SpecTM uses spectral targeted masking in multi-task self-supervised pretraining to reach R²=0.695 current-week and R²=0.620 8-day-ahead microcystin predictions on NASA PACE Lake Erie data, beating baselines with 2.2x better label efficiency.
RingMo: A Remote Sensing Foundation M odel With Masked Image Modeling
4 Pith papers cite this work. Polarity classification is still indexing.
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SpectralEarth-FM is a multisensor hierarchical transformer pretrained on a 40TB co-located HSI-MSI-SAR dataset using a JEPA-style objective and reports state-of-the-art results on hyperspectral and standard EO benchmarks.
PerASCD sets new state-of-the-art Sek scores on SECOND and LandsatSCD datasets by using a modular cascaded gated decoder on PerA foundation model features plus a new consistency loss.
LandSegmenter creates a task-specific foundation model for LULC mapping using weak labels from existing products, an RS adapter, text encoder, and confidence-guided fusion to achieve competitive zero-shot performance across modalities and taxonomies.
citing papers explorer
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SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models
SpecTM uses spectral targeted masking in multi-task self-supervised pretraining to reach R²=0.695 current-week and R²=0.620 8-day-ahead microcystin predictions on NASA PACE Lake Erie data, beating baselines with 2.2x better label efficiency.
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SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
SpectralEarth-FM is a multisensor hierarchical transformer pretrained on a 40TB co-located HSI-MSI-SAR dataset using a JEPA-style objective and reports state-of-the-art results on hyperspectral and standard EO benchmarks.
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Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery
PerASCD sets new state-of-the-art Sek scores on SECOND and LandsatSCD datasets by using a modular cascaded gated decoder on PerA foundation model features plus a new consistency loss.
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LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping
LandSegmenter creates a task-specific foundation model for LULC mapping using weak labels from existing products, an RS adapter, text encoder, and confidence-guided fusion to achieve competitive zero-shot performance across modalities and taxonomies.