Welcome to Nenya’s documentation!
Nenya is a machine learning framework for analyzing ocean satellite imagery, particularly sea surface temperature data from MODIS and VIIRS sensors. It uses self-supervised learning techniques and dimensionality reduction (UMAP) to analyze and visualize patterns in ocean imagery.
Key Features
Self-supervised learning with contrastive approaches (SimCLR/SupCon)
Extraction of meaningful latent space representations from satellite images
UMAP dimensionality reduction for visualization
Interactive web portal for data exploration
Utilities for working with MODIS and VIIRS data
Tools for regional ocean analysis
Contents
Getting Started
User Guide
API Reference
Development