EQ-VMamba adds rotation-equivariant cross-scan and group Mamba blocks to enforce end-to-end rotation equivariance, yielding better rotation robustness, competitive accuracy, and roughly 50% fewer parameters than non-equivariant baselines across classification, segmentation, and super-resolution.
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Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmentation
16 Pith papers cite this work. Polarity classification is still indexing.
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TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
GeoMMBench reveals deficiencies in current multimodal LLMs for geoscience tasks while GeoMMAgent demonstrates that tool-integrated agents achieve significantly higher performance.
A prompt-controlled diffusion framework generates class-ratio-targeted synthetic layouts and domain-consistent images that, when mixed with real data, improve segmentation accuracy on long-tailed remote-sensing datasets especially under domain shift.
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
UniGeoSeg releases the first million-scale dataset for instruction-driven remote sensing segmentation and a unified model that achieves state-of-the-art results with strong zero-shot generalization.
InduceKV is a retrieval-based continual adaptation method that uses bilevel selection to build a compact set of inducing KV memories for fixed-footprint updates to multimodal LLMs.
FROST performs training-free few-shot segmentation on remote-sensing imagery by nonparametric density-ratio classification on frozen DINOv3 features and reports 5.6 mIoU gains from one example across 17 benchmarks.
SeeCo is a training-free on-the-fly recalibration method using multi-view geometric consistency and adaptive textual calibration to improve open-vocabulary semantic segmentation in remote sensing images.
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
OVRSISBenchV2 expands open-vocabulary remote-sensing segmentation evaluation to 170K images and 128 categories, and Pi-Seg uses positive-incentive noise to improve transfer on that harder benchmark.
ProtoFlow models class prototypes as low-curvature trajectories with an explicit temporal vector field, reducing forgetting in class- and domain-incremental remote sensing segmentation by about 1.5–2.0 mIoU points.
Introduces the largest global aerial road segmentation dataset and RoadGIE, an interactive model using topology-aware prompts that reports SOTA accuracy and connectivity on the new benchmark with a 3.7M parameter network.
Muon optimizer outperforms AdamW in ViT training on two image datasets, with gains that depend on data augmentation strength and are linked to wider singular-value spread in QKV gradients and prevention of late-training mode collapse in MLP blocks.
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
citing papers explorer
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Rotation Equivariant Mamba for Vision Tasks
EQ-VMamba adds rotation-equivariant cross-scan and group Mamba blocks to enforce end-to-end rotation equivariance, yielding better rotation robustness, competitive accuracy, and roughly 50% fewer parameters than non-equivariant baselines across classification, segmentation, and super-resolution.
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Remote Sensing Image Super-Resolution for Imbalanced Textures: A Texture-Aware Diffusion Framework
TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
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GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
GeoMMBench reveals deficiencies in current multimodal LLMs for geoscience tasks while GeoMMAgent demonstrates that tool-integrated agents achieve significantly higher performance.
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Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation
A prompt-controlled diffusion framework generates class-ratio-targeted synthetic layouts and domain-consistent images that, when mixed with real data, improve segmentation accuracy on long-tailed remote-sensing datasets especially under domain shift.
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Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
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UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes
UniGeoSeg releases the first million-scale dataset for instruction-driven remote sensing segmentation and a unified model that achieves state-of-the-art results with strong zero-shot generalization.
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InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories
InduceKV is a retrieval-based continual adaptation method that uses bilevel selection to build a compact set of inducing KV memories for fixed-footprint updates to multimodal LLMs.
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FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics
FROST performs training-free few-shot segmentation on remote-sensing imagery by nonparametric density-ratio classification on frozen DINOv3 features and reports 5.6 mIoU gains from one example across 17 benchmarks.
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Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
SeeCo is a training-free on-the-fly recalibration method using multi-view geometric consistency and adaptive textual calibration to improve open-vocabulary semantic segmentation in remote sensing images.
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Diffusion Model as a Generalist Segmentation Learner
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
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Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline
OVRSISBenchV2 expands open-vocabulary remote-sensing segmentation evaluation to 170K images and 128 categories, and Pi-Seg uses positive-incentive noise to improve transfer on that harder benchmark.
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ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
ProtoFlow models class prototypes as low-curvature trajectories with an explicit temporal vector field, reducing forgetting in class- and domain-incremental remote sensing segmentation by about 1.5–2.0 mIoU points.
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RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
Introduces the largest global aerial road segmentation dataset and RoadGIE, an interactive model using topology-aware prompts that reports SOTA accuracy and connectivity on the new benchmark with a 3.7M parameter network.
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Muon in Vision Transformers: Optimizer-Recipe Interactions and Gradient Spectra
Muon optimizer outperforms AdamW in ViT training on two image datasets, with gains that depend on data augmentation strength and are linked to wider singular-value spread in QKV gradients and prevention of late-training mode collapse in MLP blocks.
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State Space Models Meet Remote Sensing: A Survey
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
- SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery