The field of conservation and search systems is experiencing significant growth, driven by advances in multimodal search and AI-driven technologies. Recent developments have focused on improving access and discoverability of large datasets, such as government documents and wildlife images. Notably, the integration of synthetic imagery and generative AI techniques has shown promise in addressing data limitations and enhancing model performance. Furthermore, the creation of large-scale datasets and benchmarks, such as SA-FARI and BioBench, is providing new foundations for advancing generalizable multianimal tracking and ecology vision tasks.

Some particularly noteworthy papers in this regard include: GovScape, which introduces a public multimodal search system for 70 million pages of government PDFs, demonstrating the potential for immediate scalability. The SA-FARI Dataset, which presents the largest open-source multianimal tracking dataset for wild animals, offering a new foundation for advancing generalizable multianimal tracking in the wild. BioBench, which provides an open ecology vision benchmark that captures what ImageNet misses, offering a template recipe for building reliable AI-for-science benchmarks in any domain.