SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification

Automatic supervised classification of satellite images with complex modelling such as deep neural networks requires the availability of representative training datasets. While there exists a plethora of datasets that can be used for this purpose, they are usually very heterogeneous and not interoperable. This prevents the combination of two or more training datasets for improving image classification tasks based on machine learning. To alleviate these problems, we propose a methodology for structuring and harmonising open training datasets on the basis of a series of fundamental attributes we put forward for any such dataset. By applying this methodology to seven representative open training datasets, we generate a harmonised collection called SatImNet. Its usefulness is demonstrated for enhanced satellite image classification and segmentation based on convolutional neural networks. Data and open source code are provided to ensure the reproducibility of all obtained results and facilitate the ingestion of additional datasets in SatImNet.