MotifGen is the first multi-source generative model for spatiotemporal interpolation of misaligned microwave cyclone images from heterogeneous instruments at irregular intervals, achieving lower CRPS via self-supervised training and closer power spectra than deterministic baselines when combining in
TerraMind: Large-scale generative multimodality for Earth observation
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale representations combining both token-level and pixel-level data across modalities. On a token level, TerraMind encodes high-level contextual information to learn cross-modal relationships, while on a pixel level, TerraMind leverages fine-grained representations to capture critical spatial nuances. We pretrained TerraMind on nine geospatial modalities of a global, large-scale dataset. In this paper, we demonstrate that (i) TerraMind's dual-scale early fusion approach unlocks a range of zero-shot and few-shot applications for Earth observation, (ii) TerraMind introduces "Thinking-in-Modalities" (TiM) -- the capability of generating additional artificial data during finetuning and inference to improve the model output -- and (iii) TerraMind achieves beyond state-of-the-art performance in community-standard benchmarks for EO like PANGAEA. The pretraining dataset, the model weights, and our code are open-sourced under a permissive license.
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LUCAS-MEGA fuses 68 heterogeneous soil datasets into a 70k-sample multimodal collection and demonstrates its value by pretraining a tabular transformer whose representations align with established soil processes.
COP-GEN models multimodal Copernicus Earth observation data as conditional distributions via a latent diffusion transformer, producing diverse physically consistent outputs and covering 90% of the real observation manifold on a new stochastic benchmark.
A new spatial affinity component for self-supervised pretraining leverages high-resolution imagery to enhance mid-resolution satellite image representations and segmentation 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.
A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.
citing papers explorer
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MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones
MotifGen is the first multi-source generative model for spatiotemporal interpolation of misaligned microwave cyclone images from heterogeneous instruments at irregular intervals, achieving lower CRPS via self-supervised training and closer power spectra than deterministic baselines when combining in
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LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems
LUCAS-MEGA fuses 68 heterogeneous soil datasets into a 70k-sample multimodal collection and demonstrates its value by pretraining a tabular transformer whose representations align with established soil processes.
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COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data
COP-GEN models multimodal Copernicus Earth observation data as conditional distributions via a latent diffusion transformer, producing diverse physically consistent outputs and covering 90% of the real observation manifold on a new stochastic benchmark.
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Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation
A new spatial affinity component for self-supervised pretraining leverages high-resolution imagery to enhance mid-resolution satellite image representations and segmentation 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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Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.