Data Augmentation in Earth Observation: A Diffusion Model Approach

TL;DR

This paper introduces a novel four-stage data augmentation method using diffusion models to generate semantically diverse Earth Observation images, significantly enhancing AI model performance in data-scarce scenarios.

Contribution

The paper presents a new diffusion model-based augmentation approach tailored for EO imagery, addressing limitations of traditional methods in capturing natural and human-made changes.

Findings

Outperforms traditional augmentation techniques in diversity and accuracy

Generates semantically rich EO images with diffusion models

Improves AI model performance across multiple EO tasks

Abstract

High-quality Earth Observation (EO) imagery is essential for accurate analysis and informed decision making across sectors. However, data scarcity caused by atmospheric conditions, seasonal variations, and limited geographical coverage hinders the effective application of Artificial Intelligence (AI) in EO. Traditional data augmentation techniques, which rely on basic parameterized image transformations, often fail to introduce sufficient diversity across key semantic axes. These axes include natural changes such as snow and floods, human impacts like urbanization and roads, and disasters such as wildfires and storms, which limits the accuracy of AI models in EO applications. To address this, we propose a four-stage data augmentation approach that integrates diffusion models to enhance semantic diversity. Our method employs meta-prompts for instruction generation, vision-language models for rich captioning, EO-specific diffusion model fine-tuning, and iterative data augmentation. Extensive experiments using four augmentation techniques demonstrate that our approach consistently outperforms established methods, generating semantically diverse EO images and improving AI model performance.