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.

本研究评估了在基于大规模公众科学数据集上使用平衡的存在性损失函数训练深度学习模型的效果，证明这种处理数据不平衡的损失函数在各种数据集和任务中优于传统损失函数，特别是在精确建模稀有物种和有限观察情况下。

物种分布建模中的平衡意识存在性损失函数