BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping

Srikumar Sastry, Subash Khanal, Aayush Dhakal, Di Huang, Nathan Jacobs; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 7136-7145

We propose a metadata-aware self-supervised learning (SSL) framework useful for fine-grained classification and ecological mapping of bird species around the world. Our framework unifies two SSL strategies: Contrastive Learning (CL) and Masked Image Modeling (MIM), while also enriching the embedding space with meta-information available with ground-level imagery of birds. We separately train uni-modal and cross-modal ViT on a novel cross-view global birds species dataset containing ground-level imagery, metadata (location, time), and corresponding satellite imagery. We demonstrate that our models learn fine-grained and geographically conditioned features of birds, by evaluating on two downstream tasks: fine-grained visual classification (FGVC) and cross-modal retrieval. Pre-trained models learned using our framework achieve SotA performance on FGVC of iNAT-2021 birds as well as in transfer learning setting for CUB-200-2011 and NABirds datasets. Moreover, the impressive cross-modal retrieval performance of our model enables the creation of species distribution maps across any geographic region. The dataset and source code will be released at https://github.com/TBD.