# A global survey for dust devil vortices on mars using MRO context camera images enabled by neural networks

Susan J Conway, Valentin T Bickel, Lori K Fenton, Manish R Patel, Helen C Carson, Antoine Blouin, Justin Crevier, Evan Blanc, Bao Nhi Nguyen, James A Holmes, et al.

► To cite this version:

Susan J Conway, Valentin T Bickel, Lori K Fenton, Manish R Patel, Helen C Carson, et al. A global survey for dust devil vortices on mars using MRO context camera images enabled by neural networks. Planetary and Space Science, 2025, 259, pp.106072. 10.1016/j.pss.2025.106072. hal-05076462

HAL Id: hal-05076462

https://nantes-universite.hal.science/hal-05076462v1

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# A global survey for dust devil vortices on mars using MRO context camera images enabled by neural networks

Susan J. Conway<sup>a, *, 1</sup> <sup>1</sup>**, Valentin T. Bickel<sup>b, c, 1</sup>, Lori K. Fenton<sup>d</sup>, Manish R. Patel<sup>e</sup>, Helen C. Carson<sup>f</sup>, Antoine Blouin<sup>a</sup>, Justin Crevier<sup>g</sup>, Evan Blanc<sup>a</sup>, Bao Nhi Nguyen<sup>a</sup>, James A. Holmes<sup>e</sup>, Brian Jackson<sup>g</sup>, Lonneke Roelofs<sup>h</sup>

## ARTICLE INFO

**Keywords:**

Mars

Dust devils

MRO CTX

Weather

Climate

Machine learning

## ABSTRACT

Dust devils are atmospheric vortices that loft dust from the ground, typically at the hottest times of the day and year on Mars. They contribute dust to the atmosphere and so indirectly affect the global atmospheric circulation. Their size, shape and velocity can provide indications of the weather on Mars. Hence, tracking their occurrence in time and space provides useful data for understanding Mars' current climate. They are also of relevance to landed missions as they can clean solar panel surfaces, extending mission lifetimes. Despite the numerous observations and surveys of dust devils to date, the global extent, distribution and occurrence of dust devils is not yet consistently constrained, mainly due to the relatively limited spatial and temporal scope of manual analyses. To provide the most comprehensive global catalogue of active martian dust devils to date, we applied a RetinaNet convolutional neural network to existing remote sensing images of Mars to identify the distinctive signature of the light-toned lofted dust cloud and dark shadow formed by active dust devils on Mars. The algorithm used ~6 m/pixel Context Camera (CTX) images from Mars Years 28–36, scanning through a total of 132 359 images. False positives were manually removed with the help of the Zooniverse platform, resulting in 13 409 detections. This survey presents the most spatially and temporally exhaustive global catalogue of dust devils to date. We confirm many trends revealed in disparate previous studies. For example, approximately half of the detections are concentrated in the Amazonis Planitia monitoring site – a hotspot identified from previous imaging campaigns. In addition, we confirm that orbital observations are not well-suited for detecting dust devils at landing sites, despite the ubiquitous detection of vortices with in-situ data. Our study reveals previously understudied hotspots where dust devil lofted clouds can be seen from orbit, most notably southern Hellas Planitia where only dust devil tracks had been previously extensively reported. Importantly, our results reveal latitudinal clusters of dust devils, and in particular large dust devils, at around 60°N and 60°S during local summer solstice, which had only been hinted at by previous work. This concentration is at a much higher latitude than previous modelling suggests, indicating that dust devil generation on Mars is controlled by more factors than are currently accounted for.

<footnote><sup>a</sup> Nantes Université, Univ Angers, Université du Mans, CNRS UMR 6112, Laboratoire de Planétologie et Géosciences, France</footnote>

<footnote><sup>b</sup> Center for Space and Habitability, University of Bern, Bern, Switzerland</footnote>

<footnote><sup>c</sup> ETH Zurich, Zurich, Switzerland</footnote>

<footnote><sup>d</sup> Carl Sagan Center, SETI Institute, Mountain View, CA, USA</footnote>

<footnote><sup>e</sup> School of Physical Sciences, The Open University, Milton Keynes, UK</footnote>

<footnote><sup>f</sup> Department of Materials Science & Engineering, University of Washington, Seattle, WA, USA</footnote>

<footnote><sup>g</sup> Department of Physics, Boise State University, Boise, Idaho, USA</footnote>

<footnote><sup>h</sup> Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands</footnote>

<footnote>* Corresponding author.
E-mail address: susan.conway@univ-nantes.fr (S.J. Conway).</footnote>

<footnote n="1">¹ shared first author.</footnote>
# 1. Introduction

Dust devils are natural wind vortices that lift dust from the surface, creating a visible “cloud”. They are common phenomena in desert regions on Earth (e.g., Lorenz et al., 2016) and have been observed on the surface of Mars by orbital (e.g., Cantor et al., 2006; Thomas and Gier-asch, 1985) and in situ missions (e.g., Greeley et al., 2006; Jackson, 2022; Newman et al., 2019). Their action is primarily driven by vigorous dry convection, yet the exact environmental, topographic and surface properties that trigger the initial instabilities are non-unique and so they are most often, but not uniquely, found at the hottest times of year/day (Balme and Greeley, 2006). They are typically comprised of three parts: i) the interface between the ground and the vortex, which is where the dust is “inhaled”, ii) the rotating column from which little dust escapes and iii) the top of the column where the vortex breaks down and the dust is liberated as a cloud into the atmosphere (Sinclair, 1966). From orbit, the liberated cloud of dust is the most obvious feature, appearing as bright and ranging from opaque to translucent. The column is often masked from direct orbital observation by the summit cloud, yet it can be inferred from the distinctive shadow it casts on the ground (Fig. 1). The direct contact point between the bright cloud and the shadow implies the presence of the contact zone with the ground. In contrast, in situ observations by landed hardware can more easily identify the column and contact point, and less easily identify the liberated dust cloud.

Dust devils on Mars have been intensely studied because of their role in the martian dust cycle. Dust is an important atmospheric component and drives many aspects of the atmospheric circulation (Basu et al., 2006; Newman and Richardson, 2015). However, the source of dust in the martian atmosphere is poorly constrained and dust devils are suspected to be a major contributor (Basu et al., 2006; Kahre et al., 2006).

Since the ambient conditions under which dust devils arise remain obscure, assessing their exact contribution to the martian dust budget is difficult. A large survey for dust devils could help constrain that contribution by helping elucidate their formation conditions and how frequently they occur in different regions on Mars. Further, as dust devils remove a layer of dust from the surface, their action cleaned the solar panels of the Mars Exploration Rovers (MER), contributing to their longevity (Lorenz and Reiss, 2015). In contrast, although this action was anticipated for InSight, vortices at this site were not strong enough to lift the dust from the solar panels, leading to the mission stopping after only one Mars Year (Lorenz et al., 2021). Hence, understanding the action of dust devils is also important for in situ martian exploration driven by solar powered missions.

Previous studies have shown that dust devil diameters can range from metres as observed by rovers/landers (e.g., Jackson, 2022) to more than a kilometre (e.g., review by Fenton et al., 2016) and are therefore detectable by the majority of orbital imagers. Studies by rovers give a more densely temporally sampled, yet spatially limited vision of dust devil activity (e.g., Greeley et al., 2006; Jackson, 2022; Newman et al., 2019). In contrast, surveys for dust devils in orbital images can capture seasonal trends over a spatially large area and so there is a trade-off between spatial/temporal coverage and spatial ground sampling. In orbital images dust devils are identified by their cloud and the attached shadow (indicating ground contact) - Fig. 1. However, they are often hard to identify and currently require each image to be manually searched (e.g., Cantor et al., 2006), which, given the huge quantity of available orbital images, is a daunting task.

Here, we use data from the Mars Reconnaissance Orbiter Context (CTX) camera with a nominal ground sampling of 6 m/pix and whose images cover ~40% of the martian surface per Mars Year (MY) as shown

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Examples of validated dust devil detections from CTX images with a variety of shapes and sizes. North is up in all images and the CTX image ID is below each image. CTX image credits: NASA/JPL/MSSS.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Examples of false positives including mesas and craters that either have strongly contrasting light and shaded slopes in close proximity or cast a linear dark shadow. From top-left to bottom-right these features are: small conical hill, oblate-shaped hill, impact crater, small conical hill, crescent-shaped hill and elongate mesa. North is up in all images. CTX image credits: NASA/JPL/MSSS.</figcaption></figure>

in Fig. S1. CTX is able to detect dust devils whose cloud and/or shadow are more than approximately 6 pixels in size and up to the width of the detector. The previous studies using CTX data were mostly restricted to Amazonis Planitia (from 196 to 208°E and 30 to 40°N; Fenton and Lorenz, 2015; Guo et al., 2024) and Fenton and Lorenz (2015) only identified dust devils from MY 28 to 31. We use a machine learning approach to expedite the search through the 132 359 images captured up to June 1, 2022 (Mars Years 28–36; MRO orbits 9 to 74277). The objective of the global mapping campaign is to advance our understanding of the general spatio-temporal distribution of dust devils on Mars, to identify previously unknown regions that host concentrations of dust devils – or appear to be dust devil free at the CTX-scale.

## 2. Data and methods

### 2.1. Data

CTX is a pushbroom sensor that takes single-band images in the spectral range between 500 and 800 nm, with swath widths of about 30 km wide and a median swath length of about 120 km (Malin et al., 2007). Typical images have a ground sampling of ~6 m/pix, are on average ~19k lines long (ranging from 169 to 190k lines) and are usually a fixed 5056 samples wide. CTX images are taken at approximately 3 p.m. local time on Mars. Each Mars Year CTX images cover typically at least ~30% of the planet (Fig. S1). For the Convolutional Neural Network (CNN) driven mapping, we used full-resolution, 8-bit, map-projected .tiff CTX images, downloaded from the CTX ASU image server, specifically by using targeted queries to the server: https://image.mars.asu.edu/stream/. An example of

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Example of detections on part of CTX image P01_001393_2161_XN_36N156W. (a) Image only. (b) Detections made by the CNN outlined in yellow bounding boxes. (c) By comparing with the extended Fenton and Lorenz (2015) dataset, the detections are classed into True Positives (TP) in green, False Positives (FP) in red and as black crosses False Negatives (FN).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Performance of the CNN on the regional dataset (Amazonis Planitia). In all plots the CNN Score is binned into 0.05 intervals from 0.2 to 1 and the central value is given on the x-axis. (a) Number of False Positives vs CNN Score. (b) Percent of False Positives (100 – precision) vs CNN Score (c) Absolute value of True Positives (i.e. number of dust devils successfully detected). vs CNN Score. (d) Percent of True Positives vs CNN Score. In all panels the dashed vertical line is the chosen CNN Score for the global dataset.</figcaption></figure>

such a query to produce a 1200x1200 tile at pixel coordinates x = 4762, y = 10875 on CTX image B19_017096_1712_XN_08S105W would be: https://image.mars.asu.edu/subset?image=/mars/images/ctx/mrox_10_56/prj_full/B19_017096_1712_XN_08S105W.tiff&format=JPEG&tilesize=1400,1400&region=4762,10875,1400,1400.

By querying the Planetary Data System “label” files (.LBL) and cumindex files, we created a header file for each CTX image that contains study-relevant metadata, such as the latitude and longitude of image corners as well as the image acquisition date (in UTC, Mars Year, Solar Longitude and local solar time) and geometry (spatial resolution,

emission, incidence and phase angles). Using the position of dust devil detections within an image, we reconstructed their position in real world coordinates, using only the parent images' meta-information.

## 2.2. Dust devil detection with an off-the-shelf CNN

Given the lack of available dust devil training data, the performance of any deep learning-driven object detector is mainly controlled by 1) the complexity and uniqueness of the to-be-detected feature and 2) the amount and quality of available training data. We use an off-the-shelf (vs customized) object detector called RetinaNet (Lin et al., 2020), anticipating that the scarcity of training data is the main factor controlling the detection performance, not the network architecture. RetinaNet has proven its performance and capability during numerous planetary science studies, for example, involving the detection of mass wasting features such as rockfalls and granular flows, as well as fractured boulders (Bickel et al., 2020a, 2022; Ruesch and Bickel, 2023). RetinaNet is a scale-invariant single-stage detector consisting of a combination of two sub-networks, a feature pyramid network and a ResNet (He et al., 2016). It reaches top performance on datasets like COCO (Common Objects in Context - arXiv:1405.0312), is very fast, and is well-established throughout the literature (Walter et al., 2022; Weinstein et al., 2019; Zhang et al., 2022). Fizyr implemented an open-access version of RetinaNet using the Keras python interface to the TensorFlow library, making RetinaNet easy to adapt to specific purposes (https://github.com/fizyr/keras-retinanet).

## 2.3. Dust devil labelling and (few-shot) detector training

Labelling (annotation) of training data, i.e., the manual localization of dust devils in CTX images, is a challenging task, because of their dynamic appearance and irregular shape. However, martian dust devils have three main characteristics that distinguish them from other objects on the martian surface (compare Figs. 1 and 2): 1) the compact and bright core in contact with an elongated dark shadow; 2) the combination of areas of very high- (core/body) and very low-intensity (shadow) pixels, with a smooth transition into the surrounding terrain and 3) due to MRO's sun-synchronous orbit, dust devils in map-projected images will always cast an elongated shadow towards the east, while their bright core is located on the western side of the feature. We follow labelling routines that were established in previous work (Bickel et al., 2020b, 2021). There are certain landforms and features that can lead to false positives; common examples are mesas and pitted cones – geologic features with a positive topographic relief (Fig. 2), and in polar areas seasonal phenomena. These features are easy to reject by a human operator.

In total, we used 34 CTX images with varying spatial resolutions for labelling, cropped into 1000 x 1000 pixel patches. In these patches, we labelled 198 dust devils of varying sizes and shapes, traversing various surface types, including sandy and rocky surfaces. Dust devil clouds and shadows together range from ~6 pixels to the whole width of the patch. The labelled dust devils are randomly located across their respective host patches. We used these labels to train a total of 2 detectors, one for 70°N to 70°S (non-polar), and one for the polar regions beyond 70°N and S. We chose to train a separate polar detector to account for the fact that the visual appearance (i.e., the location of the shadow) of dust devils can slightly differ at very high latitudes due to illumination geometry and MRO's orbit.

The images used for labelling have been acquired over all of Mars with 15 located in the Amazonis monitoring area (Fenton and Lorenz, 2015; Guo et al., 2024). Supplementary Table S1 shows the IDs of all images used for training. We note that 198 labels are a relatively small number for the training of a deep learning-based object detector (few-shot training). However, the unique appearance of dust devils makes it easier for a detector to learn their most important characteristics (as listed above) even in small training datasets. We note that recent studies
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** For the global dataset, plots of recall (percentage of True Positives) against: (a) CNN score where the x-axis labels represent the upper bound of the bins (e.g. 0.525 is in the interval 0.5–0.525), (b) latitude, and (c) x dimension of the bounding box size in metres. For panels b and c only data with CNN Score >0.5 are presented.</figcaption></figure>

successfully used even smaller training and validation datasets, such as Bickel et al. (2024). In order to boost the robustness of the detector, we included a total of 44 and 100 negative patches in the non-polar and polar training sets, respectively, cropped (1000 x 1000 pixel) from the 34 full CTX images, consisting of features like large outcrops, mesas, and craters. These negative patches do not include dust devils. Due to the extremely small number of labels, we made extensive use of data augmentation techniques during training, namely image shear, scaling, flipping, rotation, and up- and down-sampling to produce a larger set of augmented dust devil labels, applied to both the positive labels and the negative patches. For example, shearing helps the neural network to generalize to a wider range of emission angles and dust devil morphologies. As the appearance as controlled by the sun-synchronous orbit

of MRO is one of the key characteristics of non-polar dust devils, we only applied rotation within a ±10° angle range and no image flipping (left-right, up-down) during the training of the non-polar detector (named Devil1E50). The training of the polar detector (named Dev1IP50) involved a wider range of image rotation angles (±30°) as well as up-down flipping to account for the wider variety in lighting geometry.

Both dust devil detectors have been trained with 198 labels and a ResNet50 (Residual Network 50, He et al., 2016) backbone. Dev11E50 was trained over 26 epochs, using a total of 6292 augmented dust devil labels. Here, the term epoch describes one pass of the entire (augmented) training dataset through the neural network. Dev11P50 was trained over 75 epochs, using a total of 22 350 augmented dust devil labels used for training. Dev11P50 required more time to train due to the
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Percent confirmed true positive dust devils with CNN score >0.5 (grey) for each elevation bin (top row) and TES albedo bin (middle row) compared to the overall distribution of Mars values (blue symbols corresponding to blue y-axis in percent of the overall number of pixels). Bottom row is the distribution of data by season as denoted by solar longitude). The MOLA elevation values were extracted from the global gridded map (ground sampling of ~450 m/pix) and the TES albedo values were extracted from the global map mosaic (ground sampling of 7410 m/pix) at the locations of dust devils in our survey for the grey bars and from the whole dataset in the blue points. Histograms containing all data are on the left and excluding the Amazonis, Arcadia, Syria and Meridiani monitoring sites on the right.</figcaption></figure>

more pronounced data augmentation (rotation & flipping). We used RetinaNet's focal loss function with an 80-20 split for training and validation, i.e., 80 % of the labels were used for training and 20 % were used for validation. We stopped training as soon as the loss regression stagnated after several learning rate cutbacks. We note that we performed detector evaluation (testing) on a regional, expert-derived dust devil dataset based on the one created by Fenton and Lorenz (2015) in the Amazonis Planitia region (see section 2.4).

## 2.4. Regional detector validation in Amazonis Planitia

In order to evaluate the detector performance, we deployed Devil1E50 to 1402 CTX images taken over Amazonis Planitia using a maximum recall approach. Specifically, we used a very low CNN Score of 0.2 to define the cut-off at which detections predicted by the neural network were rejected, hence maximising recall. We hosted Devil1E50 in a processing pipeline adopted from Bickel et al. (2020a) that was modified to operate with CTX imagery and its metadata. The input CTX images were tiled in square patches (1000 x 1000 pixels) which were
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** (a) Global map of all dust devil detections in our study (black points) over the colourised and hillshaded global MOLA elevation map where blue represents low elevations (around –8000 m) and red high elevations (~21200 m). White boxes indicate CTX monitoring sites referred to in the text. (b) Frequency of dust devil detections in our study with latitude. (c) Frequency of dust devil detections in our study with latitude excluding monitoring sites marked in panel a, normalised by available image area in km² and multiplied by 10⁶.</figcaption></figure>

then processed by Devil1E50. All detections made by the CNN were stored and their locations translated to parent image coordinates and then to real world coordinates, using the image-associated metadata files. The output of the pipeline is GIS-ready and includes: 1) an image thumbnail of each detection, 2) the footprint of each CTX image scanned, and 3) a matrix with the following information about each dust devil candidate: a) internal ID, b) CTX ID, c) acquisition Earth date and time, d-e) detection centre latitude and longitude, f-m) various detection box properties, such as box image coordinates as well as box width and height, n) CNN Score, o) CTX image spatial resolution, p-r) CTX emission, incidence, and phase angles, s-w) various solar and spacecraft variables, such as sub solar longitude, sub solar latitude, spacecraft altitude and local Mars time.

All detections made by Devil1E50 have been compared to a testing database created by human operators (authors L. Fenton, H. Carson and S.J. Conway) – the extended Fenton and Lorenz (2015) dataset. The testing database is completely separate from the training and validation dataset described above to avoid information leakage. This dataset comprises 3135 dust devil identifications across 138 CTX images of the 173 searched (Supplementary Table S2). The data includes the location and estimated heights from shadow length measurements (where possible) of 2191 dust devils in Amazonis Planitia measured across 127

CTX images from MY 28 to 31 published in Fenton and Lorenz (2015). The published data were expanded by 11 images up to the end of MY 31 adding 288 identifications (by H. Carson) and this dataset was supplemented by 656 additional identifications within the whole dataset (by S. J. Conway) (Table S2).

The detector performance has been assessed with recall and precision, where the extended Fenton and Lorenz (2015) dataset served as ground truth. Here, the recall represents the percentage of dust devils detected (e.g. 8/10 dust devils have been detected), described as (True Positives)/(True Positives + False Negatives); precision describes the percentage of correct detections (9/10 detections are correct), described as (True Positives)/(True Positives + False Positives), as implemented by and described in detail in previous studies (Bickel et al., 2019, 2020b). An example of dust devil detections is shown in Figs. 1 and 3.

