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Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

Domaine:

geospatial

Type de record:

dataset
Créateur:
Alemayehu MidekisaFelix HollDavid J. SavoryRicardo Andrade-Pacheco
Éditeur:
Zenodo
Hôte:avatar

This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).

 

Visit

doi.org

Tasks

computer visionimage classification

Licenses

info:eu-repo/semantics/restrictedAccess

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