Streaks on martian slopes are dry

Domain: earth_science
Published: 2025-05-19
Editorial outcome: minor_revision

Abstract
Slope streaks are dark albedo features on martian slopes that form spontaneously and fade over years to decades. Along with seasonally recurring slope lineae, streak formation has been attributed to aqueous processes, implying the presence of transient yet substantial amounts of liquid water or brines on Mars’ surface, with important implications for present-day Mars’ habitability. Here, we use a deep learning-enabled approach to create the first consistent, global catalog of half a million individual slope streaks. We show that slope streaks modify less than 0.1% of the martian surface, but transport several global storm equivalents of dust per Mars year, potentially playing a major role in the martian dust cycle. Our global geostatistical analysis challenges wet streak formation models and instead supports dry streak formation, driven by seasonal dust delivery and energetic triggers like wind and meteoritic impacts. We further identify a qualitative, spatiotemporal relation between recurring slope lineae formation, seasonal dust deposition, and dust devil activity. Our findings suggest that modern Mars’ slopes do not commonly experience transient flows of water or brines, implying that streak bearing terrain can be explored without raising planetary protection concerns.

Introduction
First observed in Viking images in 19771,2, slope streak occurrence and formation have been subject to a continuous debate ever since. Dark slope streaks form sporadically and fade over years and decades (~3 to ~20 Mars years, MY)3,4, occasionally turning brighter than the surrounding regolith3,5. Slope streak populations have been observed to occur in dusty, low thermal inertia equatorial regions6,7. In stark contrast, recurring slope lineae (RSL), first discovered in 2011, are predominantly located in rocky, southern, high thermal inertia highlands8,9. The formation of streaks and RSL has been explained by dry and wet mechanisms, such as airfall- or thermal cycling-induced dust avalanching and impact-, quake-, dust devil-, or rockfall-induced mass/dust movements (dry)10–18, as well as groundwater spring-, frost-, and brine-induced flows (wet)7,9,19–23. Recently4, and24 identified an enhanced streak formation rate in northern autumn and winter (Ls ~140°–270°). Similarly, RSL have been observed to predominantly form in southern summer (Ls ~270°8). To date, it remains unclear whether slope streaks and RSL are different expressions of the same process, or fundamentally different features (e.g. refs. 5,25). If confirmed, a wet formation mechanism for streaks or RSL would indicate that present-day Mars’ hydrological cycle is substantially more pronounced than previously anticipated, with substantial implications on our understanding of martian weather, climate, surface evolution, and habitability. In addition, liquid water or brines on Mars’ surface would evoke serious concerns about planetary protection. Here, we fuse several decades’ worth of orbital data and use a global-scale deep learning- and data-driven approach to create the first global catalog of slope streaks, shedding new light on a fundamental, decades-old debate.

Results
Martian streak global geostatistics
We identify a total of 13,026 bright and 484,019 dark slope streaks with a wide range of morphologies in the pre-v01 version of the global MRO CTX (Mars Reconnaissance Orbiter Context Imager) mosaic26,27 (‘detected population’, pD), including previously unknown slope streak-bearing regions (Fig. 1; Supplementary Figs. S1, S2). There is no clear, universally applicable, quantitative way to separate ‘dark’ from ‘bright’ streaks, especially in CTX data, as the transition between dark and bright is continuous. Here, ‘dark’ streaks include recently-formed streaks that appear dark in CTX images, and ‘bright’ streaks refer to old, partially-faded streaks that appear bright in CTX images (Fig. 1A, B). The deep learning-driven detector is able to identify about 20% and 55% of all bright and dark streaks in a given CTX image, on average; at the same time, we estimate that about 6% of all detections are duplicates caused by the spatial overlap of some of the CTX images that make up the mosaic (Supplementary Fig. S3), suggesting that the actual number of bright and dark streaks observable on the surface of Mars are ~60,000 and ~830,000 (at the CTX-scale, ~6 m, ‘corrected population’, pC). We note that the deep learning-predicted detections contain an average of 2.33 individual streaks per detection box due to streak co-location and overlap (see Fig. 1A, B, Supplementary Fig. S1), indicating there might be up to ~140,000 and ~1,900,000 individual, CTX-scale bright and dark slope streaks on Mars, which is several factors more than was previously estimated (~800,000 dark streaks28). Applying the power law relation between the number of resolved streaks and image resolution as established by Bergonio et al.29, we estimate there might be ~14 times more streaks resolvable at the HiRISE-scale (High Resolution Imaging Science Experiment, ~0.3 m30), i.e., ~2,000,000 bright and ~28,000,000 dark streaks.