The network CNN Score quantifies the confidence (posterior probability) of the detector in a detection: generally, higher CNN Score values would produce lower recall, but higher precision, while lower CNN Score values would produce higher recall, but lower precision, as detectors tend to detect more low than high confidence features. Recall and precision are derived by counting the true positives (TP, correct detections), false positives (FP, false detections), and false negatives (FN, missed detections) produced by the detectors in the test set. Testing has

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Global map of all dust devil detections in our study coloured by bounding box width (purple >1000 m, green <1000 m) over the TES global albedo map (black corresponds to bolometric albedo of ~0.06 and orange ~0.32).</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Fig. 9. Global maps of dust devils coloured by bounding box width and CTX image coverage (white indicates no image available) per season as given by Solar Longitude ranges (denoted by Ls). See the main text for justification of the colour cut off.</figcaption></figure>

been performed with a standard non-maximum suppression filter (NMS) with an Intersection over Union (IoU) value of 0.3 to remove potential double detections, i.e. overlapping bounding boxes. An NMS filter removes duplicate detections by using the bounding box and confidence information; the IoU describes the amount of overlap between a CNN-predicted bounding box and the ground truth (test set) bounding box.

## 2.5. Global-scale dust devil mapping

Ultimately, we deployed Devil1E50 and Devil1P50 to the full MRO CTX image archive with 132 359 individual images (Table S3), limiting Devil1E50 to latitudes from 70°N to 70°S and Devil1P50 to latitudes >70°N and >70°S. We used one local (1x NVIDIA Titan Xp) and 4 Google Cloud Platform machines (4x NVIDIA Tesla T4) to download and process the entire CTX projected image archive in ~6 weeks. We used the same processing pipeline and outputs as described in Section 2.4. At this step, we added an estimate of dust devil size based on the relation between the x-dimension of the CNN-placed bounding box and the cloud size (the physical size of the top of the dust devil column where the vortex is liberated as a dust cloud) of the detected dust devil, as measured manually by a human operator using Zooniverse. Zooniverse is typically used for Citizen Science, yet here it was used simply as an efficient online digitising tool for our team, allowing us to iterate quickly through image clips. The human operator (co-author A. Blouin) placed an ellipse over the brightest part of the dust devil and the cloud size is taken as the mean of the minor and major axes. This relationship has been established with 862 randomly chosen detections (Table S4) and is plotted in Fig. S2 in the supplement. The spatial distribution of these data are shown in Fig. S2. Although the data have a lot of scatter, we found a natural cut-off in the data at bounding box widths of ~1000 m: below this value 75% of the detections have a cloud size less than 65 m and above this value 75% of detections have a cloud size of 67 m or greater, we used this bounding box cut-off value to define “large” and “small” dust devils in later plots.

Independently of the CNN-based mapping step described above, we used a separate Convolutional Neural Network called VGG16 (with all layers were pre-trained in ImageNet) (Simonyan and Zisserman, 2014) to cluster all previously detected dust devil candidates into 20 individual classes in an unsupervised manner (unsupervised clustering), solely using their visual appearance, following the workflow introduced by Ruesch and Bickel (2023). This separate clustering step groups the previously detected dust devils as well as any false detections in clusters – despite being agnostic to dust devils or any other martian surface feature – largely facilitating manual review and batch-removal of false positives. The whole dataset of images clipped to the bounding boxes was first reviewed manually by S.J. Conway and V. Bickel. This initial review comprised a first pass where we removed images containing features that were clearly not dust devils, including: impact craters, barchan dunes, flat-topped mesas or flat-bottomed pits with adjacent shaded/well-lit slopes, seasonal polar jets, and conical mounds (e.g., Fig. 2), this step was facilitated by the VGG16 output. In a second pass, we examined the more ambiguous cases, eliminating albedo patterns that did not have a similar intensity in the black and white, where the outlines were not diffuse and/or irregular (allowing terrain underneath to show through) and where the bright part was much longer than the dark part (for dust devils, either the dark shadow is much longer or in a few cases with little wind the same size as the cloud). At this stage we kept the majority of ambiguous features, because afterwards we performed a final review using 1400x1400 pixel clips centred on the bounding box detection in Zooniverse involving S. J. Conway, E. Blanc, A. Blouin, J. Crevier, B. N. Nguyen and L. Roelofs. We also collected other data in the platform (not reported here), but the first step of this larger workflow was to identify whether there was a dust devil in the scene, using the same identification criteria as above. The advantage of using this wider field of view than the bounding box is that it was easier to: i) identify albedo patterns (usually hills) that resemble dust devils by comparing to other examples in the scene, ii) compare the texture of the surrounding terrain to the clarity/visibility of the terrain in the
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Maps of Amazonis region. Top row from left to right: Global TES albedo map with red box marking the location of the other panels, THEMIS day IR controlled mosaic by USGS and colourised MOLA elevation. The remaining rows are the same plots as in Fig. 9 zoomed in on Amazonis.</figcaption></figure>

bounding box which would be obscured if there was a dust devil, and iii) observe the whole of the cloud and shadow if present, as the bounding box often cut off the end of the shadow and sometimes also part of the cloud. At this stage any remaining features considered to be ambiguous were not included.

# 3. Results

## 3.1. Regional evaluation of Devil1E50

This section presents the results of the initial test of the CNN at a regional scale over Amazonis Planitia as described in Section 2.2. In this test $Devil1E_{50}$ needed ~0.04 s to scan through one single CTX patch. When all pre- and post-processing steps of the pipeline are included, the total processing time per CTX image was ~45 s, enabling a highly effective mapping of the martian surface, where ~3000 CTX images can be processed per day using a single desktop computer and an NVIDIA Titan Xp. These processing speeds were achieved using a local copy of the respective images and, thus, do not consider image download times.

One hundred and thirty eight images in the extended Fenton and Lorenz (2015) dataset contained dust devils and they were all identified by the CNN. Two additional images were identified with detections by the CNN compared to the human catalogue, but these detections were false positives. This is compared to the machine learning study of Guo et al. (2024) where they only find 14 of these images with dust devils. Within these 14 images they report in all but one case, finding between 2 and 8 additional dust devils (for the one exception our CNN detected 9 more) compared to our study. We excluded the dust devils that are counted twice in their study due to the way the images are split into

overlapping tiles. In the same images the human operator-created dust devil catalogue detailed in Section 2.4 contained between 20 and 57 identifications (Table S5), which amounted to 2–19 more than detected by Guo et al. (2024). Hence, we consider the within-image performance broadly similar between our detectors. In comparing the CNN-derived data (>0.2 CNN score) with the human operator-created dust devil catalogue detailed in Section 2.4 (Fenton and Lorenz, 2015) we find 1727 dust devils were identified by humans and the CNN, where 52 of these were centred on the end of the shadow rather than the cloud-shadow part. 105 (~6%) detections were false positives. There were 230 new identifications missed in the original human catalogue. There were 1437 false negatives or dust devils that were missed by the CNN. The dust devils missed by the CNN are smaller than those detected by it, with median shadow lengths of 710 m compared to 940 m. These results are presented in Table S6.

The performance of the detector is summarized in Fig. 4, where we show how numbers and percent of FP and TP vary with CNN Score. Bearing in mind the detection is a cumulative process with decreasing CNN Score where generally reviewing at CNN Score ≥ 0.7 gives the best ratio between effort and recall, our plot shows many more dust devils could be recovered at lower CNN Scores. Hence, given these data we chose a CNN Score of 0.5 to use as a reasonable lower limit in the global study, considering the jump in false positives at values lower than 0.5.

