Impact of Satellites Streaks for Observational Astronomy: A Study on

Data Captured During One Year from Luxembourg Greater Region

Olivier Parisot and Mahmoud Jaziri

Luxembourg Institute of Science and Technology (LIST), 5 Avenue des Hauts-Fourneaux,

4362 Esch-sur-Alzette, Luxembourg

Keywords:

Astronomy, Satellites Streaks, Computer Vision.

Abstract:

The visible and signiﬁcant presence of satellites in the night sky has an impact on astronomy and astropho-

tography activities for both amateurs and professionals, by perturbing observations sessions with undesired

streaks in captured images, and the number of spacecrafts orbiting the Earth is expected to increase steadily

in the coming years. In this article, we test an existing method and we propose a dedicated approach based

on eXplainable Artiﬁcial Intelligence to detect streaks in astronomical data captured between March 2022 and

February 2023 with a smart telescope in the Greater Luxembourg Region. To speed up the calculation, we

also propose a detection approach based on Generative Adversarial Networks.

1 INTRODUCTION

We live in a time when global connectivity is becom-

ing an unstoppable trend, with mega satellite con-

stellations such as SpaceX’s Starlink, OneWeb and

Amazon’s Project Kuiper proliferating in low-Earth

orbit (Langston and Taylor, 2024). While these satel-

lite networks have started to revolutionize the indus-

try, they also raise growing concerns, for multiple as-

pects (environment, defence, culture, etc.) (Venkate-

san et al., 2020). In particular, the impact of these

mega-constellations on astronomy and astrophotog-

raphy has become a hot topic (Walker et al., 2020),

calling into question the possibility of observing the

night sky without disturbance.

A modern obstacle is satellite light pollution,

which occurs when orbiting satellites reﬂect the sun’s

light unto the Earth. This light disturbance can make

astronomical observations considerably more difﬁcult

(Lawler, 2023), and affect the quality of images cap-

tured by amateur and professional astronomers alike:

• Light trails: mega-constellation satellites can cre-

ate light trails as they pass in front of a telescope

or camera lens during long exposure photography.

These streaks can compromise image quality by

leaving unwanted lines of light across astronomi-

cal images.

• Increased sky brightness: the sun’s reﬂection off

satellite surfaces can contribute to a general in-

crease in the brightness of the night sky. This

makes it more difﬁcult to observe and capture

faint, distant celestial objects, such as galaxies,

nebulae and faint stars.

• Reduced contrast: the presence of moving satel-

lites can reduce the contrast between celestial ob-

jects and the sky background. Subtle details in

astronomical pictures, which depend on a dark,

uniform night sky, can be compromised by bright

streaks and scattered spots.

• Complications for image calibration: satellite

light trails can disrupt the process by introduc-

ing non-stellar elements into the images, making

treatment difﬁcult or even impossible.

• Need for advanced post-processing: this may re-

quire technical adjustments and specialized soft-

ware to mitigate undesirable effects caused by

satellites, such as inpainting.

It’s also a problem for professional ground-based

observatories (Hainaut and Williams, 2020), making

it imperative to set up a process to estimate concrete

impact on the quality of large digital sky surveys (Lu,

2024), avoid disturbances and then correct data if pos-

sible (Tyson et al., 2020). The effect is signiﬁcant: as

recent studies have shown (Lawler, 2023; Barentine

et al., 2023), the increase in trafﬁc in low-Earth or-

bit will inevitably lead to a loss of astronomical data

and therefore reduce the possibilities of discoveries

on the ground, as weak astrophysical signals are in-

creasingly lost in the noise. The International As-

Parisot, O. and Jaziri, M.

Impact of Satellites Streaks for Observational Astronomy: A Study on Data Captured During One Year from Luxembourg Greater Region.

DOI: 10.5220/0012787800003756

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024), pages 417-424

ISBN: 978-989-758-707-8; ISSN: 2184-285X

417

tronomical Union (IAU) recently published a ’Call to

Protect the Dark and Quiet Sky from Harmful Inter-

ference by Satellite Constellations’

. Furthermore,

it’s a hot topic for space-based observatories like Hub-

ble (Kruk et al., 2023), which adds a number of con-

straints that are difﬁcult to resolve, especially given

the cost of operating such facilities in space.

In this article, we propose the study of a dataset

made up of astronomical images captured over a year

with a smart telescope, in conditions accessible to am-

ateurs, and we evaluate the quantity of images effec-

tively impacted by satellite trails. The rest of the pa-

per is structured as follows. In Section 2, we present

a brief review of the state of the art concerning the

detection of satellite streaks in astronomical images.

Section 3 describes the dataset of images captured

by the author, while Section 4 proposes a study of

this dataset using different methods. In Section 5, we

discuss the results, before concluding and proposing

some perspectives in Section 6.

2 RELATED WORKS

For many years now, the scientiﬁc community has

proposed many techniques to detect and track satel-

lites, and to deal with the trails they cause in astro-

nomical images (Nir et al., 2018; Calvi et al., 2021;

Jiang et al., 2023). Speciﬁc software for astronom-

ical images processing propose features to manage

this problem, like SharpCap

. It is important to note

that all types of fast-moving Near-Earth Objects, such

as meteors, satellites or even cosmic rays, can leave

streaks, trails and linear features on astronomical im-

ages (Nir et al., 2018).

In the Python software ecosystem, we can mention

these tools:

• ASTRiDE aims to detect streaks in astronomical

images (Kim, 2016) with boundary-tracing and

morphological parameters. ASTRiDE can detect

not only long streaks, but also relatively faint,

short or curved ones. As we will see later in this

article, this is also a problem because it tends to

confuse real streaks with tracking problems – so it

requires a ﬁne conﬁguration like in (Duarte et al.,

2023).

• Authors of (Danarianto et al., 2022) proposed a

Python pipeline for lightweight streak detection,

identiﬁcation and initial orbit determination from

FITS raw ﬁles captured by amateur-grade tele-

scopes – but it was tested on only a few images

https://cps.iau.org/documents/49/techdoc102.pdf

https://www.sharpcap.co.uk

captured around the celestial equator (85). FITS

(Flexible Image Transport System) is a ﬁle format

most commonly used into store astronomical data.

• A research team applied the probabilistic Hough

transform through a Python scripts using well-

known open source libraries like openCV and

scikit-images, and by using GPU-speciﬁc compu-

tation to detect streaks in FITS ﬁles captured by

the Tomo-e Gozen camera at Kiso Observatory in

Japan (Cegarra Polo et al., 2021). Unfortunately,

the source code is not available.

• pyradon is a Python package based on Fast Radon

Transform (FRT) to ﬁnd streaks in 2D astronomi-

cal images (Nir et al., 2018).

Some existing approaches are based on Deep

Learning. For instance, an approach based on YOLO

(You Only Look Once) was proposed by (Varela et al.,

2019) to detect streaks in images captured by a multi-

camera wide ﬁeld of view system. The authors note

that the labelling of training dataset is an issue. Fur-

thermore, a recent work compared two techniques

based on Deep Convolutional Neural Networks: an

extended feature pyramid network (EFPN) with faster

region-based CNNs (Faster R-CNN) and a feature

pyramid network (FPN) with Faster R-CNN (El-

hakiem et al., 2023). This approach is elaborated but

it was only tested on synthetic data.

In this paper, we compare an existing approach

with a dedicated technique combining Deep Learn-

ing and eXplainable Artiﬁcial Intelligence to search

for streaks in astronomical data that we have captured

ourselves using smart telescopes.

3 DATA ACQUISITION

Nowadays, Electronically Assisted Astronomy

(EAA) is increasingly applied by astronomers to

observe Deep Sky Objects (DSO), i.e. astronomical

objects that are not individual stars or Solar System

objects, like nebulae, galaxies or clusters. By cap-

turing images directly from a camera coupled to a

telescope and applying lightweight image process-

ing, EAA allows to generate and display enhanced

images on screens (laptop, tablet, smartphone), even

in places heavily impacted by light pollution and

poor weather conditions. The recent years brought

the emergence of smart telescopes, making sky

observation more accessible (Parisot et al., 2022).

Even the scientiﬁc community is taking advantage of

these instruments to study astronomical events (i.e.

asteroids occultations, exoplanets transits, eclipses) .

In this context, MILAN Sky Survey is a set of raw

images with DSO visible from the Northern Hemi-

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

418

sphere (galaxies, stars clusters, nebulae, etc.), col-

lected during 205 observation sessions (Parisot et al.,

2023). These images were captured between March

2022 and February 2023 from Luxembourg Greater

Region by using the built-in alignment and stacking

features of a Stellina smart telescope, based on an Ex-

tra Low Dispersion doublet with an aperture of 80 mm

and a focal length of 400 mm (focal ratio of f/5), and

equipped with a Sony IMX178 CMOS sensor with a

resolution of 6.4 million pixels. A CLS ﬁlter (City

Light Suppression) is placed in front of the camera

sensor. The Dawes Limit of the instrument is 1.45

arc-seconds.

The dataset and the data acquisition process is

deeply is described in (Parisot et al., 2023), here is

a short summary:

• The default settings of Stellina were applied, i.e.

10 seconds of exposure time and 20 dB of gain

for each single image. These values are a sat-

isfying trade-off to obtain good images with the

alt-azimuth motorized mounts of the instruments

(higher value of exposure time may cause a reduc-

tion in captured image quality, particularly with

moving blur (Loke, 2017), higher gain may in-

crease the noise level).

• For each observation session, the instrument was

installed in a dark environment (no direct light)

and properly balanced using a bubble level on a

stable ﬂoor (it’s mandatory to ensure a good track-

ing).

• Observation sessions were conducted only when

the sky was clear and of reasonable quality. The

authors were always present during observations

to deal with any weather-related issues such as

wind, cloud, fog, rain, or disturbance from ani-

mals.

In total, 205 observation sessions, leading to

50068 FITS images of resolutions 3096 × 2080 were

obtained (corresponding to a ﬁeld of view of approx-

imately 1° × 0.7°). As each image was obtained with

an exposure time of 10 seconds, it represents a total

cumulative time of 139 hours, 4 minutes and 40 sec-

onds.

4 METHOD

We have analyzed the MILAN Sky Survey dataset

with different methods, to count FITS ﬁles contain-

ing streaks, and so the maximum of images impacted

by satellites. The computations were realized with

the following hardware: 40 cores and 128 GB RAM

Figure 1: False positive example of streak, due to tracking

error. The FITS ﬁle is stored in NGC457-20220807.zip.

(Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz) and

NVIDIA Tesla V100-PCIE-32GB.

4.1 Detection with ASTRiDE

First, we have tested ASTRiDE, by using differ-

ent settings and by ﬁltering the results (Table 1).

ASTRiDE ﬁrst pre-processes the image by remov-

ing the background using its level and standard de-

viation before searching for streaks (Kim, 2016). It

then computes the contour map to identify all ob-

ject borders within the image. ASTRiDE then ana-

lyzes the morphologies of each object, as determined

by the morphological parameters, to differentiate be-

tween streaks and stars.

By using the default parameters (contour thresh-

old=3, shape cut=0.2), and by considering FITS im-

ages with at least one detected streak, we observed

this selection is too large, retaining images with just

blurred stars due to tracking errors (example: Figure

1).

To make a more restrictive selection, we have ﬁl-

tered FITS images with at least one streak with a min-

imal perimeter (128 pixels), and we have found that

streaks are detected in 1316 FITS ﬁles for a total of

50068 ﬁles, i.e. 2.6 %. In other words, it detected

than 137 observation sessions are impacted for a total

of 205, i.e. 66 % (Figure 2.

With the standard settings of ASTRiDE, the se-

lection is too large. Moreover, and given the fact that

FITS ﬁles have an high resolution, the computation

may be slow (the tools does not use GPU to speed-

up the analysis – the ASTRiDE’ authors advice is to

deal with parameters to ﬁnd a good trade-off between

accuracy and speed).

