gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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Esther Rolf, Michael I Jordan, and Benjamin Recht. Post-estimation smoothing: A simple baseline
for learning with side information. In International Conference on Artiﬁcial Intelligence and
Statistics , pp. 1759–1769. PMLR, 2020.
Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara,
Benjamin Recht, and Solomon Hsiang. A generalizable and accessible approach to machine
learning with global satellite imagery. Nature communications , 12(1):1–11, 2021.
Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, Jakob Richter, and Alexander Brenning. Hy-
perparameter tuning and performance assessment of statistical and machine-learning algorithms
using spatial data. Ecological Modelling , 406:109–120, 2019.
Helen R Sofaer, Catherine S Jarnevich, Ian S Pearse, Regan L Smyth, Stephanie Auer, Gericke L
Cook, Thomas C Edwards Jr, Gerald F Guala, Timothy G Howard, Jeffrey T Morisette, et al.
Development and delivery of species distribution models to inform decision-making. BioScience ,
69(7):544–557, 2019.
Insang Song and Daehyun Kim. Three common machine learning algorithms neither enhance pre-
diction accuracy nor reduce spatial autocorrelation in residuals: An analysis of twenty-ﬁve so-
cioeconomic data sets. Geographical Analysis , 2022.
Stephen V Stehman, Bruce W Pengra, Josephine A Horton, and Danika F Wellington. Validation of
the us geological survey’s land change monitoring, assessment and projection (lcmap) collection
1.0 annual land cover products 1985–2017. Remote Sensing of Environment , 265:112646, 2021.
Devis Tuia, Claudio Persello, and Lorenzo Bruzzone. Domain adaptation for the classiﬁcation of
remote sensing data: An overview of recent advances. IEEE geoscience and remote sensing
magazine , 4(2):41–57, 2016.
Roozbeh Valavi, Jane Elith, Jos ´e J Lahoz-Monfort, and Gurutzeta Guillera-Arroita. blockcv: An r
package for generating spatially or environmentally separated folds for k-fold cross-validation of
species distribution models. bioRxiv , pp. 357798, 2018.
Alexandre MJ-C Wadoux, Gerard BM Heuvelink, Sytze De Bruin, and Dick J Brus. Spatial cross-
validation is not the right way to evaluate map accuracy. Ecological Modelling , 457:109692,
2021.
May Yuan and Arlo McKee. Embedding scale: New thinking of scale in machine learning and
geographic representation. Journal of Geographical Systems , 24(3):501–524, 2022.
7
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial
Representation Learning
Colorado J Reed1,2, Ritwik Gupta1, Shufan Li1*,
Sarah Brockman3, Christopher Funk3, Brian Clipp3,
Kurt Keutzer1, Salvatore Candido2, Matt Uyttendaele2, Trevor Darrell1
1Berkeley AI Research;2Meta AI, FAIR;3Kitware Inc.
correspondence to ritwikgupta@berkeley.edu
Abstract
Large, pretrained models are commonly finetuned with
imagery that is heavily augmented to mimic different condi-
tions and scales, with the resulting models used for various
tasks with imagery from a range of spatial scales. Such
models overlook scale-specific information in the data for
scale-dependent domains, such as remote sensing. In this
paper, we present Scale-MAE , a pretraining method that ex-
plicitly learns relationships between data at different, known
scales throughout the pretraining process. Scale-MAE pre-
trains a network by masking an input image at a known input
scale, where the area of the Earth covered by the image deter-
mines the scale of the ViT positional encoding, not the image
resolution. Scale-MAE encodes the masked image with a
standard ViT backbone, and then decodes the masked image
through a bandpass filter to reconstruct low/high frequency
images at lower/higher scales. We find that tasking the net-
work with reconstructing both low/high frequency images
leads to robust multiscale representations for remote sensing
imagery. Scale-MAE achieves an average of a 2.4−5.6%
non-parametric kNN classification improvement across eight
remote sensing datasets compared to current state-of-the-art
and obtains a 0.9mIoU to 1.7mIoU improvement on the
SpaceNet building segmentation transfer task for a range of
evaluation scales.
1. Introduction
Remote sensing data is captured from satellites and planes
through a mixture of sensors, processing pipelines, and view-
ing geometries. Depending on the composition and relative
geometry of the sensor to the Earth, each image’s Ground
Sample Distance (GSD - the physical distance between two
*Denotes co-first authorship. Co-first authors will prioritize their names
on their resumes/websites.
Ground Truth Input Image Scale-MAE Vanilla MAE
Correct Incorrect0.3m GSD0.3m GSD
3.0m GSD3.0m GSDFigure 1. Scale-MAE learns better representations for multiscale
tasks compared to vanilla MAE. (Column 1) The top image spans
an area at 0.3m GSD and the bottom image shows the same region
at a coarser GSD. (Columns 2-4) The following columns show
a ground truth building segmentation, Scale-MAE segmentation
from a finetuned UperNet, and segmentation from an analogously
finetuned UperNet from a vanilla MAE, respectively. Scale-MAE
demonstrates better performance across images at both scales. See
the supplementary material for more examples.
adjacent pixels in an image) can vary from 0.3m to 1km, so a
100x100 pixel image could span anywhere from an Olympic-
size swimming pool (900 m2) to almost the entire country of
Jamaica (10,000 km2). The data within each image, and the
corresponding objects and points of interest, can therefore
vary across wide spatial ranges. Data from these multiscale
sensors provide critical and complementary information for
various operational and research applications in areas such
as atmospheric, hydrologic, agricultural, and environmental
monitoring [45, 52].
Few modern computer vision methods have explicitly ad-
dressed multiscale remote sensing imagery [35]. Neverthe-
less, the remote sensing vision community has increasingly
used large, pretrained models [13, 20], where such appli-
cations finetune a pretrained model for a single source ofarXiv:2212.14532v4 [cs.CV] 22 Sep 2023
Patchify + Mask
Resampled I
224px, .7m GSDResampled I

