Omkar2118/datasets huggingface dataset:
Satellite

The Satellite dataset forms a practical VFL scenario for location identification based on satellite imagery. Each AOI, with its unique location identifier, is captured by 16 satellite visits. Assuming each visit is carried out by a distinct satellite organization, these organizations aim to collectively train a model to classify the land type of the location without sharing original images. The Satellite dataset encompasses four land types as labels, namely Amnesty POI (4.8%), ASMSpotter (8.9%), Landcover (61.3%), and UNHCR (25.0%), making the task a 4-class classification problem of 3927 locations, containing 62,832 images across 16 parties, simulating a practical VFL scenario of collaborative location identification via multiple satellites.

This ZIP file comprises 32 CSV files, corresponding to training and testing datasets split at a ratio of 8:2. Each training and testing file contains 3,142 and 785 flattened images from a party, respectively.

Introducing paper: VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks