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more, such natural image models are constantly being im- |
proved and released by the general computer vision commu- |
nity, providing a consistent source of better baseline mod- |
els. Therefore, an approach that could enable the geospa- |
tial domain to leverage these improvements with minimal |
resource needs and carbon footprint paves the way for con- |
tinual, sustainable benefits for the geospatial community. |
However, when we initially experiment with the standard |
continual pretraining formulation, we find it provides only |
marginal benefits (Section 3.2). Instead, we discover that |
utilizing ImageNet representations as an auxiliary distilla- |
tion objective during pretraining leads to a stronger geospa- |
tial foundation model. Building upon this principle, we pro-pose a multi-objective continual pretraining paradigm that |
significantly enhances performance while requiring mini- |
mal resources. Our approach leverages ImageNet’s power- |
ful representations to facilitate and expedite learning, while |
also enabling the acquisition of valuable in-domain features |
via self-supervised learning on geospatial data. Further- |
more, our proposed Geospatial Foundation Model (GFM) |
exhibits strong performance, surpassing previous state-of- |
the-art (SOTA) methods across a diverse range of down- |
stream tasks (Section 4). Our contributions are as follows: |
• We investigate a novel paradigm for creating highly ef- |
fective geospatial models with minimal resource costs. |
Our methodology begins with data selection and con- |
struction of a compact yet diverse dataset from multi- |
ple sources to promote feature diversity and enhance |
pretraining effectiveness, which we term GeoPile. We |
further explore the potential of continual pretraining |
from ImageNet models, but find it is not satisfactory in |
its standard formulation. |
• Therefore, to achieve better performance with minimal |
resource needs, we propose a multi-objective contin- |
ual pretraining paradigm. Our design is surprisingly |
simple yet effective, constructed as a teacher-student |
strategy with both a distillation objective and self- |
supervised masked image modeling. This approach |
allows GFM to leverage the strong representations of |
ImageNet to guide and quicken learning, while simul- |
taneously providing the freedom to learn valuable in- |
domain features. |
• We evaluate our GFM approach, as well as several |
baseline and SOTA methods, on 7 datasets covering |
important geospatial applications such as change de- |
tection, classification, multi-label classification, se- |
mantic segmentation, and super-resolution. Overall, |
our GFM performs favorably over previous methods |
(as shown in Figure 1). |
2. Related Work |
Geospatial Pretraining . Various works have experi- |
mented with employing supervised or self-supervised pre- |
training paradigms in the geospatial domain. The clas- |
sical work of [29], and more recent paper [38], investi- |
gate supervised pretraining on individual datasets of various |
sizes. Interestingly, these still often found the ImageNet |
pretrained models to perform very well, particularly with |
vision transformers [14, 25]. Other works have explored |
self-supervised learning paradigms for remote sensing, pri- |
marily focused on contrastive methods. [28] and [2] employ |
a MoCo [7] style objective using spatially aligned but tem- |
porally different images as the positive pairs. [23] and [20] |
also utilize a MoCo-inspired objective, but specify a crop- |
ping procedure to generate positives and negatives within |
and across images. [37] employs a colorization objective |
on Sentinel-2 imagery utilizing the various spectral bands. |
Most recently, SatMAE [9] explores the use of masked im- |
age modeling to train a large ViT model. This work is sim- |
ilar in some respect to ours, as we also train a transformer |
model with an MIM objective. However, we find that Sat- |
MAE often does not perform better than the off-the-shelf |
ImageNet-22k pretrained ViT (Section 4). This indicates |
both the difficulty of building strong geospatial pretrained |
models from scratch and highlights the potential usefulness |
of leveraging continual pretraining instead, as we investi- |
gate in this work. |
Masked Image Modeling . Masked image modeling |
(MIM) has been proposed in various forms in recent years, |
and has recently been found to be particularly effective |
in the natural image domain, surpassing many contrastive |
works and being shown to be friendlier to downstream op- |
timization [41, 18, 44, 3, 40] In general, the goal is to learn |
from data in a self-supervised manner by asking the model |
to generate pixel values for intentionally-withheld regions |
in an image. [32] is an early work with an aim of learning |
strong visual representations through inpainting masked re- |
gions. In [6], Chen et. al train a large transformer to pre- |
dict pixels autoregressively. After the introduction of vi- |
sion transformers (ViT) [14], many works continued to im- |
prove various MIM variants. [3] and [44] take inspiration |
from BERT [12] in natural language processing, and tok- |
enize the image patches with either a pretrained model or |
jointly trained online tokenizer, with the objective being to |
reconstruct at a token-level rather than raw pixels. Recently, |
[41] and [18] show that a masked image modeling task of |
simply regressing directly on the image pixels is sufficient |
and effective. In this work, we leverage the framework from |
[41], as it is compatible with hierarchical transformer archi- |
tectures [25]. |
In this work, we develop our pretraining objective based |
on a masked image modeling approach like [41, 18]. Explo- |
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