Predicting butterfly species presence from satellite imagery using soft contrastive regularisation

TL;DR

This paper introduces a new dataset and a contrastive regularisation technique to improve multi-species butterfly presence prediction from satellite imagery, advancing remote sensing biodiversity monitoring.

Contribution

It presents a novel dataset for butterfly species prediction from satellite images and develops a soft contrastive regularisation method tailored for probabilistic labels.

Findings

The Resnet-based model outperforms baseline in high biodiversity areas.

Contrastive regularisation improves prediction accuracy.

The dataset enables scalable biodiversity monitoring from satellite data.

Abstract

The growing demand for scalable biodiversity monitoring methods has fuelled interest in remote sensing data, due to its widespread availability and extensive coverage. Traditionally, the application of remote sensing to biodiversity research has focused on mapping and monitoring habitats, but with increasing availability of large-scale citizen-science wildlife observation data, recent methods have started to explore predicting multi-species presence directly from satellite images. This paper presents a new data set for predicting butterfly species presence from satellite data in the United Kingdom. We experimentally optimise a Resnet-based model to predict multi-species presence from 4-band satellite images, and find that this model especially outperforms the mean rate baseline for locations with high species biodiversity. To improve performance, we develop a soft, supervised contrastive regularisation loss that is tailored to probabilistic labels (such as species-presence data), and demonstrate that this improves prediction accuracy. In summary, our new data set and contrastive regularisation method contribute to the open challenge of accurately predicting species biodiversity from remote sensing data, which is key for efficient biodiversity monitoring.