We identify streaks between ~40°N and ~20°S, with a mean latitude of 12.7°N and 16.7°N for bright and dark streaks, respectively, indicating that dark streaks are systematically shifted northwards relative to bright streaks. Notably, ~89% and ~90% of all bright and dark streaks are located north of the equator, and there are a factor ~37 more dark than bright slope streaks. Bright streak hotspots (>100 features per 5°x5° quadrangle) are located in Arabia Terra - Terra Sabaea, E Kasei Valles, Ius Chasma, and Noctis Labyrinthus, where the Arabia Terra– Terra Sabaea hotspot is the most pronounced. Dark streak hotspots (>5000 features per 5°×5° quadrangle) are located in Olympus Rupes-Lycus Sulci, Ceraunius Fossae, Arabia Terra– Terra Sabaea, Tartarus Montes, and NW Medusae Fossae – Marte Vallis, where the Olympus Rupes – Lycus Sulci hotspot is the most pronounced (holding ~68% of the global dark streak population). Only 15% of all 10×10° quadrangles across the surface host more bright than dark streaks (Fig. 1). Those regions might have experienced reduced streak formation rates in recent years; we note a particularly substantial percentage of bright streaks in Kasei Valles and Nili Fossae. The vast majority (~85%) of all bright streaks are located on Noachian terranes, only ~4% are located on Amazonian terranes. In turn, about ~50% of all dark streaks are located on Amazonian terranes, and only 31% on Noachian terranes (Fig. 2A).

Generally, bright and dark streaks are located at a) below-average topographic elevations, with average values of −126 m (bright) and −1126 m (dark); b) above-average slope angles with average values of 7° (bright) and 8.6° (dark); c) in above-average TES (Thermal Emission Spectrometer31) albedo terrain with average values of 0.27 (bright) and 0.28 (dark); and d) in above-average OMEGA (Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité32) nanophase ferric oxide terrain with average values of 1.02 (bright and dark) (Figs. 2B, 3A).

Globally, we observe a weak power law relation between the density of slope streaks and the mean slope angle (Supplementary Fig. S4). Dark slope streaks do not appear to feature a preferred slope orientation, but equator-facing slopes host slightly more dark streaks (global difference to pole-facing slopes is 4.1%, i.e., ~20,000 streaks, Fig. 2B); bright streaks feature a distinct west-facing orientation. We do not observe any substantial differences when considering northern and southern hemisphere populations separately.

Bright and dark streaks feature a strong correlation between estimated size, MOLA elevation (Mars Orbiter Laser Altimeter33), MCD diurnal surface temperature amplitude (Mars Climate Database34), albedo, and nanophase ferric iron: larger streaks appear to feature lower MOLA elevation and higher diurnal temperature amplitude, albedo, and nanophase ferric iron values (Supplementary Fig. S5). On average, bright streaks are larger than dark streaks, with an average estimated size of ~775 vs ~609 meters. We do not observe any general, longitudinal or latitudinal variations in streak size. Streak length and width are weakly linearly related; assuming a rectangular streak shape, we estimate a mean surface area for bright and dark streaks of ~0.047 and 0.034 km2, respectively. The area-frequency-distribution (AFD) of estimated streak area follows a power law with a negative slope of 3.9 and 3.8, respectively. Interestingly, an AFD derived for RSL presents a negative exponent of 1.1 (Fig. 2B). There is no correlation between streak size and the age of the host terrane (Supplementary Fig. S6).

Using the mean number of streaks per detection (2.33), we estimate that one bright or dark slope streak detection modifies a surface area of ~0.1 km2, on average. This indicates that the detected slope streaks modify an area of 49,705–103,500 km2 globally, i.e., 0.03–0.07% of the martian surface (for pD and pC, respectively). Considering a streak survival time of 3–20 MY (where streaks are observed3,4), the global surface modification rate would be 15,568 to 2485 km2/MY and 34,500 to 5175 km2/MY, i.e., 0.01–0.002% and 0.02–0.004% of the surface per MY (for pD and pC, respectively). Considering a streak thickness of ~1 m (i.e., the maximum value derived by14,15, an average streak detection would modify a volume of ~100,000 m3 or ~0.0001 km3, which would result in a total modified volume of ~50 to ~100 km3 (for pD and pC, respectively). Considering a dust bulk density of ~2500 kg/m3 or ~2.5×109 t/km335, the total modified mass would account for ~1.3×1011 to ~2.5×1011 t, i.e., ~4.2×1010 to ~6.3×109 t/MY and ~8.3×1010 to ~1.3×1010 t/MY (for pD and pC, respectively). References36,37 report that around 4.0×108 to 2.9×109 t/MY of dust is exchanged between the martian surface and atmosphere, i.e., between ~1 to ~46% and ~0.5 to ~22% of the dust mass modified by streaks (for pD and pC, respectively). Reference38 reports a total dust mass of ~4.3×108 t lifted during a global dust storm observed in 1977, which is ~1 to ~7% and ~0.5 to ~3% of the estimated mass modified by slope streaks per year (for pD and pC, respectively). These estimates indicate that slope streaks move several dust-equivalents of a global dust storm every MY. The injection of dust into the atmosphere by slope streaks has not been observed yet, but would make slope streaks an important contributor to the martian dust cycle and climate, potentially accounting for a substantial fraction of the surface-atmosphere dust exchange budget.

Endo- and exogenic drivers of streak formation
We observe that dark streaks and date-constrained impact events39 are partially co-located in dusty regions (Fig. 4A). To suppress the observational bias related to the detection of new impacts in dusty regions (e.g. refs. 39,40) we perform a statistical analysis of streak and new impact locations in the dust-rich Arabia Terra, Elysium Planitia, and Tharsis regions, which host both streaks and new impacts. We find that dark streaks tend to be located slightly closer to real impact sites than to randomly placed (synthetic) impact sites in Arabia Terra (statistically significant, P=0.038) and Elysium Planitia (not significant, P=0.231), on average, but not in Tharsis. In Arabia and Elysium, in circles with radii of 0.5, 2, and 5°, there are a factor ~1.5, ~1.3, ~1.1, as well as ~1.9, ~1.6, and ~1.3 more streaks around new impacts vs synthetic impacts, on average (Fig. 4B, see Supplementary Table S1), decreasing with increasing distance from a given impact. Beyond Arabia Terra and Elysium, we do not see a global relation between the spatial density of streaks and new impacts (Supplementary Fig. S7).

Some of the global streak hotspots are spatially co-located with InSight-recorded, tecto-genetic broadband (BB) and low-frequency (LF) events with back azimuth (BAZ)41–44, but a) not all streak hotspots feature a co-located epicenter and b) not each epicenter features a streak hotspot (Fig. 4A). We perform a statistical analysis of the two global streak hotspots with associated seismic BB or LF event(s) (Elysium Planitia and Tharsis). Dark streaks do not tend to be located closer to real quake epicenters than to randomly placed (synthetic) epicenters (no statistically significant difference, P=0.167). In Elysium, in circles with radii of 0.5, 2, and 5°, there are a factor ~1.4, ~1,4, and ~1.0 less streaks around real epicenters vs synthetic epicenters, on average (Fig. 4B, see Supplementary Table S1). We note that a substantial fraction of the CTX images used by the global mosaic were acquired prior to the InSight mission, rendering our analysis not ultimately conclusive. We do not observe a general, global relation between the distribution of streaks and the geomorphic expressions of past tectonic activity, such as graben and fossae, despite those features’ steep slopes, but identify noteworthy exceptions such as Ceraunius, Tractus, Noctis, Ulysses, Elysium, and Labeatis Fossae (Fig. 4A). Similarly, we do not observe a general, global relation between rockfall locations45 and slope streak distribution other than a small number of co-located features in the Cerberus Fossae and Ius Chasma areas (Fig. 5).

Slope streaks tend to be located in regions with above-average MCD wind speed, particularly in the morning (9 AM, statistically significant, P<2.2×10−233, Fig. 6). We observe that slope streaks and active dust devils46 are strongly inversely correlated, i.e., regions with dust devil activity are nearly entirely devoid of slope streaks. Notably, regions of active dust devil occurrence appear to coincide with several of the globally distinct boundaries of slope streak distribution, such as located in NW Lycus Sulci, Syria Planum, N Phlegra Dorsa, E Terra Sabaea, and S Arabia Terra (Fig. 6A). Slope streaks are strongly associated with above-average MCD diurnal temperature amplitude regions, in particularly pronounced in northern spring, summer, and autumn (Fig. 7). Slope streaks tend to be located in regions with above-average MCD atmosphere-to-surface heat flux during the night (12 and 3 AM), where the correlation disappears in the morning (6 AM) (Fig. 3B).

