Dataset Overview: This dataset and the associated LoRA-adapted model weights (FLUX.1 Kontext, FLUX.2, and Qwen) provide the experimental framework and results for the study: 'Parameter-Efficient Adaptation of Generative-Foundation (Flux, Qwen) vs. Zero-Shot (Gemini, SAM3) Models for Aerial Image Segmentation', published in Buildings (MDPI), 2026, 16(7), 1369 https://doi.org/10.3390/buildings16071369 . The records include the Brisbane metropolitan training/testing tiles and the fine-tuned adapters that achieved a peak mean Intersection over Union (IoU) of 89% and 96% pixel accuracy, demonstrating the efficacy of generative foundation models for high-precision spatial analysis. Technical Specifications: · Geographic Focus: Brisbane, Australia (Metropolitan regions). · Task: Binary Semantic Segmentation (Rooftop vs. Non-rooftop). · Data Split: 121 total tiles (80/20 split)- Training: 97 tiles. · Testing: 24 tiles.Format: High-resolution aerial imagery paired with dense binary annotations. Benchmarked Models & Performance: This dataset serves as the benchmark for several state-of-the-art architectures evaluated under a unified protocol: Model Category Name IoU Zero-Shot Gemini 3 Pro 85% Segmentation Baseline SAM3 (Segment Anything Model) 84% LoRA-Adapted Diffusion FLUX.1-Kontext 89% Key Research Insights: · Diffusion as Segmentors: The dataset demonstrates that with only 250–5000 steps of LoRA fine-tuning, generative models like FLUX.1-Kontext can outperform dedicated segmentation models (SAM3), reaching a mean IoU of 89%. · Generalization: While Gemini 3 Pro provides a powerful zero-shot baseline, fine-tuned diffusion models offer superior boundary fidelity in heterogeneous urban morphologies. · Efficiency: The results highlight that parameter-efficient tuning is a viable path for transforming general-purpose AI into scalable spatial analysis tools for urban monitoring and solar potential assessment. Citation Dataset could be cited from: https://explore.openaire.eu/search/result?pid=10.5281%2Fzenodo.18571111 APA style ciataion: "Shata, D., Denman, S., Omrani, S., Drogemuller, R., Ali, H., & Wagdy, A. (2026). Parameter-Efficient Adaptation of Generative-Foundation (Flux, Qwen) vs. Zero-Shot (Gemini, SAM3) Models for Aerial Image Segmentation. https://doi.org/10.5281/zenodo.18571111" APA style Publication ciataion: "Shata, D., Denman, S., Omrani, S., Drogemuller, R., Ali, H., & Wagdy, A. (2026). Parameter-Efficient Adaptation of Generative-Foundation (Flux, Qwen) vs. Zero-Shot (Gemini, SAM3) Models for Aerial Image Segmentation. Buildings, 16(7), 1369. https://doi.org/10.3390/buildings16071369"