Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation
arXiv:2606.25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity. However, such metrics, including the widely used FID, focus on visual fidelity without reflecting downstream utility, and can diverge from human per
The proliferation of deep learning for Earth Observation (EO) necessitates robust data quality assurance, especially as synthetic data generation becomes more prevalent for mitigating data scarcity.
This research highlights a critical divergence between current AI data quality metrics and actual downstream utility, impacting the reliability and trustworthiness of AI models for Earth Observation.
A framework for aligning data quality metrics with human judgment and segmentation performance will improve the efficacy of synthetic data generation and overall model development in EO.
- · AI model developers
- · Earth Observation sector
- · Satellite imagery providers
- · Data scientists
- · Untuned synthetic data generators
- · Low-quality EO data providers
Improved performance and reliability of AI models in Earth Observation applications.
Reduced operational costs and more efficient resource allocation for environmental monitoring and disaster response.
Enhanced trust in AI-driven decision-making within critical environmental and geopolitical contexts.
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Read at arXiv cs.LG