Feedforward Few-shot Species Range Estimation

TL;DR

This paper introduces a feedforward model for few-shot species range estimation that predicts the potential distribution of unseen species using limited data, improving accuracy and efficiency over existing methods.

Contribution

The paper presents a novel feedforward approach for estimating species ranges from few observations, incorporating metadata, and achieving state-of-the-art results efficiently.

Findings

State-of-the-art performance on two benchmarks

Significantly reduced computation time

Effective use of limited data and metadata

Abstract

Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. By mapping the spatial ranges of all species, we would obtain deeper insights into how global biodiversity is affected by climate change and habitat loss. However, accurate range estimates are only available for a relatively small proportion of all known species. For the majority of the remaining species, we typically only have a small number of records denoting the spatial locations where they have previously been observed. We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data. During inference, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of a previously unseen species in a feedforward manner. We evaluate our approach on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches.