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Identify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
[article]
Titre : Identify urban building functions with multisource data: a case study in Guangzhou, China Type de document : Article/Communication Auteurs : Yingbin Deng, Auteur ; Renrong Chen, Auteur ; Yang Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2060 - 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] approche hiérarchique
[Termes IGN] batiment commercial
[Termes IGN] bâtiment industriel
[Termes IGN] bâtiment public
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] données multisources
[Termes IGN] empreinte
[Termes IGN] exploration de données
[Termes IGN] Extreme Gradient Machine
[Termes IGN] figure géométrique
[Termes IGN] image Gaofen
[Termes IGN] logement
[Termes IGN] point d'intérêt
[Termes IGN] zone urbaineRésumé : (auteur) Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. Numéro de notice : A2022-739 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2046756 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2046756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101716
in International journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 2060 - 2085[article]Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products / Ankit Sagar in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)
[article]
Titre : Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products Type de document : Article/Communication Auteurs : Ankit Sagar , Auteur ; Cédric Vega , Auteur ; Olivier Bouriaud , Auteur ; Christian Piedallu, Auteur ; Jean-Pierre Renaud , Auteur Année de publication : 2022 Projets : LUE / Université de Lorraine, ARBRE / AgroParisTech (2007 -), DEEPSURF / Pironon, Jacques Article en page(s) : pp 175 - 188 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] données allométriques
[Termes IGN] données lidar
[Termes IGN] image Landsat-8
[Termes IGN] inventaire forestier national (données France)
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forest map products are widely used and have taken benefit from progresses in the multisource forest inventory approaches, which are meant to improve the precision of forest inventory estimates at high spatial resolution. However, estimating errors of pixel-wise predictions remains difficult, and reconciling statistical outcomes with map products is still an open and important question. We address this problem using an original approach relying on a model-based inference framework and k-nearest neighbours (k-NN) models to produce pixel-wise estimations and related quality assessment. Our approach takes advantage of the resampling properties of a model-based estimator and combines it with geometrical convex-hull models to measure respectively the precision and accuracy of pixel predictions. A measure of pixel reliability was obtained by combining precision and accuracy. The study was carried out over a 7,694 km2 area dominated by structurally complex broadleaved forests in centre of France. The targeted forest attributes were growing stock volume, basal area and growing stock volume increment. A total of 819 national forest inventory plots were combined with auxiliary data extracted from a forest map, Landsat 8 images, and 3D point clouds from both airborne laser scanning and digital aerial photogrammetry. k-NN models were built independently for both 3D data sources. Both selected models included 5 auxiliary variables, and were generated using 5 neighbours, and most similar neighbours distance measure. The models showed relative root mean square error ranging from 35.7% (basal area, digital aerial photogrammetry) in calibration to 63.4% (growing stock volume increment, airborne laser scanning) in the validation set. At pixel level, we found that a minimum of 86.4% of the predictions were of high precision as their bootstrapped coefficient of variation fall below calibration’s relative root mean square error. The amount of extrapolation varied from 4.3% (digital aerial photogrammetry) to 6.3% (airborne laser scanning). A relationship was found between extrapolation and k-NN distance, opening new opportunities to correct extrapolation errors. At the population level, airborne laser scanning and digital aerial photogrammetry performed similarly, offering the possibility to use digital aerial photogrammetry for monitoring purposes. The proposed method provided consistent estimates of forest attributes and maps, and also provided spatially explicit information about pixel predictions in terms of precision, accuracy and reliability. The method therefore produced high resolution outputs, significant for either decision making or forest management purposes. Numéro de notice : A2022-629 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101495
in ISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 175 - 188[article]Potential and limitation of PlanetScope images for 2-D and 3-D Earth surface monitoring with example of applications to glaciers and earthquakes / Saif Aati in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
[article]
Titre : Potential and limitation of PlanetScope images for 2-D and 3-D Earth surface monitoring with example of applications to glaciers and earthquakes Type de document : Article/Communication Auteurs : Saif Aati , Auteur ; Jean-Philippe Avouac, Auteur ; Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2022 Article en page(s) : n° 4512919 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de scène 3D
[Termes IGN] artefact
[Termes IGN] image PlanetScope
[Termes IGN] modèle de déformation des images
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] séisme
[Termes IGN] surveillance géologiqueRésumé : (auteur) The Planet PlanetScope (PS) CubeSat constellation acquires high-resolution optical images that cover the entire surface of the Earth daily, enabling an unprecedented capability to monitor the Earth’s surface changes. However, our analysis reveals artifacts of the geometry of PS images related to the imaging system and processing issues, limiting the usability of these data for various Earth science applications, including the monitoring of glaciers, dune motion, or the measurement of ground deformation due to earthquakes and landslides. Here, we analyze these artifacts and propose ways to remediate them. We use two examples to evaluate the data and assess the performance of our proposed approaches. The first is the ground deformation caused by the 2019 Ridgecrest earthquake sequence, California, USA, and the second is the 2018–2019 surge of the Shisper glacier in the Karakorum. Using an image correlation technique, we show that PS images exhibit several geometric artifacts, such as scene-to-scene misregistration, inconsistence geolocation accuracy between spectral bands, and topographic artifacts. Altogether, these artifacts make a quantitative analysis of ground displacement difficult and inaccurate. We present a method that remediates most of these geometric artifacts. In addition, we propose a framework for selecting the most appropriate images and a procedure for refining the rational function model (RFM) of unrectified images to monitor surface displacements and topography changes in 3-D. These tools should enhance the use of PS images for Earth science applications. Numéro de notice : A2022-951 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3215821 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3215821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103278
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 4512919[article]Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning / J.F. Roberts in Computers & geosciences, vol 167 (October 2022)
[article]
Titre : Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning Type de document : Article/Communication Auteurs : J.F. Roberts, Auteur ; R. Mwangi, Auteur ; F. Mukabi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] carte thématique
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] image Sentinel-MSI
[Termes IGN] informatique en nuage
[Termes IGN] Kenya
[Termes IGN] langage de programmation
[Termes IGN] observation de la Terre
[Termes IGN] Python (langage de programmation)
[Termes IGN] surveillance forestièreRésumé : (auteur) Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - “Python for Earth Observation”. Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences. Numéro de notice : A2022-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105192 Date de publication en ligne : 09/07/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101575
in Computers & geosciences > vol 167 (October 2022) . - n° 105192[article]A relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
[article]
Titre : A relation-augmented embedded graph attention network for remote sensing object detection Type de document : Article/Communication Auteurs : Shu Tian, Auteur ; Lihong Kang, Auteur ; Xiangwei Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1000718 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] détection d'objet
[Termes IGN] graphe
[Termes IGN] image à haute résolution
[Termes IGN] information sémantique
[Termes IGN] relation sémantique
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal de graphes
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method. Numéro de notice : A2022-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3073269 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3073269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101788
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 1000718[article]The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)PermalinkThe iterative convolution–thresholding method (ICTM) for image segmentation / Dong Wang in Pattern recognition, vol 130 (October 2022)PermalinkComparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska / Jiang Chen in Geocarto international, vol 37 n° 20 ([20/09/2022])PermalinkForest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (September-15 2022)PermalinkThe FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation / Shuaijun Liu in Remote sensing of environment, vol 279 (September-15 2022)PermalinkAnalytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)PermalinkForest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)PermalinkHistorical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)PermalinkLarge-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt / André Bertoncini in Remote sensing of environment, vol 278 (September 2022)Permalink