Centre de documentation
scientifique

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]