We observe that slope streaks appear to be located in regions with slightly above-average WEH (water equivalent hydrogen, statistically significant, P=~0), H2O, and H, particularly bright slope streaks. Notably, not all high-H2O/H/WEH regions feature slope streaks and many streak hotspots are located in low H2O/H/WEH regions (Fig. 8; e.g., in Elysium, 10°N 170°E, and E Tharsis, 20°N 110°W). Slope streak locations feature slightly enhanced water vapor column values in northern summer and autumn (Fig. 8B). The vast majority/parts of the slope streak population pass through the ~273 to ~283 K temperature band that theoretically allows for transient liquid surface water, in northern summer and southern summer, respectively (northern summer is particularly pronounced, Fig. 7B). Streak locations do not show any substantial overlap with the valley networks mapped by Hynek et al.47, expect for a few locations in Naktong, Deva, Minio, Padus, and Sabis Vallis (Fig. 5A). We note that dark streaks are located in regions that see above-average dust deposition rates in southern summer (statistically significant, P=~0), but below-average rates for the rest of the year (Fig. 3B).

Methods
Few-shot learning-driven slope streak mapping
We select 40 globally-distributed MRO (Mars Reconnaissance Orbiter) CTX (Context Imager) images with a pixel scale of ~6 m64 and crop them into ~1000×1000 pixel patches. With those patches we create a training dataset by labeling a total of 1007 and 147 dark and bright slope streaks, following previously established workflows65–68. Here, a label represents a rectangular box drawn by a human expert around a slope streak. We label an additional 35 dark and 6 bright slope streaks for model testing and verification (~4% of the training set size; remains unseen during training). During labeling, we carefully separate bright and dark slope streaks following the definition provided in the main text and showcased in Fig. 1. We purposely minimize the size of the testing dataset, to maximize the data available for model training. Here, the usage of the term ‘few-shot learning’ accounts for the fact that the number of slope streak labels available for training and testing is lower than in traditional deep learning which commonly uses tens of thousands of labels. Importantly, past studies have demonstrated that a few-shot learning approach can successfully map geomorphic and atmospheric features across various planetary bodies and datasets45,46,69–73. A list of all used CTX images in shown in Supplementary Table S2 and their illumination and imaging geometries are listed in Supplementary Table S3.

We use the training dataset to fine-tune a COCO-pretrained (‘Common Objects in COntext’), off-the-shelf PyTorch 1.7 implementation of the YOLOv5x convolutional neural network (CNN) provided by ultralytics via GitHub (https://github.com/ultralytics/yolov5). YOLOv5 is a well-established, verified, cutting-edge object detection framework that has been utilized for a wide range of scientific applications, such as food science, robotics, medical image processing, and planetary science (e.g. refs. 71,73–76). YOLOv5 represents the best compromise between state-of-the-art performance and reliability, and has been shown to substantially outperform (performance and speed) earlier machine learning algorithms such as based on AdaBoost, Histogram of Oriented Gradients, and Region-based CNNs (e.g. refs. 77–79) as were used by earlier studies that reported on the automated detection of slope streaks80.

We apply substantial label augmentation during training to prevent model overfitting and minimize any negative impact of the relatively small number of labels, as is common practice in computer vision and few shot learning, utilizing label rotation, up-down flipping, shearing, up- and down-scaling, and brightness and contrast modifications. We do not apply left-right flipping to preserve a proper representation of the illumination conditions governed by MRO’s sun-synchronous orbit in the training data. The training of the CNN required ~15 min on one single NVIDIA RTX 3090 GPU (Graphical Processing Unit).

We evaluate the performance of the trained detector using the testing dataset and the three most common metrics used for object detector evaluation: recall (i.e., the number of detected streaks, e.g. 8/10), precision (i.e., the number of correctly detected streaks, e.g. 9/10), and average precision (AP, relation of recall and precision over a wide range of model confidence scores, i.e., the area under the precision-recall curve, which summarizes the performance of a detector in one single number, Fig. 9C). The CNN assigns a confidence score to each detection (0, low to 1, high), representing its confidence in the assigned detection and classification; higher confidence score populations usually feature lower recall but higher precision. At confidence 0.8, the trained slope streak detector achieves full precision (100%), a recall of 20% and 55%, as well as 0.98 and 0.50 AP (0.74 mAP, mean average precision for all classes) for bright and dark streaks in the test set, respectively (Fig. 9C). This indicates that the detector can be expected to make none to very few mistakes, while being able to (consistently) identify about 20 and 55% of all bright and dark slope streaks observed by CTX, at a confidence score of 0.8 and higher.

