  I explored adapting Stable Diffusion v1.5 for generating domain-specific
satellite and aerial images in remote sensing. Recognizing the limitations of
existing models like Midjourney and Stable Diffusion, trained primarily on
natural RGB images and lacking context for remote sensing, I used the RSICD
dataset to train a Stable Diffusion model with a loss of 0.2. I incorporated
descriptive captions from the dataset for text-conditioning. Additionally, I
created a synthetic dataset for a Land Use Land Classification (LULC) task,
employing prompting techniques with RAG and ChatGPT and fine-tuning a
specialized remote sensing LLM. However, I faced challenges with prompt quality
and model performance. I trained a classification model (ResNet18) on the
synthetic dataset achieving 49.48% test accuracy in TorchGeo to create a
baseline. Quantitative evaluation through FID scores and qualitative feedback
from domain experts assessed the realism and quality of the generated images
and dataset. Despite extensive fine-tuning and dataset iterations, results
indicated subpar image quality and realism, as indicated by high FID scores and
domain-expert evaluation. These findings call attention to the potential of
diffusion models in remote sensing while highlighting significant challenges
related to insufficient pretraining data and computational resources.
