Unified Multi-Application Segmentation of High-Resolution Satellite Images Through Consensus-Based Probabilistic Fusion

Deep-learning-based land cover segmentation models for high-resolution aerial images often suffer from inconsistent performance on unseen data due to limited spatial and temporal dataset ranges alongside inherent labeling inconsistencies. Existing solutions, such as foundation models and unsupervised domain adaptation, offer indirect improvements but require considerable post-processing or transfer learning. To address this limitation, this study introduces a direct dataset integration method that fuses heterogeneous datasets with disparate class compositions into a single operational framework. For each individual dataset, multiple model instances (MMIs) predict pixel-wise class probabilities. These probabilities are subsequently combined within a single, unified, and customizable label space, enabling improved generalization to unseen domains without additional manual annotation. We applied and validated this method using three benchmark remote sensing datasets: OpenEarthMap, FLAIR, and the cloud dataset based on high-resolution optical satellite imagery (KOMPSAT-3 and 3A), sourced from the official AI-Hub platform in South Korea. Across both 2-fold and holdout tests, statistical validation confirmed that the unified model enhances generalization on untrained imagery (p < 0.0001) while reliably maintaining or slightly improving original intra-dataset accuracy. Furthermore, it successfully consolidates diverse land-cover categories with atmospheric features such as clouds, which were previously handled only by separate dedicated models.