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Implementation of a service solution to automate the storage and retrieval of satellite data used by Geotree / Maeve Blarel (2023)

Public Titre : Implementation of a service solution to automate the storage and retrieval of satellite data used by Geotree : Scaling and refining Earth Observation data processing for nature-based solutions development Type de document : Mémoire Auteurs : Maeve Blarel, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2023 Importance : 97 p. Format : 21 x 30 cm Note générale : bibliographie Rapport de fin d'étude, cycle des Ingénieurs diplômés de l’ENSG 3ème année, Spécialité TSI Langues : Anglais (eng) Descripteur : [Termes IGN] carbone [Termes IGN] gaz à effet de serre [Termes IGN] Python (langage de programmation) [Termes IGN] système de gestion de base de données [Termes IGN] télédétection Mots-clés libres : Cloud  CO2  Docker  Gaz à Effet de Serre  Git  Marché du carbone  Python  Serviced’API  SGBD  Standard STAC  Télédétection  Traitement des données Index. décimale : MTSI Mémoires du Master Technologies des Systèmes d'Information Résumé : Carried out at Geotree, in Austria, and in collaboration with Mantle Labs, this Final Year Project (FWP) is part of the problem of scaling up and perfecting the processing of Earth observation data for the development of nature-based solutions. Geotree and MantleLabs are working together on Earth monitoring projects as part of the carbon market. With its extensive expertise in remote sensing, Geotree deploys a digital twin of the Earth that unlocks nature-based solutions.For its part, Mantle Labs is contributing its extensive experience in the use of satellite data, with the help of an international team. Through its cutting-edge tools for monitoring and verifying carbon sinks, Geotree provides scientific support for this market. The aim of this internship was to implement a service solution to automate the storage and retrieval of satellite data, central to Geotree. Indeed, quick and easy access to a large amount of data is becoming a common need. After an analysis phase of the STAC data standard, the work on this project consisted of developing an IT solution for the management and storage of satellite data. There are a number of prospects for this project. Finalising the deployment of the API on the Cloud receiving the solution is essential for its future use by the company’s team of data scientists. On the other hand, this API will be able to accommodate more different data (not standardised by STAC) and other functions (read function). Note that the Python codes, functional and commented, implemented during the internship is accessible via the Github continuous integration platform, but remains the property of Geotree. Con?sequently, no script from the source code will be presented in this report. Note de contenu : Introduction 1. Internchip presentation 2. STAC standard 3. Solution architecture 4. Project management Conclusion Numéro de notice : 24172 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Mémoire de fin d'études IT Organisme de stage : Geotree / Mantle Labs (Vienne) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103729

An automated approach for clipping geographic data before projection that maintains data integrity and minimizes distortion for virtually any projection method / Jim Graham in Cartographica, Vol 57 n° 4 (December 2022)  

