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Roberts 2022 CaGEO Pyeo forest cover change detection.pdf (4.2 MB)
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Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning

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posted on 2022-08-30, 09:44 authored by JF Roberts, R Mwangi, F Mukabi, J Njui, K Nzioka, JK Ndambiri, PC Bispo, FDB Espirito-Santo, Y Gou, SCM Johnson, V Louis, AM Pacheco-Pascagaza, P Rodriguez-Veiga, K Tansey, C Upton, C Robb, H Balzter

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.


University of Leicester's Global Prospects Fund, the Natural Environment Research Council (NERC) Follow-on fund (NE/N017021/1) and a NERC Pathfinder grant (NE/M007839/1), the National Centre for Earth Observation (NCEO), the UK Space Agency International Partnership Fund projects Forests 2020 and EASOS and the European Space Agency project ForestMind.

NERC National Centre for Earth Observation (PR140015)


Author affiliation

Institute for Environmental Futures, Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester


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Elsevier BV



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