gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

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A.6 Presto’s failure modes
Presto processes pixel-timeseries independently, without spatial context from other pixels or locations.
This means that when we make image-based predictions (such as for scene classification), Presto’s
independent pixel representations must be aggregated into a single prediction. We opt for a simple
concatenation of the element-wise mean and standard deviation of the representations, from which
a classifier makes a prediction. Information gets lost in such a simple aggregation, which impacts
Presto’s performance on such tasks.
19
Table 13: Additional results for the EuroSat task - results for the ScaleMAE, SatMAE and ConvMAE
models are from (Reed et al., 2022). We report kNN classifier results for different values of k, and at
varying input resolutions.
Resolution 16 32 64
k 5 20 100 5 20 100 5 20 100
SatMAE 0.729 0.727 0.695 0.871 0.876 0.854 0.934 0.931 0.913
ScaleMAE 0.751 0.744 0.699 0.912 0.901 0.869 0.960 0.956 0.935
ConvMAE 0.835 0.826 0.788 0.909 0.898 0.863 0.947 0.940 0.914
Presto (RGB) 0.869 0.828 0.713 0.869 0.829 0.712 0.869 0.829 0.713
Presto (MS) 0.916 0.892 0.844 0.920 0.892 0.846 0.921 0.893 0.846
Table 14: Additional results for the EuroSat task for Presto when run with reduced resolutions
(compared to those used by (Reed et al., 2022) and reported in Table 13). We report kNN classifier
results for different values of k, and at varying input resolutions.
Resolution 2 4 8
k 5 20 100 5 20 100 5 20 100
Presto (RGB) 0.843 0.811 0.699 0.860 0.820 0.706 0.869 0.826 0.710
Presto (MS) 0.873 0.852 0.799 0.895 0.874 0.824 0.911 0.886 0.838
Table 15: Additional results for the TreeSatAI (as in (Ahlswede et al., 2023), we report precision
and recall in addition to F1score and mAP). In addition, we report the results of finetuning Presto
(Presto FT) from the pre-trained weights and from a random initialization.
Model Data Aggregation F1 mAP Precision Recall
MLP
S1Weighted10.09 29 .42 33 .29 7 .13
LightGBM 11.86 32 .79 37 .96 8 .06
Presto FT(random init.) 40.36±0.77 39 .77±0.79 30 .69±0.82 64 .69±1.09
Presto FT 38.69±0.78 37 .41±0.58 30 .09±0.74 61 .20±0.85
Presto RF 38.34±0.07 35 .45±0.03 29 .67±0.07 57 .23±0.06
MLP
Micro12.82 33 .09 63 .01 7 .13
LightGBM 14.07 35.11 55.49 8 .06
Presto FT(random init.) 42.04±0.73 43 .00±0.80 31 .20±1.00 64 .69±1.09
Presto FT 41.65±0.46 40 .75±0.69 31 .58±0.47 61 .20±0.85
Presto RF 40.79±0.04 38 .64±0.02 31 .69±0.03 57 .23±0.06
MLP
S2Weighted51.97 64 .19 74 .59 42 .23
LightGBM 48.17 61 .99 74 .27 40 .04
Presto FT(random init.) 52.74±0.50 57 .24±0.64 45 .87±1.17 64 .29±1.51
Presto FT 53.63±0.42 59 .16±1.24 47 .15±1.40 65 .11±3.21
Presto RF 55.29±0.08 61 .53±0.09 56 .93±0.07 58 .56±0.09
MLP
Micro54.49 65 .83 77 .18 42 .23
LightGBM 52.52 61 .66 76 .27 40 .04
Presto FT(random init.) 52.56±0.41 58 .08±0.66 44 .56±1.03 64 .29±1.51
Presto FT 53.31±0.18 59 .77±1.13 45 .51±1.46 65 .11±3.21
Presto RF 58.29±0.06 63 .31±0.06 58 .04±0.05 58 .56±0.09
20
Table 16: Full results on the S2-Agri 100dataset, including standard errors obtained from 3 runs.
To obtain standard errors for the SITS-Former, we run the official code ( https://github.com/
linlei1214/SITS-Former ) with 3 seeds. Best results are highlighted .
Params (M) Pre-trained? OA κ F1
SITS
Former2.565.13±3.01 0.55±0.03 42.12±0.52
✓ 67.03±2.24 0.56±0.02 42.83±0.30
Presto 0.445.98±2.74 0.35±0.02 27.45±0.64
✓ 68.89±1.05 0.58±0.01 40.41±0.25
Figure 7: Accuracy of kNN@5 classifier with Presto RGB representations on the EuroSat dataset vs.
the input resolution, for different categories. Some categories have been left out for clarity.
(a) Forest
(b) Annual Crop
(c) Highway
(d) River
Figure 8: the RGB bands of example images from EuroSat classes.
For example, Presto’s performance on the EuroSat dataset reaches a plateau when increasing the
input resolution. As Figure 7 shows, this is mainly caused by a failure to accurately predict specific
classes (for example, the Highway andRiver classes). Figure 8 shows example images for these
classes, as well as for the Forest andAnnualCrop classes, on which Presto achieves higher accuracies.
While in the Forest andAnnualCrop images, most pixels of the image actually represent the labelled
class, in the Highway andRiver images only a relatively small part of the image actually contains the
label (a highway or river). We hypothesize that since many pixels in the Highway andRiver images
do not actually represent that class, the crude token-aggregation method we use to represent images
is insufficiently discriminative to accurately classify these images.
Other pre-trained remote sensing models use much more powerful mechanisms for aggregating
spatial information. For example, ViT models convolve over patches and then apply an attention
mechanism between spatial patches. If image-based predictions are needed and these predictions are
highly dependent on the occurrence of objects in subregions of the image, models which natively
process this important spatial information may be better suited.
We plan on exploring techniques to mitigate this difficulty with Presto in future work.
21
Geography-Aware Self-Supervised Learning
Kumar Ayush*
Stanford UniversityBurak Uzkent*
Stanford UniversityChenlin Meng*
Stanford UniversityKumar Tanmay
IIT Kharagpur
Marshall Burke
Stanford UniversityDavid Lobell
Stanford UniversityStefano Ermon
Stanford University
Abstract
Contrastive learning methods have signiﬁcantly nar-
rowed the gap between supervised and unsupervised learn-
ing on computer vision tasks. In this paper, we explore their