At a score of 0.6 our detector outperforms (for dark streaks only, recall ~85%) the performance as reported by earlier studies that used a combination of feature selection and machine learning (binary) classification techniques and HiRISE images (ref. 80, recall ~79%), at a continuously high level of precision (~94%). However, given the substantial number of expected slope streak candidates we deliberately choose a confidence score of 0.8 for this study, to maximize the precision of the detector and minimize the amount of time required for the subsequent review efforts, as described below.

We deploy the trained detector in a customized image download and processing pipeline originally established by Bickel et al.69 and further refined by Bickel et al.70,72, and Conway et al.46. The pipeline successively downloads all of the 86,546 CTX images contained in the post-beta27, pre-v01 version of the global CTX mosaic (pers. comm. J. Dickson), which was later officially published in ref. 26, at https://image.mars.asu.edu/stream/, and tiles them into 1000×1000 pixel patches (Fig. 9A). At the time of data processing, the final version of the mosaic was not yet available, which required us to process the individual CTX images that made up the pre-v01 version of the mosaic instead. All patches are scanned by the streak detector, the geographic location of all detections are reconstructed using the image’s metadata, and all detections as well as their associated metadata are stored (including a size estimate, i.e., the diameter of the bounding box), including so-called ‘thumbnails’, i.e., crop-outs of all CNN-predicted bounding boxes that are used for the subsequent expert review (see Fig. 9B, Supplementary Fig. S1). We use YOLOv5’s default Non-Maximum-Suppression (NMS) algorithm to remove duplicate predictions in each patch, following computer vision best practice. Overall, the processing of the entire dataset requires ~2 months of processing time using one single NVIDIA RTX 3090 located in a regular desktop computer.

Due to the potential for geo-location and co-registration inaccuracies as well as partial overlap of the CTX images that make up the pre-v01 version of the global CTX mosaic (pers. comm. J. Dickson) there is a small chance that slope streaks located along the seams of individual images are detected multiple times (‘duplicates’). However, a case study in the Tharsis region suggests that the number of potential duplicate detections in the overall dataset is expected to be small, around 6% of the total streak count (Supplementary Fig. S3). The slight location inaccuracies paired with the large number of slope streak detections impedes an automated and manual detection and removal of duplicates, which is why they are not specifically identified and removed. We note that duplicate detections are evenly distributed across the mosaic and globe and, thus, do not systematically affect the geostatistical science analysis.

After processing, the detector identified 661,144 slope streak candidates (40,855 bright, 620,289 dark) at a confidence score of 0.8 and higher. At confidence 0.7, 0.6, and 0.5 the detector identified 1,494,416, 2,015,709, and 2,463,542 slope streak candidates, respectively.

Candidate review
We note a number of false detections among the bright and dark slope streak detection candidates, which closely resemble the visual appearance of streaks, such as wind streaks, linear shadowed topography, dunes, and mountain ridges (Supplementary Fig. S15). Past experience with object detectors being applied to very large and heterogeneous orbital image datasets has shown that the test set performance is not always fully representative of the ‘real world’ performance of the detector in the full dataset, due to the inherently limited representability of the strictly size-constrained training and testing datasets as well as unknown/unexpected anomalies such as related to imaging artefacts, weather-related phenomena (clouds, etc.), and a-priori unknown look-alikes (see, e.g. refs. 45,70–72). The increased number of false detections as achieved by the bright streak detector (Fig. 9C) can likely be attributed to a) the smaller training dataset, which in turn is caused by the relative scarcity of bright streaks (as noted by other authors, such as, e.g. refs. 3,5), and b) the more subtle radiometric contrast between bright streaks and the surrounding slope (compared to dark streaks). The presence of false positives in the output of both detectors underline the importance of the manual review of all candidates and the removal of all false positives.

