PhenoMap: Phenological Neural Fields for Ecological Mapping

Foundation models in scientific domains have predominantly organized representations around a single dominant axis of supervision. In biology, this axis has been species identity, typically captured via image–text contrastive pretraining or multimodal systems that use species as the binding modality. Yet for plants, visual appearance changes dynamically across developmental stages, a process known as phenology, making species an incomplete signature. Dedicated phenology models reduce this variability to narrow classification targets, while standard taxonomic foundation models encode species identity with bias to phenophases overrepresented in training distributions. To close this gap, we introduce PhenoMap, a multimodal model that leverages time, location, climate, and species to produce factorized species and phenology embeddings aligned with plant imagery. To train and benchmark our model, we curate Pheno3M, pairing citizen-science plant imagery and phenophase annotations with per-observation climate and satellite drivers, and an external expert-survey benchmark. We extensively evaluate PhenoMap on new phenological benchmarks built from Pheno3M, where it excels on compositional species $\times$ phenophase and fine-grained phenophase retrieval. We further demonstrate its utility by qualitatively showing phenological processes emerging when the spatiotemporal field is queried as a map, and evaluating those learned embeddings on USA-NPN expert observations. All code and data will be released upon acceptance.