Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning - Roberts, J.F.; Mwangi, R.; Mukabi, F.; Njui, J.; Nzioka, K.; Ndambiri, J.K.; Bispo, P.C.; Espirito-Santo, F.D.B.; Gou, Y.; Johnson, S.C.M.; Louis, V.; Pacheco-Pascagaza, A.M.; et al. (Computers & geosciences, 2022)
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. •Highlight 1: A Python package for Earth observation processing chains for change detection is presented.•Highlight 2: Data can be processed in near-real-time whenever a new satellite image is acquired.•Highlight 3: The satellite change detection algorithm informs the user of detected change events.•Highlight 4: An application to a forest in Kenya is presented to demonstrate the software.•Highlight 5: This software is used by the Kenya Forest Service for monitoring deforestation from Sentinel-2.