Climate change is a major driver of biodiversity loss, changing the geographic range and abundance of many species. However, there remain significant knowledge gaps about the distribution of species, due principally to the amount of effort and expertise required for traditional field monitoring. We propose an approach leveraging computer vision to improve species distribution modelling, combining the wide availability of remote sensing data with sparse on-ground citizen science data. We introduce a novel task and dataset for mapping US bird species to their habitats by predicting species encounter rates from satellite images, along with baseline models which demonstrate the power of our approach. Our methods open up possibilities for scalably modelling ecosystems properties worldwide.

翻译：气候变化是生物多样性丧失的主要驱动因素，改变了众多物种的地理分布范围和种群丰度。然而，由于传统野外监测需要大量人力与专业知识投入，物种分布方面仍存在显著的知识空白。我们提出了一种利用计算机视觉改进物种分布建模的方法，将广泛可用的遥感数据与稀疏的地面公民科学数据相结合。通过引入一项创新任务与数据集，利用卫星图像预测物种遇见率，将美国鸟类物种与其栖息地建立映射关系，同时提供基线模型验证了该方法的效果。我们的技术为全球范围内可扩展的生态系统特性建模开辟了新途径。