It is worthwhile to note that despite the extremely small training dataset, the performance of the non polar detector is remarkable. Considering the effectiveness of the detectors, the utilization of other or more sophisticated object detection architectures appears redundant – as anticipated – as a potential improvement would likely be minimal. We did not test the polar detector in the same way, but given that the labels
<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Dust devil frequency with season (solar longitude) in the northern (>10°) and southern (<-10°) hemispheres and equatorial region (10°S-10°N). The dashed blue line indicates the northern hemisphere summer solstice and the red dashed line indicated the southern hemisphere summer solstice, with blue zones indicating the hot period in the northern hemisphere and pink zones indicating the hot period in the southern hemisphere. The data are divided into the northern (>10°) and southern (<-10°) hemispheres and the equatorial region (10°S-10°N). (a) Percentage of the total number of dust devils observed in each geographic zone and (b) the number of dust devils in each column is normalised by the areal extent of the images available in km² for that geographic zone and solar longitude bin – figures are shown multiplied by 10⁶ for ease.</figcaption></figure>

are identical and that we used only slightly more augmentation we expect the performance to be nearly identical.

## 3.2. Global mapping of dust devils

This section describes the global mapping of dust devils using the CNN (detector Devil1E50 for latitudes <60° and Devil1P50 for latitudes >60°) over 132 359 CTX images over Mars Years 28–36. In this analysis we take into account the potential influence of sites targeted for regular monitoring by the CTX team following the study by Cantor et al. (2006) by presenting plots with and without these sites. Compared to the regional scale study detailed so far, in the global study we were not able to match the processing speed of the regional study (~3000 images/day) due to download speed limitations; the average processing speed was ~528 images/day/machine (factor ~6 lower). Our detectors identified 67 284 dust devil candidates in the CTX image archive and out of these 13 409 were confirmed as true positive detections by an experienced human operator (see Data Access for a link to these data). We found 4513 CTX images containing dust devils (3.4% of images). Hence, the true precision (20%) was below what was expected for a CNN Score value better than 0.5 from the regional dataset (Fig. 4), which was around 94%. We attribute this false positive rate to the greater variability of terrains outside the Amazonis region leading to a higher rate of false positives. We note that this discrepancy between test-set and inference performance is common for the application of object detectors in very large datasets (see, e.g., Bickel et al., 2024, 2022, 2020a; Mills et al., 2024; Rüsch and Bickel, 2023).

Guo et al. (2024) reported 100 images where they detected dust devils globally and these were all input images to our study. The image identifiers of the images they scanned where no dust devils were detected were not provided. Of the 100 images, six did not contain validated dust devil detections in our study. In one case we could not identify a dust devil in the Guo et al. (2024) data, in the other five cases dust devils were identifiable in the Guo et al. (2024) data and either no detections were recorded or only false positives in our dataset. For the remaining 94 images: in five cases the number of dust devils detected was the same, in seventeen cases our CNN detected between 1 and 30 additional validated dust devils and in the remainder the Guo et al. (2024) detected between 1 and 17 additional dust devils (Table S7). The lack of georeferencing information and limited number of images scanned in the Guo et al. (2024) study meant we could not use it further as a means to robustly assess the performance of our detector.

Fig. 5 shows how the percent of true positives (compared to true and false positives) varies with CNN Score, latitude and bounding box dimension. As expected, the number of correct detections increases sharply with CNN Score, with percent true positives being only 10% at 0.5–0.525 CNN Score value, rising slowly to 20% at 0.65–0.675 CNN Score value, then more steeply to 60% at 0.8–0.825 to a peak of 70% at 0.9–0.925. The percentage of true positives varies considerably with latitude, with no particular trend apart from particularly low percentages (<10%) near the poles which we attribute to seasonal processes (e. g., seasonal fans – Diniega et al., 2021) mimicking dust devil albedo patterns. In contrast, a clear trend in true positive percentage is observed with bounding box size, which can be considered as a rough proxy for
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Dust devil frequency with season (solar longitude) and Mars Year in the northern (>10°) and southern (<-10°) hemispheres and equatorial region (10°S-10°N). The percentages are calculated for each panel, i.e. as a percentage of the total number of dust devils observed for each Mars Year in each geographic zone. The blue dashed line is the northern summer solstice and red dashed line the southern summer solstice, with blue zones indicating the hot period in the northern hemisphere and pink zones indicating the hot period in the southern hemisphere. Green lines are the periods with global dust storms referred to in the discussion. A version normalised by available image area is in the supplement (Fig. S17).</figcaption></figure>

dust devil size. At bounding box sizes less than 500 m (dust devil cloud sizes less than ~60 m – Fig. S1), the percent of true positives is less than 10%. Between 500 and 725 m (dust devil cloud sizes less than 100 m), there is a linear increase in the percent of true positives up to 25%, where it levels off and remains between 25 and 30% up to the largest bounding box sizes (5 km). We attribute the observed decrease in detector performance for small dust devils to the fundamental issue of feature uniqueness, where smaller features are represented by fewer pixels and are, thus, more likely to be confused with other objects. The largest bounding box size is an upper limit determined by the 1000-pixel patch size used in our training data. This shows that there are fewer confounding features at bounding box sizes >725 m.

The true positive detections of dust devils with CNN Score values > 0.5 are globally distributed where the data access statement provides the whole dataset and links to the image crops themselves. Dust devils are found at all elevations (Fig. 6), and their distribution broadly matches that of martian elevation values, particularly once the monitoring sites are excluded. The highest elevation dust devil detected is at 20 402 m on top of Olympus Mons in both datasets. The monitoring sites are located in Amazonis, Arcadia, Syria and Meridiani (Fig. 7) and have been specifically targeted for regular monitoring by the CTX team following the study by Cantor et al. (2006) and hence could present a potential spatial bias in our results. Equally, dust devils are found on terrains with a range of albedo values that generally correspond with the range shown by Mars (Fig. 6). However, neither the distributions with or without the monitoring sites perfectly match the distribution of albedos found on Mars. The dust devil albedo data show a bimodal distribution with peaks at 0.15 and 0.25, both of which are present in the global data. Yet the 0.25 peak is stronger in the whole dataset, whereas the 0.15 peak is stronger in the dataset excluding the monitoring sites. It may be that beyond the monitoring sites (principally Amazonis with its relatively high albedo), the bright dust plume may be easier to identify against a darker background.

We use the bounding box width to split the data into two categories:

"small" - for bounding boxes <1000 m the corresponding dust devil has a 75% chance of having a cloud size smaller than 65 m, and "large" for bounding boxes >1000 m the corresponding dust devil cloud has a 75% chance of being larger than 65 m. Figs. 7 and 8 show.

* With the exception of the monitoring sites, there is a general concentration of dust devils (of any size) at 50-65°S and 40-70°N.

* Excepting Amazonis/Arcadia, there are more dust devils (of any size) in the southern mid-latitudes than in the north.

* Dust devil occurrence and size is not strongly controlled by elevation. Note the 50-60°S zone in which dust devils, and in particular large dust devils, are common both on the floors of Hellas and Argyre Planitiae (elevations ~ -6500 and ~-3000 m, respectively) and on the southern highlands (elevation ~1000 m)

In Fig. 9 we show the detections together with the corresponding CTX image distribution on a global map divided into 30° intervals of solar longitude. In Fig. 10 the same type of display is used for the Amazonis study area – Figs. S3–16 show the study areas of Cantor et al. (2006) and zoomed-in views of the landing sites of various Mars missions. Several clear trends emerge.

