AlphaEarth land-cover priors improve SAR flood segmentation IoU over SAR-only and DEM baselines across CNN and ViT backbones on held-out events like Hurricane Florence.
Ter- ratorch: The geospatial foundation models toolkit
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Standardized pretraining and evaluation of geospatial multimodal foundation models on GEOBench reveals design trade-offs in flexibility, modality alignment, and task performance.
LIANet encodes multi-temporal Earth observation data into a coordinate-based neural field that supports label-only fine-tuning for downstream tasks without access to raw imagery.
Transformer backbones with mean pooling and combined self-supervised embeddings yield robust, compact representations for EO tasks that are over 500x smaller than raw data.
citing papers explorer
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Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones
AlphaEarth land-cover priors improve SAR flood segmentation IoU over SAR-only and DEM baselines across CNN and ViT backbones on held-out events like Hurricane Florence.
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Emerging Flexible Designs for Geospatial Multimodal Foundation Models
Standardized pretraining and evaluation of geospatial multimodal foundation models on GEOBench reveals design trade-offs in flexibility, modality alignment, and task performance.
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Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
LIANet encodes multi-temporal Earth observation data into a coordinate-based neural field that supports label-only fine-tuning for downstream tasks without access to raw imagery.
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How to Embed Matters: Evaluation of EO Embedding Design Choices
Transformer backbones with mean pooling and combined self-supervised embeddings yield robust, compact representations for EO tasks that are over 500x smaller than raw data.