EOImageNET - multiscale global dataset and DNN model for object detection on optical EO data

This package contains the EOImageNet dataset: a comprehensive multi-dataset training framework for object detection in remote sensing data (satellite, drone, aircraft images) that unifies 13 fully open-source remote sensing datasets together with the supplementary Tuatara dataset into a single standardized benchmark. Our approach addresses dataset heterogeneity through systematic preprocessing, annotation format unification, and a novel cross-labeling methodology that automatically annotates missing object classes across datasets with different taxonomies. The unified dataset encompasses 51,826 images with 810,551 annotated objects across 26 object classes, representing diverse geographic regions, imaging conditions, and object scales.This package uses following naming convention for dataset splits:- train.txt: Training set (41,208 images)- test.txt: Hyperparameter tuning set (10,316 images)- validation.txt: Final evaluation set (302 images)