In order to remove all obvious false detections and ensure the integrity of the scientific analysis, we manually review all CNN-identified candidates. The review consists of a human expert looking at every single slope streak candidate thumbnail, one-by-one. Due to the overwhelming amount of candidates at lower scores (e.g., >1 million candidates are score 0.7) we decide to only review all candidates with score 0.8 or higher, i.e., all very high confidence detections (n=661,144). Overall, the manual review of all candidates required ~two weeks, achieving a reviewing rate of ~183 detections/minute or ~3 detections/second, assuming an 8-hour work day and 5 work days per week. We identify a total of 13,026 bright and 484,019 dark slope streaks, indicating a detector precision of ~32% (bright) and ~78% (dark) (Fig. 9C). The test set precision at score 0.8 is 100% for both types, indicating there is a drop of performance by 68% for bright and by 22% for dark streaks, as was expected based on previous experience. After expert review, we conclude the validation of the dataset by examining all remaining slope streak detections as plotted on the 26,27 global CTX mosaic, considering all detections in their geomorphic context and removing any remaining outliers. All analyses and results (as well as the associated dataset) utilize the fully reviewed streak catalog.

Verification of the reviewed mapping results
The quality and reliability of the produced global slope streak dataset rely on the a) combined quality of the used CTX images, b) performance of the slope streak detector, and c) quality of the manual expert review. We do not find any direct or indirect evidence for any negative effect of the scanned CTX images on the mapping outputs, such as potentially caused by cloud cover, shadowing, haze, and data artifacts. We point out that 26,27 carefully selected the images that make up the global mosaic to minimize any such negative impacts. The detector was carefully trained and its performance thoroughly tested and assessed using state-of-the-art approaches and heterogeneous, geographically-distributed training data. We do not expect any observational bias for the dark slope streak class; the very wide range of slope streak morphologies identified by the detector (Supplementary Fig. S1) provides evidence for a robust and capable detector.

Notably, the preferably more equatorward distribution and W-facing orientation of bright slope streaks is likely to be attributed to a bright slope streak detector that is more successful in retrieving bright streaks on well-illuminated, Sun-facing slopes, given the very faint appearance of bright slope streaks (Fig. 1A, C) and MRO’s sun-synchronous orbit (imaging takes place in the early local afternoon). Potentially, the higher mean length as reported for bright streaks could be attributed to the lower performance of the bright streak detector (compared to the performance of the dark streak detector) resulting in systematically less precisely placed bounding boxes, leading to larger size estimates. We point out that the evenly-distributed orientation of dark streaks (Fig. 2B) implies that there is no general, CTX imaging-related bias regarding slope shadowing, as further verified by qualitative testing – and as expected based on the careful selection of mosaic images performed by Dickson et al.26,27.

We expect the manual review to capture and remove the overwhelming majority of all false detections contained in the original candidate dataset. In confirmation of that expectation, our derived streak distribution maps agree exceedingly well with previously published maps of slope-bearing terrain (e.g., refs. 3,5, Supplementary Fig. S2), which further increases our confidence in the derived maps and relations. Notably, we identify a number of previously unknown streak hotspots, such as located in Xanthe Terra, Lunae Planum, E Kasei Valles, and Syrtis Major (Fig. 1, Supplementary Fig. S2). We note that our mapping approach is relying on a stitched mosaic consisting of CTX images taken several years apart (2006–202026,27), and at different times/seasons; as a result, our slope streak catalog does not represent one snapshot in time, but a wide time-range. Much of the remaining ambiguity could be addressed with globally-distributed information about slope streak and RSL formation times.

Streak number and size estimate correction
The CNN reports a size estimate for every slope streak detection, representing the diameter of the CNN-predicted bounding box, which closely encompasses the streaks, i.e., represents a proxy for streak length (see Supplementary Fig. S1). Using the spatial resolution of the associated CTX image, we convert the diameter from pixel to meter. Following a previously established methodology65,66 and in order to improve the accuracy of the relation between bounding box diameter and actual streak length, we manually measure the actual length of a randomly drawn subsample of 1000 slope streaks and relate it to the respective bounding box diameter (linear relation), deriving the following relation:

length = 0:8774 * length_CNN - 61:503 (1)

We use this relation and correct all ~500,000 streak length estimates; all scientific analyses use the corrected length estimate. As the size of each bounding box is dominated by the largest contained streak, this relation might not be entirely representative for bounding boxes that include multiple streaks with distinct streak length differences. We further establish a linear relation between the length of streaks and their width, measured at their morphological center, using the same randomly drawn subsample of 1000 slope streaks:

width = 0:0461 * length_actual + 19:038 (2)

In addition, we use the subsample to quantify the average number of individual slope streaks contained in one single detection (see Supplementary Fig. S1), calculating an average of 2.33 streaks/detection. Multiple, spatially co-located slope streaks are not always easily discernible, such as visualized by Fig. 1A, B, Supplementary Fig. S8. We note that the absolute counts of individual slope streaks as derived in this work might under- or over-estimate the actual number of slope streaks in a given region.

Area estimate
We use the corrected streak length and the relation between length and width to produce estimates of streak area for the entire dataset, assuming a simplified rectangular streak shape:

area = length_actual * width (3)

For RSL, we manually measure a subsample of 100 semi-randomly selected, geographically-distributed features (taken from, e.g. refs. 8,51) and estimate their surface area using Eq. (3).

Geostatistical analysis & auxiliary dataset limitations
We compare our slope streak catalog to a large number of auxiliary spatial raster and vector datasets using Quantum GIS (QGIS). Specifically, we use TES surface albedo31, MOLA topography81, RSL locations mapped in MRO HiRISE (such as by McEwen et al. 8,51), modeled environmental outputs produced by the Mars Climate Database (MCD34, assuming a climatology average solar scenario), date-constrained (new) impacts39, InSight tecto-genetic broadband (BB) and low-frequency (LF) events with back azimuth (BAZ41,42), the most recent version of the martian geologic map82, river valleys47, rockfall locations45, OMEGA nanophase ferric iron83, TGO FREND water-equivalent-hydrogen (WEH84), GRS H2O and H85, and dust devil locations (ref. 46, covering MY28 to MY36). We use the MOLA topography data to create two derived topography products, slope angle and slope aspect, using built-in QGIS algorithms. We quantitatively define the significance of differences between different populations and datasets using a two-sample T-test with a default P-criterion of 0.05.

Importantly, many of the used global auxiliary datasets feature spatial resolutions several factors below the size of CTX-detected slope streaks and/or depend on various assumptions and models, such as the MCD-derived outputs. For example, the global, km-scale, MCD-derived wind speed maps do not fully capture the complex influence of small-scale topography on wind speed and direction. We underline the importance of local, site-specific studies to verify the global-scale observations and interpretations made by this work.

Seismic & impact event distance analysis
Using a methodology developed by Bickel et al.70,86, we quantify the statistical relation between streak and date-constrained impact/marsquake epicenter locations (Fig. 10). First (‘Criterion 1’), we calculate the shortest distance between all streaks and a) all real impacts/quakes and b) the same number of randomly placed (synthetic) points (in the same area). Second (‘Criterion 2’), we count the number of streaks in 0.5, 2, and 5° radii around a) all real impacts/quakes and b) the same number of randomly placed (synthetic) points (in the same area). If there was a physical relation between impacts/quakes and streak occurrence, the average distance to streak locations should be smaller, and the number of streak should increase in the proximity of real impacts/quakes (Fig. 10).

We note that this method is strongly affected by the quality and spatiotemporal availability of the impact and marsquake data; ref. 39 acknowledge that their impact catalog is likely incomplete, particularly in the southern highlands; in addition, a CTX image used in the mosaic might have been taken prior to an impact even at the same location. However, there is a substantial number of date constrained impacts available for a statistical analysis (n=1203) which have been mapped over the same time period as the images for the global CTX mosaic have been acquired, indicating there is a robust statistical foundation for our analyses.

