  Geospatial technologies are becoming increasingly essential in our world for
a wide range of applications, including agriculture, urban planning, and
disaster response. To help improve the applicability and performance of deep
learning models on these geospatial tasks, various works have begun
investigating foundation models for this domain. Researchers have explored two
prominent approaches for introducing such models in geospatial applications,
but both have drawbacks in terms of limited performance benefit or prohibitive
training cost. Therefore, in this work, we propose a novel paradigm for
building highly effective geospatial foundation models with minimal resource
cost and carbon impact. We first construct a compact yet diverse dataset from
multiple sources to promote feature diversity, which we term GeoPile. Then, we
investigate the potential of continual pretraining from large-scale
ImageNet-22k models and propose a multi-objective continual pretraining
paradigm, which leverages the strong representations of ImageNet while
simultaneously providing the freedom to learn valuable in-domain features. Our
approach outperforms previous state-of-the-art geospatial pretraining methods
in an extensive evaluation on seven downstream datasets covering various tasks
such as change detection, classification, multi-label classification, semantic
segmentation, and super-resolution.
