16 citations · 21 across the 3 of their papers we have counts for
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cs.LG2023★ 4 cited
Challenges in data-based geospatial modeling for environmental research and practice
Diana Koldasbayeva, Polina Tregubova, Mikhail Gasanov +3
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research.…
cs.LG2023★ 1 cited
Correcting sampling biases via importance reweighting for spatial modeling
Boris Prokhorov, Diana Koldasbayeva, Alexey Zaytsev
In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introd…