EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
Prediction of annual snow accumulation using a recurrent graph convolutional approach, in: IGARSS2023-2023IEEEInternationalGeoscienceandRemoteSensingSymposium
9 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.
Mixing real UAV imagery with 2101 AI-generated image-mask pairs improves semantic segmentation F1 scores for fine-grained forest species by over 15 percentage points overall and up to 30 points for rare classes.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.
Deployment-aligned low-precision NAS recovers about two-thirds of the accuracy drop from post-training quantization, achieving 0.826 mIoU on-device for a 95k-parameter model on Intel Movidius Myriad X without added complexity.
TerraQ is a spatiotemporal question-answering engine for satellite image archives that processes natural language requests involving image metadata and knowledge base entities.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
citing papers explorer
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EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents
EO-Gym supplies an executable multimodal environment and 9k-trajectory benchmark that turns Earth Observation into a tool-using, multi-step reasoning task, revealing that current VLMs struggle on temporal and cross-sensor workflows while fine-tuning lifts Pass@3 from 0.49 to 0.74.
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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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Leveraging Image Generators to Address Training Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
Mixing real UAV imagery with 2101 AI-generated image-mask pairs improves semantic segmentation F1 scores for fine-grained forest species by over 15 percentage points overall and up to 30 points for rare classes.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
K-STEMIT reduces RMSE by 21% for subsurface stratigraphy thickness estimation from radar data via a knowledge-informed spatio-temporal GNN with adaptive feature fusion and physical priors from the MAR weather model.
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Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Proposes ISensD and ESensI methods to increase robustness of multi-sensor EO models to missing sensors, with experiments on three temporal datasets showing ensemble models are most robust.
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Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI
Deployment-aligned low-precision NAS recovers about two-thirds of the accuracy drop from post-training quantization, achieving 0.826 mIoU on-device for a 95k-parameter model on Intel Movidius Myriad X without added complexity.
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TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives
TerraQ is a spatiotemporal question-answering engine for satellite image archives that processes natural language requests involving image metadata and knowledge base entities.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.