mlsanityData to 7 Oct 2026

MALPOLON: A Framework for Deep Species Distribution Modeling

arXiv 2409.18102 ↗First posted 26 Sep 2024cs.LG cs.CVcited by 1

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Abstract (arXiv)

This paper describes a deep-SDM framework, MALPOLON. Written in Python and built upon the PyTorch library, this framework aims to facilitate training and inferences of deep species distribution models (deep-SDM) and sharing for users with only general Python language skills (e.g., modeling ecologists) who are interested in testing deep learning approaches to build new SDMs. More advanced users can also benefit from the framework's modularity to run more specific experiments by overriding existing classes while taking advantage of press-button examples to train neural networks on multiple classification tasks using custom or provided raw and pre-processed datasets. The framework is open-sourced on GitHub and PyPi along with extensive documentation and examples of use in various scenarios. MALPOLON offers straightforward installation, YAML-based configuration, parallel computing, multi-GPU utilization, baseline and foundational models for benchmarking, and extensive tutorials/documentation, aiming to enhance accessibility and performance scalability for ecologists and researchers.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 26 Sep 2024.

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7 of 40 references.

References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
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  6. 2016Deep Multi-Species Embeddingcited by 20
  7. 2024GeoPlant: Spatial Plant Species Prediction Datasetcited by 16

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