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ARXIV:2604.02479 · GENERATIVE DATA AUGMENTATION · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02479GENERATIVE DATA AUGMENTATIONSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNValeria Martin · K. Brent Venable · Derek Morgan · arXiv
Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach.
Opportunity summary
Pain Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach.
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Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize…
The scarcity of labeled satellite imagery remains a fundamental bottleneck for deep-learning (DL)-based wildfire monitoring systems. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize realistic post-wildfire Sentinel-2…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that inpainting-based pipelines consistently outperform full-tile generation across all metrics, with the structured inpainting prompt achieving the best spatial alignment (Burn IoU…
Generative Data Augmentation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach.
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10.48550/arXiv.2604.02479Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach.
Abstract
The scarcity of labeled satellite imagery remains a fundamental bottleneck for deep-learning (DL)-based wildfire monitoring systems. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize realistic post-wildfire Sentinel-2 RGB imagery conditioned on existing burn masks, without task-specific retraining. Using burn masks derived from the CalFireSeg-50 dataset (Martin et al., 2025), we design and evaluate six controlled experimental configurations that systematically vary: (i) pipeline architecture (mask-only full generation vs. inpainting with pre-fire context), (ii) prompt engineering strategy (three hand-crafted prompts and a VLM-generated prompt via Qwen2-VL), and (iii) a region-wise color-matching post-processing step. Quantitative assessment on 10 stratified test samples uses four complementary metrics: Burn IoU, burn-region color distance (ΔC_burn), Darkness Contrast, and Spectral Plausibility. Results show that inpainting-based pipelines consistently outperform full-tile generation across all metrics, with the structured inpainting prompt achieving the best spatial alignment (Burn IoU = 0.456) and burn saliency (Darkness Contrast = 20.44), while color matching produces the lowest color distance (ΔC_burn = 63.22) at the cost of reduced burn saliency. VLM-assisted inpainting is competitive with hand-crafted prompts. These findings provide a foundation for incorporating generative data augmentation into wildfire detection pipelines. Code and experiments are available at: https://www.kaggle.com/code/valeriamartinh/genai-all-runned
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PROBLEM
Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesi...
METHOD
The scarcity of labeled satellite imagery remains a fundamental bottleneck for deep-learning (DL)-based wildfire monitoring systems. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize realistic post-wildfire...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that inpainting-based pipelines consistently outperform full-tile generation across all metrics, with the structured inpainting prompt achieving the best spatial alignment (Burn IoU = 0.456)...
WHY NOW
Generative Data Augmentation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize realistic post-wildfire Sentinel-2 RGB imagery conditioned on existing burn masks, without task-specific retraining.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The scarcity of labeled satellite imagery remains a fundamental bottleneck for deep-learning (DL)-based wildfire monitoring systems. This paper investigates whether a diffusion-based foundation model for Earth Observation (EO), EarthSynth, can synthesize realistic post-wildfire Sentinel-2 RGB imagery conditioned on existing burn masks, without task-specific retraining.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that inpainting-based pipelines consistently outperform full-tile generation across all metrics, with the structured inpainting prompt achieving the best spatial alignment (Burn IoU = 0.456) and burn saliency (Darkness Contrast = 20.44), while color matching produces the lowest color distance (ΔC_burn = 63.22) at the cost of reduced burn saliency. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative Data Augmentation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Generate realistic satellite imagery for wildfire detection using mask-conditioned diffusion models, addressing data scarcity with a novel inpainting approach.
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