A.6 Presto’s failure modes

Presto processes pixel-timeseries independently, without spatial context from other pixels or locations.

This means that when we make image-based predictions (such as for scene classification), Presto’s

independent pixel representations must be aggregated into a single prediction. We opt for a simple

concatenation of the element-wise mean and standard deviation of the representations, from which

a classifier makes a prediction. Information gets lost in such a simple aggregation, which impacts

Presto’s performance on such tasks.

19

Table 13: Additional results for the EuroSat task - results for the ScaleMAE, SatMAE and ConvMAE

models are from (Reed et al., 2022). We report kNN classifier results for different values of k, and at

varying input resolutions.

Resolution 16 32 64

k 5 20 100 5 20 100 5 20 100

SatMAE 0.729 0.727 0.695 0.871 0.876 0.854 0.934 0.931 0.913

ScaleMAE 0.751 0.744 0.699 0.912 0.901 0.869 0.960 0.956 0.935

ConvMAE 0.835 0.826 0.788 0.909 0.898 0.863 0.947 0.940 0.914

Presto (RGB) 0.869 0.828 0.713 0.869 0.829 0.712 0.869 0.829 0.713

Presto (MS) 0.916 0.892 0.844 0.920 0.892 0.846 0.921 0.893 0.846

Table 14: Additional results for the EuroSat task for Presto when run with reduced resolutions

(compared to those used by (Reed et al., 2022) and reported in Table 13). We report kNN classifier

results for different values of k, and at varying input resolutions.

Resolution 2 4 8

k 5 20 100 5 20 100 5 20 100

Presto (RGB) 0.843 0.811 0.699 0.860 0.820 0.706 0.869 0.826 0.710

Presto (MS) 0.873 0.852 0.799 0.895 0.874 0.824 0.911 0.886 0.838

Table 15: Additional results for the TreeSatAI (as in (Ahlswede et al., 2023), we report precision

and recall in addition to F1score and mAP). In addition, we report the results of finetuning Presto

(Presto FT) from the pre-trained weights and from a random initialization.