Esther Rolf, Michael I Jordan, and Benjamin Recht. Post-estimation smoothing: A simple baseline

for learning with side information. In International Conference on Artiﬁcial Intelligence and

Statistics , pp. 1759–1769. PMLR, 2020.

Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara,

Benjamin Recht, and Solomon Hsiang. A generalizable and accessible approach to machine

learning with global satellite imagery. Nature communications , 12(1):1–11, 2021.

Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, Jakob Richter, and Alexander Brenning. Hy-

perparameter tuning and performance assessment of statistical and machine-learning algorithms

using spatial data. Ecological Modelling , 406:109–120, 2019.

Helen R Sofaer, Catherine S Jarnevich, Ian S Pearse, Regan L Smyth, Stephanie Auer, Gericke L

Cook, Thomas C Edwards Jr, Gerald F Guala, Timothy G Howard, Jeffrey T Morisette, et al.

Development and delivery of species distribution models to inform decision-making. BioScience ,

69(7):544–557, 2019.

Insang Song and Daehyun Kim. Three common machine learning algorithms neither enhance pre-

diction accuracy nor reduce spatial autocorrelation in residuals: An analysis of twenty-ﬁve so-

cioeconomic data sets. Geographical Analysis , 2022.

Stephen V Stehman, Bruce W Pengra, Josephine A Horton, and Danika F Wellington. Validation of

the us geological survey’s land change monitoring, assessment and projection (lcmap) collection

1.0 annual land cover products 1985–2017. Remote Sensing of Environment , 265:112646, 2021.

Devis Tuia, Claudio Persello, and Lorenzo Bruzzone. Domain adaptation for the classiﬁcation of

remote sensing data: An overview of recent advances. IEEE geoscience and remote sensing

magazine , 4(2):41–57, 2016.

Roozbeh Valavi, Jane Elith, Jos ´e J Lahoz-Monfort, and Gurutzeta Guillera-Arroita. blockcv: An r

package for generating spatially or environmentally separated folds for k-fold cross-validation of

species distribution models. bioRxiv , pp. 357798, 2018.

