CaDS Seminar 2026 - May 5

Sayan Mandal (SDL AI and ML for Remote Sensing)

Biomazon: A Multimodal Benchmark for Full Vertical Structure and Biomass Modeling in the Amazon Basin

Abstract:

Accurate characterization of tropical forest vertical structure is critical for carbon accounting and ecosystem monitoring, yet most machine-learning pipelines reduce GEDI's rich waveform information to a single scalar, typically a high relative-height percentile as canopy height. This simplification discards the ordered height distribution that GEDI encodes across its full relative height (RH) profile, and that its own biomass algorithms depend on. We introduce Biomazon, an open, ML-ready multimodal benchmark dataset at 20 m resolution over the Amazon Basin, designed to support joint prediction of the full GEDI RH profile (RH0 to RH100) together with above-ground biomass density (AGBD). The dataset pairs GEDI-derived targets with multi-sensor predictors including Sentinel-1, Sentinel-2, ALOS-2 PALSAR-2, Copernicus DEM, Dynamic World land cover, and geospatial foundation model embeddings, all co-registered on a common grid with standardized spatial splits and evaluation protocols to enable reproducible comparison of methods. We formulate RH prediction as structured output learning with a monotonicity constraint that enforces physical consistency across percentiles, and we provide baseline results from systematic ablations over model scale, sensor contributions, and the role of AlphaEarth embeddings, both as standalone predictors and in fusion with raw modalities. Results are contextualized against existing gridded products to assess practical relevance. Biomazon addresses a gap in current benchmarking by shifting the task formulation from scalar regression toward structure-aware modeling, and by providing the community with an open, multi-sensor dataset and protocol for investigating when and how different data sources, including learned representations, contribute to forest structure and biomass retrieval in tropical forests.