naturalis/sdmdl
Species Distribution Modelling using Deep Learning
This helps ecologists, conservationists, and biodiversity researchers predict where species are likely to be found based on environmental conditions. You provide species occurrence data (latitude/longitude) and various environmental maps (like temperature, rainfall, or elevation). It then generates detailed global distribution maps as GeoTIFFs and PNGs, along with model performance metrics and insights into which environmental factors are most important.
Use this if you need to create accurate species distribution maps and understand the environmental factors driving them, especially when dealing with complex, non-linear relationships and correlated environmental variables.
Not ideal if you prefer simpler, traditional species distribution modeling methods like MaxEnt, or if you don't have environmental raster layers and species occurrence data readily available.
Stars
26
Forks
12
Language
Python
License
MIT
Category
Last pushed
Mar 27, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/naturalis/sdmdl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
GAA-UAM/scikit-fda
Functional Data Analysis Python package
mlr-org/mlr3
mlr3: Machine Learning in R - next generation
mlr-org/mlr3extralearners
Extra learners for use in mlr3.
mlr-org/mlr3book
Online version of Bischl, B., Sonabend, R., Kotthoff, L., & Lang, M. (Eds.). (2024). "Applied...
mlr-org/mlr3learners
Recommended learners for mlr3