Alexandre MJ-C Wadoux, Gerard BM Heuvelink, Sytze De Bruin, and Dick J Brus. Spatial cross-

validation is not the right way to evaluate map accuracy. Ecological Modelling , 457:109692,

2021.

May Yuan and Arlo McKee. Embedding scale: New thinking of scale in machine learning and

geographic representation. Journal of Geographical Systems , 24(3):501–524, 2022.

7

Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial

Representation Learning

Colorado J Reed1,2*, Ritwik Gupta1*, Shufan Li1*,

Sarah Brockman3, Christopher Funk3, Brian Clipp3,

Kurt Keutzer1, Salvatore Candido2, Matt Uyttendaele2, Trevor Darrell1

1Berkeley AI Research;2Meta AI, FAIR;3Kitware Inc.

correspondence to ritwikgupta@berkeley.edu

Abstract

Large, pretrained models are commonly finetuned with

imagery that is heavily augmented to mimic different condi-

tions and scales, with the resulting models used for various

tasks with imagery from a range of spatial scales. Such

models overlook scale-specific information in the data for

scale-dependent domains, such as remote sensing. In this

paper, we present Scale-MAE , a pretraining method that ex-

plicitly learns relationships between data at different, known

scales throughout the pretraining process. Scale-MAE pre-

trains a network by masking an input image at a known input

scale, where the area of the Earth covered by the image deter-

mines the scale of the ViT positional encoding, not the image

resolution. Scale-MAE encodes the masked image with a

standard ViT backbone, and then decodes the masked image

through a bandpass filter to reconstruct low/high frequency

images at lower/higher scales. We find that tasking the net-

work with reconstructing both low/high frequency images

leads to robust multiscale representations for remote sensing

imagery. Scale-MAE achieves an average of a 2.4−5.6%

non-parametric kNN classification improvement across eight

remote sensing datasets compared to current state-of-the-art

and obtains a 0.9mIoU to 1.7mIoU improvement on the

SpaceNet building segmentation transfer task for a range of

evaluation scales.

1. Introduction

Remote sensing data is captured from satellites and planes

through a mixture of sensors, processing pipelines, and view-

ing geometries. Depending on the composition and relative

geometry of the sensor to the Earth, each image’s Ground

Sample Distance (GSD - the physical distance between two

*Denotes co-first authorship. Co-first authors will prioritize their names

on their resumes/websites.

Ground Truth Input Image Scale-MAE Vanilla MAE

Correct Incorrect0.3m GSD0.3m GSD

3.0m GSD3.0m GSDFigure 1. Scale-MAE learns better representations for multiscale

tasks compared to vanilla MAE. (Column 1) The top image spans

an area at 0.3m GSD and the bottom image shows the same region

at a coarser GSD. (Columns 2-4) The following columns show

a ground truth building segmentation, Scale-MAE segmentation

from a finetuned UperNet, and segmentation from an analogously

finetuned UperNet from a vanilla MAE, respectively. Scale-MAE

demonstrates better performance across images at both scales. See

the supplementary material for more examples.

adjacent pixels in an image) can vary from 0.3m to 1km, so a

100x100 pixel image could span anywhere from an Olympic-

size swimming pool (900 m2) to almost the entire country of

Jamaica (10,000 km2). The data within each image, and the

corresponding objects and points of interest, can therefore

vary across wide spatial ranges. Data from these multiscale

sensors provide critical and complementary information for

various operational and research applications in areas such

as atmospheric, hydrologic, agricultural, and environmental

monitoring [45, 52].

Few modern computer vision methods have explicitly ad-

dressed multiscale remote sensing imagery [35]. Neverthe-

less, the remote sensing vision community has increasingly

used large, pretrained models [13, 20], where such appli-

cations finetune a pretrained model for a single source ofarXiv:2212.14532v4 [cs.CV] 22 Sep 2023

Patchify + Mask

Resampled I

224px, .7m GSDResampled I