# Imbalance-aware Presence-only Loss Function for Species Distribution Modeling

arXiv: https://arxiv.org/abs/2403.07472 · HTML: https://mlsanity.com/paper/2403.07472/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks](https://mlsanity.com/paper/2310.06743/) (2023)
- [Spatial Implicit Neural Representations for Global-Scale Species Mapping](https://mlsanity.com/paper/2306.02564/) (2023)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning](https://mlsanity.com/paper/2401.02989/) (2024)

## Built on by (most cited first, 4 of 4)

- [Combining Observational Data and Language for Species Range Estimation](https://mlsanity.com/paper/2410.10931/) (2024)
- [Multi-Scale and Multimodal Species Distribution Modeling](https://mlsanity.com/paper/2411.04016/) (2024)
- [Feedforward Few-shot Species Range Estimation](https://mlsanity.com/paper/2502.14977/) (2025)
- [When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?](https://mlsanity.com/paper/2606.26053/) (2026)

## Abstract (arXiv)

In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations.