  In the realm of geospatial analysis, the diversity of remote sensors,
encompassing both optical and microwave technologies, offers a wealth of
distinct observational capabilities. Recognizing this, we present msGFM, a
multisensor geospatial foundation model that effectively unifies data from four
key sensor modalities. This integration spans an expansive dataset of two
million multisensor images. msGFM is uniquely adept at handling both paired and
unpaired sensor data. For data originating from identical geolocations, our
model employs an innovative cross-sensor pretraining approach in masked image
modeling, enabling the synthesis of joint representations from diverse sensors.
msGFM, incorporating four remote sensors, upholds strong performance, forming a
comprehensive model adaptable to various sensor types. msGFM has demonstrated
enhanced proficiency in a range of both single-sensor and multisensor
downstream tasks. These include scene classification, segmentation, cloud
removal, and pan-sharpening. A key discovery of our research is that
representations derived from natural images are not always compatible with the
distinct characteristics of geospatial remote sensors, underscoring the
limitations of existing representations in this field. Our work can serve as a
guide for developing multisensor geospatial pretraining models, paving the way
for more advanced geospatial capabilities.
