Update README.md

README.md

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 SKeMa is a Python-based tool for semantic segmentation of kelp in Sentinel-2 satellite imagery, developed for coastal monitoring. It uses a deep learning model from the Segmentation Models PyTorch library, with a MaxViT-Tiny-512 encoder and UNet decoder. The model outputs a binary GeoTIFF (kelp = 1, non-kelp = 0).
 
-**Authors**: Mohsen Ghanbari et al., Spectral Lab, University of Victoria  
+**Authors**: Mohsen Ghanbari, Neil Ernst, Taylor A. Denouden, Luba Y. Reshitnyk, Piper Steffen, Alena Wachmann, Eduardo Loos, Margot Hessing-Lewis, Maycira Costa  
 **License**: Creative Commons Attribution 4.0 International (CC-BY-4.0)  
 **Citation**: SKeMa models by Mohsen Ghanbari et al., SPECTRAL Remote Sensing Laboratory, University of Victoria, DOI: [10.57967/hf/6790](https://huggingface.co/m5ghanba/SKeMa)  
 **GitHub**: [m5ghanba/skema](https://github.com/m5ghanba/skema)  
@@ -105,7 +105,7 @@ Please cite as:
 
 ```bibtex
 @software{skema_2025,
-  author       = {Mohsen Ghanbari et al.},
+  author       = {Mohsen Ghanbari, Neil Ernst, Taylor A. Denouden, Luba Y. Reshitnyk, Piper Steffen, Alena Wachmann, Eduardo Loos, Margot Hessing-Lewis, Maycira Costa},
   title        = {SKeMa: Satellite-based Kelp Mapping using Semantic Segmentation on Sentinel-2 imagery},
   year         = 2025,
   publisher    = {Hugging Face},