* Large dust devils are concentrated at 60°N from Ls 90°–180° – peaking just after the northern summer solstice (Ls 90° - Fig. 9);

* Large dust devils are concentrated at 60°S from Ls 270°–30° – peaking just after the southern summer solstice (Ls 270° - Fig. 9);

* Large dust devils are generally sparse at equatorial latitudes at all times of year;

* The Amazonis zone (Fig. 10) has a high spatial density of dust devils from Ls 330 to 180° and large dust devils are always present amongst these detections; and

* A clear spatial cut off in dust devil occurrence is observed in the northern part of the Amazonis study zone where the albedo of the surface transitions abruptly from high to low (this appears as a
<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Plot of percent of dust devils detected by the CNN vs measured shadow length, for the 2307 dust devils with measured shadow lengths from the extended Fenton and Lorenz (2015) dataset (see Sections 2.4 and 3.1). The percent of dust devils detected by the CNN is calculated for every 230 human identifications in ascending order of shadow length, hence the bars have different widths.</figcaption></figure>

bright, high temperature region in the THEMIS day IR mosaic in Fig. 10). We note that this cut off is not an artefact of image coverage as it can be observed inside individual images, as also reported by Fenton and Lorenz (2015) and Cantor et al. (2006).

Figs. 11 and 12 show the number of dust devils detected as a function of latitude and season, where Fig. 11 aggregates all the data from Fig. 12 and S17 where the trends are shown for each Mars Year individually. In general, in the northern hemisphere there are few or no dust devils between Ls 180° and 330° (northern winter); the number sharply increases between Ls 30° and 60° and remains high until Ls 150–180° (Fig. 11). The timing of the peak in frequency depends on Mars Year and seems to be somewhat influenced by global dust storms (Fig. 12). In MY29, the northern peak occurs early in the dust devil season which immediately followed the MY28 dust storm (Ls 260–290°; Smith, 2019). In MY34, the peak in frequency occurs instead at the end of the dust devil season (Ls 180°) coincident with the start of a global dust storm (Ls 185–193°; Smith, 2019). It should be noted that peaks in frequency at the start of the season also occur in MY 35 and at the end of the season in MY 30.

For the equatorial zone, the aggregated data reveal dust devils have a low frequency from Ls 150–230° and two peaks around at Ls 30° and Ls 280° (Fig. 11). However, Fig. 12 and S17 show that there is considerable interannual variation in this pattern. For example in MYs 28, 30, 31, 33 and 34 the southern summer peak is near Ls 270°, whereas the other Mars Years it has a more variable timing. In MY 29 there are two peaks near Ls 30° and 330°, in MYs 32 and 36 the peak is strong and near Ls 30–60°. For MYs 28 and 34, the peaks occur either side of the global dust storms of those MY, with the actual period of the dust storm having a low rate of detections as the storm obscured the surface.

For the southern hemisphere, the peak in dust devil occurrence is from Ls 300–330°, where the ramp up to that peak occurs from 270 to 300° and then afterwards the number slowly diminishes towards a background level (Fig. 12). Unlike in the northern hemisphere, no season fully lacks dust devils. Further, the southern hemisphere during southern spring is far more quiescent than northern hemisphere during northern spring. In general, the seasonal trend in the aggregated data is

reflected in the annual data (Fig. 11) with some interannual variability mainly explained by global dust storms. Dust devil counts normalised by CTX areal coverage indicate that the solstitial global storm of MY 28 suppressed dust devil formation and detection throughout the southern summer, whereas the equinoctial global storm of MY 34 may have only slightly reduced the southern summer dust devil peak.

# 4. Discussion

We will compare our results to those found in other studies. Before doing so, we will discuss the limitations of our dataset that could affect these comparisons.

## 4.1. Dataset limitations

One of the limitations of our method is that the CNN tends to perform better for larger dust devils compared to a human studying the same image as shown by Fig. 13. At dust devil shadow lengths up to ~310 m the CNN only returns 41% of those found by humans, whereas for shadow lengths greater than ~430 m over 70% of the human identified dust devils are found. The CNN returned bounding boxes greater than 40 pixels (~240 m), demonstrating the lower limit of detected objects. At the larger end, the CNN returned bounding boxes limited to ≤1100 pixels (~6.3 km or 20% of the swath width) and so the CNN should be able to detect the largest dust devils reported of 1.65 km in diameter (Stanzel et al., 2008). From orbit dust devil size is typically quantified using the size of the cloud, and/or the length of the shadow. In our detections, the cloud was usually completely contained within the detected object, yet the shadow often extended outside of the bounding box. Occasionally the shadow was identified without the cloud (for 3% of detections in Amazonis), indicating that dust devil vortices located outside the image boundary can still be detected. In in situ images dust devil vortices are measured using their vortex column width and sometimes column height. The column width does not correlate well with cloud diameter nor shadow length measured from orbit.
## 4.2. Comparison to previous orbital data studies

Our dataset is of comparable magnitude to the previous largest global survey of dust devils from orbit produced by Cantor et al. (2006) who found 11 456 dust devils in Mars Orbital Camera (MOC) NA and WA images with a spatial resolution of 1.5–12 and 240–500 m/pix, respectively, spanning Mars Years 23–27. They detected dust devils in 0.4% of images inspected, compared to 3.4% of CTX images in our study. MOC-NA only covered ~5% of Mars in the mission lifetime, which probably explains this low figure. Despite the variable imaging resolution and spatial coverage compared to CTX their survey found more dust devils per year than our survey (2300/MY compared to 1600/MY). This discrepancy could be partly explained by MOC images being taken generally at an earlier local time of 13:00–15:00 compared to CTX at 15:00–15:30. In situ studies consistently show that dust devils are most active just after solar midday and their occurrence drops off steeply by 15:00 there is only 1/3 the number of dust devils compared to midday (e.g., Hueso et al., 2023). Orbital studies with CaSSIS also show this steep drop-off (Bickel et al., 2023). In addition, we know that at ~46% of dust devils are missed by the CNN compared to a human (particularly small ones). Further, the Cantor et al. (2006) survey likely picked up many smaller dust devils in the (commonly higher spatial resolution) MOC-NA images difficult to detect with our CNN in CTX data, and these small dust devils are known to be more numerous (Kurgansky, 2022; Lorenz, 2012). The average detectable diameters of the MOC survey were assumed to be 200 m for the dust devils detected with the WA camera and 20 m for the dust devils detected with the NA camera. Prior to the Cantor et al. (2006) study, global surveys had been performed with Viking Orbiter data, where fewer than 100 dust devils were detected over MY 12–14 (Thomas and Gierasch, 1985). Stanzel et al. (2008) performed a survey of HRSC images (≥15 m/pix) MY 26–28 in Amazonis Planitia, Chryse Planitia, and Syria Planum finding only 205 dust devils. Subsequent orbital surveys with CTX have been regional in extent (e.g., Fenton and Lorenz, 2015; Guo et al., 2024). Hence, our survey represents the most comprehensive global survey with CTX data to date. CTX presents a consistent year to year coverage of Mars (Fig. S1) at a consistent spatial resolution overcoming some of the limitations in deducing seasonal and interannual trends encountered with other imaging datasets, such as Viking, THEMIS or HRSC.