Public [article]  inCartographica > Vol 57 n° 4 (December 2022) . - pp 257 - 269 Titre : An automated approach for clipping geographic data before projection that maintains data integrity and minimizes distortion for virtually any projection method Type de document : Article/Communication Auteurs : Jim Graham, Auteur Année de publication : 2022 Article en page(s) : pp 257 - 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Projections [Termes IGN] carroyage [Termes IGN] intégrité des données [Termes IGN] polygone [Termes IGN] projection [Termes IGN] Python (langage de programmation) Résumé : (auteur) Selecting a map projection is key to minimizing distortion and thus clear communication of spatial data and accurate spatial analysis. Methods exist for selecting projections based on the intended area of use but not for finding polygons that can be used to clip geographic data to ensure the data are projected correctly and within desired distortion limits. The projection methods available in the Proj library were examined to determine the nature of the errors and distortions they created based on global data and a wide variety of available settings. Approaches were then identified for each projection including simple bounding boxes and more complex clipping polygons. To make sure that errors were not introduced into the projected data, data integrity polygons (DIPs) were created by placing a grid of cells over the Earth and then finding a cell near the origin that was within the specified criteria. Adjacent cells were added to the DIPs that met the criteria until no additional cells could be added. The criteria included projected cell sides could not intersect with themselves or other cells, the order of the cell corners could not be reversed, and distortion within the cell had to be within specified limits. I found that up to two DIPs with a limit on length distortion of a factor of 4 provided a general solution for all but three projection methods. Limitations included the time to find DIPs at high resolution. Clipping polygons and visualizations of the results were made available on a website. Numéro de notice : A2022-923 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart-2021-0015 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.3138/cart-2021-0015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102465 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
031-2022041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds / Zhilin Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 11 (November 2022) . - n° 5705111 Titre : Graph-based leaf–wood separation method for individual trees using terrestrial lidar point clouds Type de document : Article/Communication Auteurs : Zhilin Tian, Auteur ; Shihua Li, Auteur Année de publication : 2022 Article en page(s) : n° 5705111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bois [Termes IGN] branche (arbre) [Termes IGN] chemin le plus court, algorithme du [Termes IGN] données lidar [Termes IGN] échantillonnage de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] feuille (végétation) [Termes IGN] graphe [Termes IGN] Python (langage de programmation) [Termes IGN] segmentation [Termes IGN] semis de points Résumé : (auteur) Terrestrial light detection and ranging (lidar) is capable of resolving trees at the branch/leaf level with accurate and dense point clouds. The separation of leaf and wood components is a prerequisite for the estimation of branch/leaf-scale biophysical properties and realistic tree model reconstruction. Most existing methods have been tested on trees with similar structures; their robustness for trees of different species and sizes remains relatively unexplored. This study proposed a new graph-based leaf–wood separation (GBS) method for individual trees purely using the xyz -information of the point cloud. The GBS method fully utilized the shortest path-based features, as the shortest path can effectively reflect the structures for trees of different species and sizes. Ten types of tree data—covering tropical, temperate, and boreal species—with heights ranging from 5.4 to 43.7 m, were used to test the method performance. The mean accuracy and kappa coefficient at the point level were 94% and 0.78, respectively, and our method outperformed two other state-of-the-art methods. Through further analysis and testing, the GBS method exhibited a strong ability for detecting small and leaf-surrounded branches, and was also sufficiently robust in terms of data subsampling. Our research further demonstrated the potential of the shortest path-based features in leaf–wood separation. The entire framework was provided for use as an open-source Python package, along with our labeled validation data. Numéro de notice : A2022-853 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3218603 Date de publication en ligne : 01/11/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3218603 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102099 [article]

Novel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)  

Public [article]  inForests > vol 13 n° 10 (October 2022) . - n° 1534 Titre : Novel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud Type de document : Article/Communication Auteurs : Jie Yang, Auteur ; Xiaorong Wen, Auteur ; Qiulai Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] branche (arbre) [Termes IGN] C++ [Termes IGN] Chine [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] itération [Termes IGN] modélisation de la forêt [Termes IGN] semis de points [Termes IGN] squelettisation Résumé : (auteur) More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes. Numéro de notice : A2022-835 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13101534 Date de publication en ligne : 17/09/2022 En ligne : https://doi.org/10.3390/f13101534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102032 [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)  

Public [article]  inComputers & geosciences > vol 167 (October 2022) . - n° 105192 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 forestière Résumé : (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 [article]

A geospatial workflow for the assessment of public transit system performance using near real-time data / Anastassios Dardas in Transactions in GIS, vol 26 n° 4 (June 2022)  

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An algorithm to assist the robust filter for tightly coupled RTK/INS navigation system / Zun Niu in Remote sensing, vol 14 n° 10 (May-2 2022)  

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Applications of multi-image remote sensing / Roger Mari Molas (2022)  

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Construction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique / Houssayn Meriche (2022) 

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Création d’un indicateur de qualité de la desserte des transports pour des parcelles à une échelle locale / Nick Lin (2022)

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Generic programming in modern C++ for image processing / Michaël Roynard (2022)  

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Introduction à la géomatique pour le statisticien : quelques concepts et outils innovants de gestion, traitement et diffusion de l’information spatiale / François Sémécurbe (2022)  

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Preparation of the VENµS satellite data over Israel for the input into the GRASP data treatment algorithm / Maeve Blarel (2022) 

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Python software to transform GPS SNR wave phases to volumetric water content / Angel Martín in GPS solutions, vol 26 n° 1 (January 2022)  

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Remise en forme des données géographiques des biotopes en milieu ouvert du Luxembourg / Alexandre Nghien (2022) 

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