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Št. zadetkov: 2
Global distribution of cattle, horses, goats, sheep and buffaloes at 1 km resolution for 2000–2022 based on subnational census data and spatiotemporal machine learning
Leandro Parente, Steffen Ehrmann, Tomislav Hengl, Steffen Fritz, Carmelo Bonannella, Žiga Malek, Dominik Wisser, Giuseppina Cinardi, Lindsey Sloat
Izvirni znanstveni članek
Oznake: census data;livestock;machine learning;areal regression;agriculture;random forest;gradient boosting tree;spatiotemporal modeling;global mapping;open data;
The article describes the production and evaluation of annual livestock densities and headcounts of cattle, horses, sheep, goats and buffaloes (including 95% probability prediction intervals) at 1 km spatial resolution for the 2000–2022 period using spatiotemporal machine learning. A compilation of ...
Leto: 2026 Vir: Biotehniška fakulteta (UL BF)
Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning
Leandro Parente, Lindsey Sloat, Vinicius Mesquita, Davide Consoli, Radost Stanimirova, Tomislav Hengl, Carmelo Bonannella, Nathália Teles, Ichsani Wheeler, Maria Hunter, Žiga Malek, Fred Stolle
Izvirni znanstveni članek
Oznake: grasslands;global grassland class;maps;spatiotemporal distribution;machine learning;
The paper describes the production and evaluation of global grassland extent mapped annually for 2000–2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, ...
Leto: 2024 Vir: Biotehniška fakulteta (UL BF)
Št. zadetkov: 2
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