RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing Image Editing
Remote sensing image editing aims to modify remote sensing images according to natural language instructions while preserving geographic rules and sensor observation characteristics. Existing benchmarks mainly target natural images or general visual scenes, and thus may not fully capture the reasoning, regional control, and sensor-consistency abilities required in remote sensing editing. To fill this gap, we introduce RS-RIE-Bench, the first benchmark for reasoning-guided remote sensing image editing. RS-RIE-Bench organizes tasks into three categories: temporal reasoning, causal reasoning, and
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing I” ≈ “Tongyi-MAI/Z-Image-Turbo””
- PossiblePossibly related (embedding) · 45%Exploring self-distilled reasoning for supervised fine-tuning with Amazon Nova →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- LinkedLinked via arxiv author · 85%Zihan Qin →
“RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing Image Editing”
- LinkedLinked via arxiv author · 85%Boao Xu →
“RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing Image Editing”
- LinkedLinked via arxiv author · 85%Zhao Dong →
“RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing Image Editing”