So we have tried with optimized settings which

Impact of Satellites Streaks for Observational Astronomy: A Study on Data Captured During One Year from Luxembourg Greater Region

419

Figure 2: Graphical output of ASTRiDE, after the detec-

tion of a large streak in a FITS ﬁle captured during an ob-

servation session of the Messier 81 galaxy (stored in M81-

20220308.zip).

are proposed by ASTRiDE authors on the ofﬁcial

source code repository (Kim, 2016): by increasing

contour threshold and by reducing shape cut, we

avoided selecting FITS ﬁles that are impacted by mi-

nor tracking errors. Nevertheless, the selection was

still too large.

One of the advantage of ASTRiDE is its ability

of detecting faint streaks, allowing to detect images

damaged by bad tracking (wind, unexpected move-

ment of telescopes, etc.). For this use-case, and keep-

ing into account that our FITS raw images are noisy

and far from perfect (especially due to tracking prob-

lems), this tool is too sensitive and it is difﬁcult to ﬁnd

the conﬁguration that leads to the detection of streaks

produced by satellites by ignoring other issues (Fig-

ure 1).

4.2 Detection with a Dedicated

ResNet50 Classiﬁer and XRAI

We trained a dedicated classiﬁer to detect images with

real streaks – and ignoring defects due to tracking.

As we have seen in the previous sections, there are

many tools available for this task, the aim is not to

re-invent the wheel. Our aim here is rather to have a

model that is fully compatible with our input data and

its speciﬁc characteristics (in particular the fact that it

is raw, unﬁltered and not debayered).

To this end, we used ASTRiDE to ﬁlter FITS im-

ages without any streak and/or defect, as ASTRiDE

is a sensible and efﬁcient tool for this task. Starting

from these images, we generated a dataset with syn-

thetic streaks to train and evaluate a binary classiﬁer.

In practice, here are the steps followed:

• From the raw data described in Section 3, we built

a set of 25070 RGB images with 224x224 pixels

– cutting FITS images into patches to get a reso-

lution that ﬁts to ResNet50 models.

• For each image, we applied a basic stretch to ad-

just the brightness and contrast to bring out details

and make faint structures more visible.

• Random synthetic streaks have been added on

half of the images, then we formed two distinct

groups, so as to associate a class with each im-

age: images with and images without streaks (we

made sure that each group was balanced – to have

a classiﬁer with good recall). These streaks were

generated by drawing random lines, with different

thickness, sizes and color intensity.

• We made 3 sets: training, validation and test

(80%, 10%, 10%).

• A dedicated Python prototype was developed to

train a ResNet50 model to learn this binary clas-

siﬁcation. The basic image processing tasks were

Table 1: Experiments with ASTRiDE to detect streaks in FITS images of MILAN Sky Survey. Different settings for ASTRiDE

have been tested and compared.

Settings Filter FIST Files with

detected streaks

Observation ses-

sions impacted

Default (contour threshold=3, shape

cut=0.2)

At least one streak 8704/50068 198/205

Default (contour threshold=3, shape

cut=0.2)

At least one streak with

perimeter higher than 128

pixels

1316/50068 137/205

Optimized (contour threshold=5,

shape cut=0.1)

At least one streak 903/50068 101/205

Optimized (contour threshold=5,

shape cut=0.1)

At least one streak with

perimeter higher than 128

pixels

404/50068 82/205

DATA 2024 - 13th International Conference on Data Science, Technology and Applications

420

performed following best practices for optimizing

CPU/GPU usage (Castro et al., 2023).

• Empirically, the following hyper-parameters were

used for training: ADAM optimizer, learning rate

of 0.001, 50 epochs, 32 images per batch. We thus

obtained a ResNet50 model with an accuracy of

97% on the validation dataset. Note that other

architectures were also tested (such as VGG16

and MobileNetV2), but the results here are largely

similar.

• At the end, we obtained a model with a precision

of 0.940, a recall of 0.805 and then a F1-score of

0.867 (Table 2).

Inspired by recent works in the industrial (Roth

et al., 2022) and health domains (Chaddad et al.,

2023), and to check the robustness of the trained

Resnet50 model, we analysed the output with XRAI

(Region-based Image Attribution) (Kapishnikov et al.,

2019). Frequently used in eXplainable Artiﬁcial In-

telligence for Computer Vision tasks, XRAI is an in-

cremental method that progressively builds the attri-

bution scores of regions (i.e. the regions of the im-

age that are most important for classiﬁcation). XRAI

is built upon Integrated Gradients (IG) (Sundararajan

et al., 2017) which uses a baseline (i.e. an image)

to create the attribution map. The baseline choice

is application-dependent, and in our case we oper-

ate under the assumption that a black one is appro-

priated because it corresponds to the sky background,

and the attribution maps is calculated according to

the XRAI integration path and reduces the attribution

scores given to black pixels. In practice, we used the

Python package saliency

and analysed the output of

the last convolution layer. To generate a heatmap indi-

cating the attribution regions with the greatest predic-

tive power, we keep only a percentage of the highest

XRAI attribution scores here (for instance, 10 %).

With this pipeline, we have found that streaks are

detected in 25 FITS ﬁles for a total of 50068 ﬁles,

i.e. less than 0.05 %; it detected than 18 observa-

tion sessions are impacted for a total of 205, i.e. 0.1

%. In this case, we visually noted with the heatmap

that the streaks are not caused by tracking problems,

but by objects passing through the instrument’s ﬁeld

https://pypi.org/project/saliency/

Figure 3: At the top, a stretched 10-second frame of Messier

57. At the bottom, the XRAI heatmap highlighting the pix-

els that are considered by the Resnet50 classiﬁer for detect-

ing the presence of streaks, by keeping 10% of the highest

XRAI attribution scores.

of view during observation, and very probably by

satellites. For example, we can mention the follow-

ing ﬁles present in (Parisot et al., 2023): image 44

in Barnard142-143-20220922.zip, image 22 in M17-

20220723.zip, image 40 in M57-20220602.zip, im-

age 87 in M65-20220426.zip, image 40 in M103-

20220808.zip, image 167 in M10-20220615.zip.

4.3 Fast Approximation with a Pix2Pix

Model

Computing a heatmap with XRAI comes at a cost: it

requires more computational time and resources than

a simple inference of the ResNet50 model. If we con-

sider the analysis of a 3584 × 3584 astronomical im-

age: with no overlap, it may be necessary to evalu-

ate the ResNet50 prediction and the XRAI heatmap

Table 2: Confusion matrix with results of the Resnet50 model on test set (i.e. set of images with synthetic streaks that were

randomly added).

Synthetic streak(s) de-

tected: NO

Synthetic streak(s) de-

tected: YES

FITS images without synthetic streak(s) 1886 103

FITS images with synthetic streak(s) 393 1619

Impact of Satellites Streaks for Observational Astronomy: A Study on Data Captured During One Year from Luxembourg Greater Region

421

for 256 patches of 224 × 224 pixels – this may take

some time depending on the hardware. To minimise

the number of calculations required, we can apply two

simple strategies:

• Reduce the size of the image to decrease the num-

ber of patches to be evaluated.

• Process only a relevant subset of patches – for

example, ignoring those for which the ResNet50

classiﬁer detects nothing.

An other solution consists in estimating the

XRAI heatmap with Generative Adversarial Net-

works (GAN), a class of Deep Learning frameworks

that are frequently applied for Computer Vision tasks.

A GAN is composed of two Deep Learning mod-

els: a generator that ingests an image as input and

provides another image as output, and a discrimina-

tor which guides the generator during the training by

distinguishing real and generated images. Both are

trained together through a supervised process – with

the goal to obtain a generator that produces realistic

images. Among the numerous existing GAN archi-

tectures, we selected Pix2Pix – a conditional adver-

sarial approach that was designed for image to image

translation (Isola et al., 2017), and applied in many

use-cases such as image colouration and enhancement

(KumarSingh et al., 2023).

Thus, a Pix2Pix model has been designed to learn

the transformation from images with synthetic streaks

(like in Section 4.2) and images with the same syn-

thetic streaks but with an other color. We applied the

standard Pix2Pix architecture as described and imple-

mented with Tensorﬂow

, taking input images of 256

× 256 pixels, with the same resolution for outputs.

The loss function was based on the Peak Signal-to-

Noise Ratio (PSNR), and we trained the model dur-

ing 100 epochs, the batch size was set to 1, and the

process was realized with a learning rate of 0.0001.

To improve the training phase, as described in (Tran

et al., 2021), we applied random data augmentation

during each epoch with the imgaug Python package

(Jung, 2019).

It led to a Pix2Pix model with a good PSNR

(higher that 38.5) – able to reproduce an annotated

image (Figure 4), similar to what can be obtained with

ResNet50 and the XRAI heatmap. We simply note

that this model is slightly more sensitive to noise, es-

pecially if it is grouped in zones (and this can some-

times happen with hot pixels (O’Brien, 2023)).

In terms of performances, running an inference

with the Pix2Pix model on a patch of 256 × 256 pix-

els is a better alternative to calculating a heatmap with

https://github.com/afﬁnelayer/pix2pix-tensorflow

Figure 4: Example of 256x256 patch generated with a

Pix2Pix model – with highlighted streaks.

XRAI on a patch of 224 × 224 pixels: for example,

execution time is halved on a laptop without a GPU.

In practice, we used this Pix2Px model to visually

check the results obtained in the previous section, by

generating and then viewing the output of each image

in which a streak was detected.

5 DISCUSSIONS

As it is infeasible to check several tens of thousands

of raw images manually, we used different automated

methods to ﬁlter potentially affected images. It is pos-

sible that certain cases have not been identiﬁed, in

particular when obstacles in the image, tracking prob-

lems and streaks can be found in the same images.

Furthermore, the different approaches were tested

on images obtained with speciﬁc equipment (aper-

ture of 80mm, focal length of 400mm, recent CMOS

sensors, alt-azimuth mount) and imperfect conditions.

They can therefore be applied to images obtained

with identical equipment or with similar characteris-

tics (i.e. other models of smart telescopes with similar

technical characteristics). Conversely, applying these

techniques on images obtained with smaller or larger

focal length instruments would require constituting a

dataset that would contain this type of data, to then

re-train models.

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422

6 CONCLUSION AND

PERSPECTIVES

This paper presents various approaches based on

Deep Learning to detect streaks from astronomical

images captured with smart telescopes from Luxem-

bourg Greater Region, which required collecting data

for over 188 different targets visible from the North-

ern Hemisphere, with equipment accessible to ama-

teurs.

One approach consists in using ASTRiDE, and

this tool is efﬁcient to detect images without streak.

The second one is a pipeline combining a ResNet50

binary classiﬁer and the XRAI method, allowing the

detection of real streaks with a good accuracy. The

last one is an experimental model based on Genera-

tive AI in order to highlight the pixels corresponding

to the detected streaks.

As a result, we observed that less of 0.05 percent

of the captured raw images are damaged by streaks,

potentially caused by satellites. In this case it’s not

much, not enough to require special treatment to ﬁx

the affected raw ﬁles, a simple ﬁlter here may be

enough to ignore them after detection.

In future work, we plan to reproduce and improve

the current tests on recent and future observations,

we plan to gather additional astronomical data

(especially from the South Hemisphere), and we will

work on optimizations to embed the presented Deep

Learning approaches into low resource devices.

Data Availability: The MILAN Sky Survey can

be accessed by following the links listed in (Parisot

et al., 2023). Additional materials used to support

the ﬁndings of this study may be available from the

corresponding author upon request.

ACKNOWLEDGEMENTS

This research was funded by the Luxembourg

National Research Fund (FNR), grant reference

15872557. Tests were realized on the LIST AIDA

platform, thanks to Raynald Jadoul and Jean-Franc¸ois

Merche.

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