  Satellite-based remote sensing has revolutionised the way we address global
challenges in a rapidly evolving world. Huge quantities of Earth Observation
(EO) data are generated by satellite sensors daily, but processing these large
datasets for use in ML pipelines is technically and computationally
challenging. Specifically, different types of EO data are often hosted on a
variety of platforms, with differing availability for Python preprocessing
tools. In addition, spatial alignment across data sources and data tiling can
present significant technical hurdles for novice users. While some preprocessed
EO datasets exist, their content is often limited to optical or near-optical
wavelength data, which is ineffective at night or in adverse weather
conditions. Synthetic Aperture Radar (SAR), an active sensing technique based
on microwave length radiation, offers a viable alternative. However, the
application of machine learning to SAR has been limited due to a lack of
ML-ready data and pipelines, particularly for the full diversity of SAR data,
including polarimetry, coherence and interferometry. We introduce M3LEO, a
multi-modal, multi-label EO dataset that includes polarimetric,
interferometric, and coherence SAR data derived from Sentinel-1, alongside
Sentinel-2 RGB imagery and a suite of labelled tasks for model evaluation.
M3LEO spans 17.5TB and contains approximately 10M data chips across six
geographic regions. The dataset is complemented by a flexible PyTorch Lightning
framework, with configuration management using Hydra. We provide tools to
process any dataset available on popular platforms such as Google Earth Engine
for integration with our framework. Initial experiments validate the utility of
our data and framework, showing that SAR imagery contains information
additional to that extractable from RGB data. Data at huggingface.co/M3LEO, and
code at github.com/spaceml-org/M3LEO.