Our survey reveals spatio-temporal trends not identified by the Cantor et al. (2006) study using MOC – notably the two zonal bands of high dust devil concentration (and size) at 40–70°N and 50–65°S just after summer solstice (Fig. 7). The southern band was noted by Stanzel et al. (2008), but not the northern one, likely because of spatio-temporal coverage limits of the HRSC dataset at the appropriate local time, as HRSC imaging is not sun-synchronous. These bands were also reported by studies examining the seasonal appearance of dust devil tracks. For example, Grant and Schultz (1987), Balme et al. (2003), Geissler (2005) and Whelley and Greeley (2008) noted this pattern in the southern hemisphere. Whelley and Greeley (2008) also noted the pattern displayed in a subdued form in the northern hemisphere, and Geissler (2005) found a concentration of dust devil tracks between 45° and 60°N only in the dark area of Nilosyrtis. It should be noted that the frequency of dust devil tracks does not correspond with the prevalence of actually visible vortices. Our study reveals these broad patterns were likely missed by the Cantor et al. (2006) dataset.

Dust devil activity has been modelled as convective heat engine within the convective boundary layer (Rennó et al., 1998), permitting numerical atmospheric models to estimate the dust-lofting capacity of dust devils based on atmospheric conditions (Basu et al., 2004; Chapman et al., 2017; Kahre et al., 2006; Newman et al., 2002). Using this method, the energy required to drive daytime dry convective activity (and thus fuel the formation of dust devils) is determined by the sensible heat flux at the surface multiplied by a thermodynamic efficiency parameter related to the height of the convective boundary layer (e.g., Fisher et al., 2005). These factors combine to suggest that optimal dust devil

formation conditions at the solstices occur at latitudes ±30°, contrasting with our observation that peaks in both dust devil number and size occur at a higher latitude. We suggest there are factors yet unaccounted for in dust devil lifting parameterizations. For example, radiative contributions to atmospheric heating (Spiga, 2011) or dynamical atmospheric circulations (Chapman et al., 2017; Tyler et al., 2008) could impact boundary layer turbulent structures in ways not yet understood (Spiga et al., 2016). It is known that ambient conditions also impact dust devil occurrence and vigor. For instance, high winds can suppress dust devil occurrence, as wind shear likely disrupts the convective vortices (Rafkin et al., 2016). Indeed, the lack of imaged dust devils during the InSight mission seems correlated with the relatively large wind speeds observed at that site (Jackson et al., 2021).

Our study's findings agree with the highly concentrated nature of dust devils in Amazonis (Fig. 10) and to a lesser spatial extent just to the west of this hotspot in Arcadia (Fig. S4), in Syria Planum (Fig. S5) and in eastern Sinus Meridiani (Fig. S6) reported by Cantor et al. (2006). These sites were specifically targeted by the CTX team following the MOC monitoring so this concentration is not surprising, yet our dataset reveals the larger extent (outside the monitoring zones) of these three zones than revealed by Cantor et al. (2006). These four sites make up just over half of the dust devils detected in this study (~7600 of 13 409) with just over ~5500 of these being in Amazonis.

Stanzel et al. (2008) noted a concentration of dust devils in Chryse Planitia from their HRSC study, which was not found by the Cantor et al. (2006) or Fisher et al. (2005) studies. This concentration in Chryse Planitia is revealed by our dataset (Fig. S7) and again occupies a broader zone than reported by Stanzel et al. (2008). In our dataset southern Hellas Basin has a notable concentration of large dust devils between Ls 270–330°, despite the very sparse image coverage (Fig. S8). Previous orbital surveys only noted slight concentrations here, perhaps because the generally dusty atmosphere over this region produces only sparse spatio-temporal coverage. However, Hellas Basin does have a remarkable concentration of dust devil tracks (Balme et al., 2003) whose seasonal timing of appearance at Ls 260–360° matches with our data on active vortices.

The MOC surveys noted that particularly large dust devils were found on high elevation regions in the southern hemisphere and on the Tharsis plateau, which are not found in our data, yet we may be limited by the CTX image width and by the upper size limits of the detections of the CNN. The heavily asymmetric hemispheric distribution reported by Cantor et al. (2006) and Stanzel et al. (2008), with more dust devils found in the south compared to the north (excepting Amazonis), is supported by our new data. Including all data, 60% of detections are in the northern hemisphere (>10°N)– if the monitoring sites are excluded this falls to 30% with 10% being found near the equator (10°S-10°N) and 60% are found in the southern hemisphere (>10°S).

Several previous studies reported the presence of dust devils near the summit of Arsis Mons, but not on the other martian volcanos (Cantor et al., 2006; Cushing et al., 2005; Reiss et al., 2009). Our data contains two detections near the summit of Arsis Mons, three on the flank of Pavonis Mons, one on Olympus Mons and three more near the summit of Elysium Mons – demonstrating this is a rare, yet not impossible event. Because the atmospheric pressure is so low at these altitudes, their presence at these high altitudes is surprising (Fenton et al., 2016).

The dust devil frequency per season and per hemisphere reported in Fig. 11 is consistent with Cantor et al. (2006) and also broadly consistent with other regional orbital studies (e.g., Fenton and Lorenz, 2015) and in situ datasets (Greeley et al., 2010; Ordóñez-Etxeberria et al., 2020), plus those studies using dust devil tracks (Balme et al., 2003; Whelley and Greeley, 2008). The inter-annual variability in dust devil occurrence observed in our dataset is also consistent with these previous studies including the influence of dust storms, which seems to obscure it during the event. From orbital data alone, it is hard to conclude whether the surface is masked preventing detection of dust devils during dust storms or if there is no activity during dust storms. Ordóñez-Etxeberria et al.
(2020) report a complete stop of daytime pressure drops associated with convective vortices by Curiosity during the 100 sol duration of the 2018 global dust storm. In addition, they report increased activity just before the dust storm. Hence, this suggests there is reduced dust devil activity during global dust storms, rather than them being invisible from orbit.

### 4.3. Comparison to previous studies using in situ data

All in situ missions have indirectly detected convective vortices via pressure drops, but not all such events result in the lofting of sufficient dust to form dust devils. Near the Opportunity landing site we detect 12 small and one large dust devil (Fig. S9) which contrasts with rare in situ observations (Greeley et al., 2010; Lemmon et al., 2015). Our orbital data confirms the in situ camera observations InSight (Lorenz et al., 2021) that reveal a relative lack of visible dust devil vortices at this site (Fig. 10). Interestingly at the InSight lander location dust devil tracks are common (Perrin et al., 2020; Reiss and Lorenz, 2016), as opposed to at Opportunity where they are rare. We only detect four small dust devils in the Gusev Crater area where the MER Spirit rover landed (Fig. S11). In contrast, observations from Spirit reveal frequent dust devils with typical diameters of 10–20 m and more rarely up to nearly 300 m (Greeley et al., 2006, 2010). These typical sizes are near the limit for detectability of MOC-NA, MOC-WA, CTX or HRSC. In addition, the larger dust devils occur earlier in the day than imaging by CTX, hence both factors may explain the lack of detection in orbital surveys (Cantor et al., 2006; Greeley et al., 2010; Stanzel et al., 2008). Cameras on Curiosity reveal dust devils (Newman et al., 2019) yet we do not detect any in our dataset (Fig. S12). Although no systematic dimensional measurements have been collated and published yet, they appear to have scales on the order of tens of metres or less (Ordonez-Etxeberria et al., 2019), which is near the limit of detection for CTX unless the column is particularly tall and opaque (casting a large shadow). HiRISE images reveal no dust devil tracks are associated with pressure drops detected by Curiosity suggesting relatively little dust is lofted (Ordónez-Etxeberria et al., 2020), which could also hinder their detection from orbit.

In the area around Jezero Crater where the Perseverance rover landed we detected only two small dust devils, all of which were outside Jezero Crater (Fig. S13). Rates of detection by the pressure sensors exceed those at Curiosity and InSight (e.g., Hueso et al., 2023; Jackson, 2022), yet data from the imaging instruments has yet to be published. Estimates of size from meteorological sensors on Perseverance provide diameters ranging from 5 to 135 m with mean values of 20–30 m (Hueso et al., 2023; Toledo et al., 2023) that are below the detection threshold of the orbital survey. To first order our data seem consistent with these initial observations.

For the Phoenix lander area we find five large dust devils (Fig. S14). One of these was also reported in Reiss et al. (2014) and passed within 2.5 km of the lander, and other dust devils captured in the same CTX scene (of which three were not identified by our detector) may have been detected by the pressure sensors on sol 144 of the mission (Oct 16, 2008) (Ellehoj et al., 2010). Size constraints from 76 active dust devils imaged by the on-board cameras ranged from 20 to 200 m diameter, but were error-prone due to the flat plains having few reference points (Waller, 2011). These sizes are similar to those at the Spirit landing site where dust devil observations likewise lack corresponding orbital observations.

At the Zhurong landing site in Utopia Planitia, we detect only one large dust devil, distant from the lander, yet in situ data are as yet lacking from this mission regarding dust devils (Fig. S15). At the future ExoMars landing site in Oxia Planum, we find three small and three large dust devils in the region of the landing ellipse (Fig. S16) in northern summer. Favaro et al. (2021) reported two dust devils in this area of >150 m in diameter from summer HRSC images, and six more during summer (without size measurement) using pixel-differencing between two CTX images (which are not detectable without the difference image,

and thus were not identified in our study). This suggests our level of detection broadly matches their study and that Oxia Planum will likely experience a peak of dust devil activity in northern summer. We await the rover's arrival to provide in situ data on the size-frequency of dust devil occurrence to compare it with our detections from orbit.

### 4.4. Summary

In summary, our results are in broad agreement with previous studies on the spatio-temporal distribution of dust devils yet form a consistent database allowing us to bring together several observations that were reported from diverse datasets, including studies of dust devils from orbit, in situ and studies of their tracks.

## 5. Conclusions

We used a convolutional neutral network to detect 13 409 dust devil vortices in MRO Context camera images from the start of the mission in Mars Year 28 through Mars Year 35. The approach was validated using data from Amazonis Planitia where more than 3000 dust devils had been manually catalogued between MY 28 and 31 (based on data from Fenton and Lorenz, 2015) and a CNN Score of >0.5 was thereafter used for the global dataset. This global dataset initially returned 67 284 detections from which false positives were manually removed, leaving a total of 13 409 detections. Using a random sample of 862 dust devils from the catalogue we established a relationship between the bounding box size returned by the network and size of the dust devil cloud, allowing us to roughly separate “large” from “small” dust devil detections. The resulting database of dust devils.

* Confirmed previously reported hotspots for dust devil occurrence in Amazonis, Arcadia, eastern Sinus-Meridiani and Syria Planum and revealed that the spatial extent of these hotspots is much larger than the extent of the monitoring zones previously used.

* Confirmed the lack of orbital detection of dust devils at landing sites, despite in situ observations of dust devils. Hence, a better understanding of the correspondence between orbital and ground detections is needed to ascertain the cleaning power of solar panels by dust devils at given locations on Mars.

* Highlights trends hinted at by previous studies but never convincingly demonstrated, including the frequent occurrence of large dust devils in Hellas Planitia. We detected these dust devils despite the limited image coverage caused by the frequently dusty atmosphere, hence future imaging this region with CTX and other large-swath instruments should be encouraged (instrument teams normally avoid imaging Hellas when it is dusty).

* Identified two zonal concentrations of dust devils, and in particular large dust devils, at 40–70°N and 50–65°S around the local summer solstices. This trend is consistent with latitudinal dust devil track densities (Whelley and Greeley, 2008). These latitudinal maxima do not match current modelling of dust devil activity peaking at latitudes ±30°, suggesting that yet-unaccounted for factors strongly impact dust devil generation and growth on Mars.

Our results reveal that CTX is particularly suited to monitoring dust devils at a global scale, because images were obtained at a consistent local time, with a spatially extensive footprint, sampling all seasons over multiple Mars Years, and at an ideal intermediate spatial resolution. Thus this mid-resolution, high-coverage sensor is ideal for monitoring dust devils with a cloud size of ~50 m and greater, which have a significant contribution to the atmospheric dust load on Mars. Hence, we advocate for the continued development of such instruments on future missions.
CRediT authorship contribution statement

**Susan J. Conway:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.
**Valentin T. Bickel:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Lori K. Fenton:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization.
**Manish R. Patel:** Writing – review & editing, Funding acquisition.
**Helen C. Carson:** Writing – review & editing, Investigation.
**Evan Blanc:** Writing – review & editing, Investigation.
**Antoine Blouin:** Methodology, Investigation, Formal analysis.
**Justin Crevier:** Writing – review & editing, Investigation.
**Bao Nhi Nguyen:** Investigation.
**James A. Holmes:** Writing – review & editing, Investigation.
**Brian Jackson:** Writing – review & editing, Supervision, Funding acquisition.
**Lonneke Roelofs:** Investigation.

Data access

The reviewed dust devil catalogue is openly available on the Uni-
versity of Bern's open research data repository BORIS: https://doi.
org/10.48620/85395.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgements

We are thankful to the two anonymous reviewers, whose comments
improved the quality of this manuscript. SJC is supported for her Mars
related work by the French Space Agency CNES. VTB acknowledges a
Research Credit grant provided by Google Cloud. MRP and JAH
acknowledge UK Space Agency funding from grants ST/X006549/1, ST/
R003025/1, ST/V002295/1 and ST/W00268X/1. We gratefully
acknowledge the developers of the Zooniverse platform that enabled the
collection of dust devil size data in this study and the refined identifi-
cation of false positives. This publication uses data generated via the
Zooniverse.org platform, development of which is funded by generous
support, including a Global Impact Award from Google, and by a grant
from the Alfred P. Sloan Foundation.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.pss.2025.106072.

Data availability

The majority of the data produced by this study are in the suppl-
ment, and a link to the complete dataset is provided in the Data Access
section.

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