Showing 2026 · cs.CVShow all
3 papers · 2 filters
cs.CV2026
AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations
Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar +1
Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel…
cs.CV2026
SPEAR NeXT Causal Latent Forecasting Across Multiple Horizons for Spectral Temporal Earth Representation Learning
Rajiv Ranjan, Udaiveer Singh, Shashank Tamaskar +1
Earth observation is inherently dynamic, yet temporal information in many foundation models is learned through reconstruction, invariance, or retrospective sequence summarization.…
cs.CV2026
Physically Typed and Geometry-Aware Representations for Earth Foundation Models
Rajiv Ranjan
Earth-observation (EO) foundation models have become exceptionally effective at learning se mantic, high-dimensional geospatial embeddings, while modern weather and climate models…