Data-Model Complexity Trade-Off in UAV-Acquired Ultra-High-Resolution Remote Sensing: Empirical Study on Photovoltaic Panel Segmentation

With the growing adoption of deep learning in remote sensing, the increasing diversity of models and datasets has made method selection and experimentation more challenging, especially for non-expert users. This study presents a comprehensive evaluation of photovoltaic panel segmentation using a large-scale ultra-high-resolution benchmark of over 25,000 manually annotated unmanned aerial vehicle image patches, systematically quantifying the impact of model and data characteristics. Our results indicate that increasing the spatial diversity of training data has a more substantial impact on training stability and segmentation accuracy than simply adding spectral bands or enlarging the dataset volume. Across all experimental settings, moderate-sized models (DeepLabV3_50, ResUNet50, and SegFormer B4) often provided the best trade-off between segmentation performance and computational efficiency, achieving an average Intersection over Union (IoU) of 0.8966 comparable to 0.8970 of larger models. Moreover, model architecture plays a more critical role than model size; as the ResUNet models consistently achieved higher mean IoU than both DeepLabV3 and SegFormer models, with average improvements of 0.047 and 0.143, respectively. Our findings offer quantitative guidance for balancing architectural choices, model complexity, and dataset design, ultimately promoting more robust and efficient deployment of deep learning models in high-resolution remote sensing applications.