ArticleScientific data2024
A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Mapping global resource driven nature loss in the mining sector from 2001 to 2022.Nature communications · 2026Article
- The fast and the focused: Balancing timely and accurate classification of deforestation and degradation drivers using remote sensing.PloS one · 2026Article
- A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon.Scientific data · 2024Article
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Authors and funding
8 authors.
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Abstract
Understanding direct deforestation drivers at a fine spatial and temporal scale is needed to design appropriate measures for forest management and monitoring. To achieve this, reference datasets with which to design Artificial Intelligence (AI) approaches to classify direct deforestation drivers within areas experiencing forest loss in a detailed, comprehensive and locally-adapted way are needed. This is the case for Cameroon, in the Congo Basin, which has known increasing deforestation rates in recent years. Here, we created an Earth Observation dataset with associated labels to classify detailed direct deforestation drivers in Cameroon, which includes satellite imagery (Landsat and PlanetScope) and auxiliary data on infrastructure and biophysical properties. The dataset provides the following fifteen labels: oil palm, timber, fruit, rubber and other-large scale plantations; grassland/shrubland; small-scale oil palm or maize plantations and other small-scale agriculture; mining; selective logging; infrastructure; wildfires; hunting; and other.
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