SENSE is a controllable diffusion model that jointly generates realistic urban satellite imagery and aligned building energy consumption and height maps from road networks and density inputs, improving downstream tasks with under 20% labeled data.
A billion- scale foundation model for remote sensing images
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RSEdit adapts off-the-shelf text-to-image models into a collection of editing systems that follow text instructions while keeping geospatial structure intact in remote sensing images.
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
Geo-R1 uses reasoning-centric reinforcement fine-tuning to improve few-shot performance and generalization in geospatial referring expression understanding over supervised baselines.
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.
MeCSAFNet reports mIoU gains of 4.8-19.6% over U-Net and SegFormer baselines on FBP and Potsdam datasets by processing spectral channels separately and fusing features with CBAM attention.
citing papers explorer
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SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
SENSE is a controllable diffusion model that jointly generates realistic urban satellite imagery and aligned building energy consumption and height maps from road networks and density inputs, improving downstream tasks with under 20% labeled data.
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RSEdit: Text-Guided Image Editing for Remote Sensing
RSEdit adapts off-the-shelf text-to-image models into a collection of editing systems that follow text instructions while keeping geospatial structure intact in remote sensing images.
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Earth Embeddings Reveal Diverse Urban Signals from Space
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
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Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning
Geo-R1 uses reasoning-centric reinforcement fine-tuning to improve few-shot performance and generalization in geospatial referring expression understanding over supervised baselines.
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Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.
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Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation
MeCSAFNet reports mIoU gains of 4.8-19.6% over U-Net and SegFormer baselines on FBP and Potsdam datasets by processing spectral channels separately and fusing features with CBAM attention.