles [Termes IGN] Etats-Unis [Termes IGN] extraction du réseau routier [Termes IGN] histoire [Termes IGN] paysage [Termes IGN] réseau routier [Termes IGN] transport routier [Termes IGN] urbanisation Résumé : (auteur) Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography. Numéro de notice : A2022-947 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101794 Date de publication en ligne : 18/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100182 [article]

Learning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds / Michael Kölle in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa / Shenelle Lottering in Geocarto international, vol 37 n° 6 ([01/04/2022])  

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Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])  

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Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)  

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Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana / Ram Avtar in Geocarto international, vol 37 n° 2 ([15/01/2022])  

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Contributions of multi-temporal airborne LiDAR data to mapping carbon stocks and fluxes in tropical forests / Claudia Milena Huertas Garcia (2022)  

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The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)  

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Feature matching for multi-epoch historical aerial images: A new pipeline feature detection pipeline in open-source MicMac / Lulin Zhang in Blog de la RFPT, sans n° ([17/11/2021])  

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Quantifying historical landscape change with repeat photography: an accuracy assessment of geospatial data obtained through monoplotting / Ulrike Bayr in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)  

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Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

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