Model Data Aggregation F1 mAP Precision Recall

MLP

S1Weighted10.09 29 .42 33 .29 7 .13

LightGBM 11.86 32 .79 37 .96 8 .06

Presto FT(random init.) 40.36±0.77 39 .77±0.79 30 .69±0.82 64 .69±1.09

Presto FT 38.69±0.78 37 .41±0.58 30 .09±0.74 61 .20±0.85

Presto RF 38.34±0.07 35 .45±0.03 29 .67±0.07 57 .23±0.06

MLP

Micro12.82 33 .09 63 .01 7 .13

LightGBM 14.07 35.11 55.49 8 .06

Presto FT(random init.) 42.04±0.73 43 .00±0.80 31 .20±1.00 64 .69±1.09

Presto FT 41.65±0.46 40 .75±0.69 31 .58±0.47 61 .20±0.85

Presto RF 40.79±0.04 38 .64±0.02 31 .69±0.03 57 .23±0.06

MLP

S2Weighted51.97 64 .19 74 .59 42 .23

LightGBM 48.17 61 .99 74 .27 40 .04

Presto FT(random init.) 52.74±0.50 57 .24±0.64 45 .87±1.17 64 .29±1.51

Presto FT 53.63±0.42 59 .16±1.24 47 .15±1.40 65 .11±3.21

Presto RF 55.29±0.08 61 .53±0.09 56 .93±0.07 58 .56±0.09

MLP

Micro54.49 65 .83 77 .18 42 .23

LightGBM 52.52 61 .66 76 .27 40 .04

Presto FT(random init.) 52.56±0.41 58 .08±0.66 44 .56±1.03 64 .29±1.51

Presto FT 53.31±0.18 59 .77±1.13 45 .51±1.46 65 .11±3.21

Presto RF 58.29±0.06 63 .31±0.06 58 .04±0.05 58 .56±0.09

20

Table 16: Full results on the S2-Agri 100dataset, including standard errors obtained from 3 runs.

To obtain standard errors for the SITS-Former, we run the official code ( https://github.com/

linlei1214/SITS-Former ) with 3 seeds. Best results are highlighted .

Params (M) Pre-trained? OA κ F1

SITS

Former2.565.13±3.01 0.55±0.03 42.12±0.52

✓ 67.03±2.24 0.56±0.02 42.83±0.30

Presto 0.445.98±2.74 0.35±0.02 27.45±0.64

✓ 68.89±1.05 0.58±0.01 40.41±0.25

Figure 7: Accuracy of kNN@5 classifier with Presto RGB representations on the EuroSat dataset vs.

the input resolution, for different categories. Some categories have been left out for clarity.

(a) Forest

(b) Annual Crop

(c) Highway

(d) River

Figure 8: the RGB bands of example images from EuroSat classes.

For example, Presto’s performance on the EuroSat dataset reaches a plateau when increasing the

input resolution. As Figure 7 shows, this is mainly caused by a failure to accurately predict specific

classes (for example, the Highway andRiver classes). Figure 8 shows example images for these

classes, as well as for the Forest andAnnualCrop classes, on which Presto achieves higher accuracies.

While in the Forest andAnnualCrop images, most pixels of the image actually represent the labelled

class, in the Highway andRiver images only a relatively small part of the image actually contains the

label (a highway or river). We hypothesize that since many pixels in the Highway andRiver images

do not actually represent that class, the crude token-aggregation method we use to represent images

is insufficiently discriminative to accurately classify these images.

Other pre-trained remote sensing models use much more powerful mechanisms for aggregating

spatial information. For example, ViT models convolve over patches and then apply an attention

mechanism between spatial patches. If image-based predictions are needed and these predictions are

highly dependent on the occurrence of objects in subregions of the image, models which natively

process this important spatial information may be better suited.

We plan on exploring techniques to mitigate this difficulty with Presto in future work.

21

Geography-Aware Self-Supervised Learning

Kumar Ayush*

Stanford UniversityBurak Uzkent*

Stanford UniversityChenlin Meng*

Stanford UniversityKumar Tanmay

IIT Kharagpur

Marshall Burke

Stanford UniversityDavid Lobell

Stanford UniversityStefano Ermon

Stanford University

Abstract

Contrastive learning methods have signiﬁcantly nar-

rowed the gap between supervised and unsupervised learn-

ing on computer vision tasks. In this paper, we explore their