toelt-llc/cloud_segmentation_comparative

This project is essentially a static research artifact for a paper published nearly three years ago. With only 25 stars and zero recent velocity, it lacks any community momentum or network effects. The defensibility is extremely low (2/10) because the architectures benchmarked (likely standard CNNs like UNet and SegNet) have been largely superseded by Vision Transformers (ViTs) and Foundation Models in the remote sensing domain (e.g., IBM/NASA's Prithvi or Segment Anything Model adaptations like SAM-Geo). While frontier labs like OpenAI or Anthropic don't target cloud segmentation specifically, the 'displacement' has already occurred via open-source foundation models that offer better zero-shot performance than the specific models tuned in this repository. Its primary value is as a historical reference for the BenchCloudVision dataset results rather than a living tool for production use.