Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine

2026-08-17

AI Summary

This study addresses the challenges of scarce annotations and class imbalance in monitoring war-damaged farmland in Ukraine by proposing a synthetic data augmentation framework integrating conditional GANs and DDPMs, coupled with a Vision Transformer for classification. The research validates the efficacy of balanced DDPMs in geospatial few-shot scenarios, significantly mitigating data scarcity bottlenecks. Experimental results demonstrate that the model achieves 88% accuracy and a macro F1-score of 78%, while notably improving the recall for non-bombed areas from 41% to 69%. By establishing an efficient few-shot learning paradigm for war damage assessment, this work offers substantial practical value for real-world applications in conflict-affected agricultural monitoring.

Abstract

Monitoring war-induced damage to agricultural land in Ukraine is important for understanding threats to food security, environmental stability, and post-war recovery. However, the development of computer-vision systems for satellite-based damage analysis is limited by the scarcity of labeled imagery, especially for damaged agricultural fields. This work investigates synthetic data augmentation as a method for improving classification under limited and imbalanced training data. We train class-conditional Generative Adversarial Network (GAN) and Denoising Diffusion Probabilistic Model (DDPM) architectures on real satellite images and use them to generate additional bombed and not-bombed agricultural-field samples. The generated images are used only for training augmentation, while all downstream evaluation is performed on an exclusively real test set. A Vision Transformer classifier is trained under multiple real and synthetic data configurations to measure the practical utility of each generative approach. The best configuration, based on balanced DDPM augmentation, improves accuracy from 84\% to 88\%, balanced accuracy from 67\% to 81\%, macro F1 from 65\% to 78\%, and recall for the underrepresented not-bombed class from 41\% to 69\%. These results demonstrate the potential of synthetic satellite imagery for data-scarce geospatial applications in war-affected regions.

Problem

• Satellite Imagery
• Battle Damage Assessment
• Data Scarcity
• Class Imbalance
• Agricultural Monitoring

Innovation

• Synthetic Data Augmentation
• Denoising Diffusion Probabilistic Model
• Class-Conditional GAN
• Satellite Imagery
• Data Scarcity