In turn, a substantial fraction of the CTX images in the global mosaic were acquired prior to the InSight mission (late 2018 to late 2022), making a meaningful comparison with the seismic data challenging. Tecto-genetic quakes are likely to periodically re-occur in the same regions, but their periods are unknown and might exceed the survival time of slope streaks. In addition, InSight’s records are occasionally noisy, i.e., might not include all tectonic events in the monitoring period, do not cover all of Mars’ surface (core shadow), and suffer from substantial localization uncertainties (e.g. ref. 87). Overall, the geostatistical comparison of streak and marsquake locations is not expected to be entirely conclusive. In order to compensate the data limitations, we also compare streak locations with the distribution of permanent geomorphic expressions of geologically recent tectonic activity, such as graben and fossae.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary\nMachine learning methods were applied to a global CTX image mosaic to develop an inventory of slope streak occurrence.\nStatistics were gathered to count streak occurrences, size, area, and distribution. These data were correlated with geologic\nunits and global geophysical datasets such as elevation, minerology, surface albedo, thermal properties, and atmospheric\nproperties. The new catalog was compared to previous regional or local catalogs to validate the machine learning results.\nThe result of having a global inventory of slope streak features, including RSL, provides a powerful dataset for a more\ncomplete analysis of the surface and atmospheric characteristics that influence slope streak formation.\nThis study provides the first global inventory of slope streak features for Mars, using rigorous machine learning techniques to\nfully take advantage of the many years of MRO observations from CTX and HiRISE. This study's results extend previous\nstudies that examined local or regional populations of slope streaks and RSL. The main result, that slope streaks and RSL\nare the result of dry surface-atmosphere interactions, agrees with several previous works, and provides strong evidence\nagainst aqueous processes.\nNew findings reported here include the identification of previously unidentified bright streak \"clusters\" in east Kasei Valles,\nnorthwest Arabia Terra, Syrtis Major, and dark streaks in Lunae Planum.\nThe accuracy of the deep learning algorithm is carefully quantified by the authors, with uncertainties appropriately noted\nconcerning image resolution, false positives and false negatives. The uncertainties in correlating the slope streak features\nwith other global datasets at lower spatial and temporal resolution are also considered.\nThe figures are clear and well-designed and succeed in illustrating the results of the global analyses. Some corrections are\nnoted below.\nThe methodology and data used in the analyses are described in detail. It would be beneficial to the community and for\nreproducibility for the authors to provide their database of slope streak identifications as a linked dataset to this publication.\nAs a spatial dataset, this work provides strong, new analyses of streak formation. Because it was conducted on a mosaic of\nCTX images acquired at many different seasons and Mars years without distinction, convincing conclusions about temporal\nphenomena are harder to make.\nMinor corrections/revisions suggested:\nFigure 1.F) Should be 10°x10° quadrangles? Ratio numbers are hard to read. Perhaps indicate the value ranges on the\nlegend instead of writing them within the grid squares. Do keep the \"bright >> dark\" etc. annotation on the legend. It is very\nhelpful for interpreting the map.\nLines 65–69: Did the authors test the assumption that there should be \"14x more streaks resolvable at the HiRISE-scale\"?\nThat may be true for features that approach the limits of HiRISE resolution, such as RSL, but is not likely to be true for the\nlarger features that are clearly resolved at CTX scale, such as those in Figure S1. However, it may be true for some cases\nsuch as the images shown in Figure S7.\nLines 127–131 (and in the Abstract) seem to imply that estimated volumes of dust mobilized by slope streaks are a potential\nsource for the global atmospheric dust cycle. As noted by the authors in the concluding sentence of this paragraph, this\nassumption is not supported by observational evidence. If the dust were being mobilized into the atmosphere, as suggested,\nslope streaks would probably not retain topographic expression (note the assumed streak thickness of 1 m, and caveat\nnoted in lines 338–342,) unlike other mass wasting events that have been observed by HiRISE (e.g., NPLD scarp\navalanches) which leave no topographic signature but do display dust dispersing into the atmosphere. Suggest withdrawing\nthis claim (that X amount of dust is being mobilized back into the atmosphere) and just noting the estimated mass movement\nof dust downslope.\nThe point made in the Methods section (lines 485–488) about the lack of temporal information in the survey should be\nmoved to the Discussion section. The authors did conduct a few case studies showing positive and negative streak\nformation post-impact in the vicinity of known impacts (Figs. S7 and S8). Although this case-by-case analysis is not the main\npoint of the paper, it is a much stronger analysis than comparing the entire CTX dataset to the known recent impacts and\nseismic events, regardless of timing (as noted in lines 186–188). The correlation to impacts is not meaningful if the timing of\nthe image acquisition (before or after the possible date of impact) is not taken into consideration.\nFigure 2.B) Degree symbol on Bright streak values for TES albedo (0.27) and OMEGA nanophase ferric oxide (1.02) are not\nnecessary? Units measured are not degrees.\nFigure 5. White points for bright slope streaks and white lines for valley networks are confusing. Consider using a different\ncolor (light blue?) to indicate valley networks.\nReferences\n- indicate (a) and (b) for references from the same author and same year (e.g., Dundas 2020a and Dundas 2020b)\nChojnacki et al. 2016 Nature Geosciences https://doi.org/10.1002/2015JE004991 should be referenced in the Introduction.\nLine 356: Suggest adding citation to Dundas et al., 2017 (Nature Geosci.) https://doi.org/10.1038/s41561-017-0012-5.", "reviewer_3": "Review of manuscript: Streaks on Martian Slopes are Dry\nBy V. T. Bickel & A. Valantinas\nGeneral Overview\nSlope streaks (SS) are narrow, elongated, fan-shaped features which range from about a few meters to several kilometers in\nlength with a width in the range of tens of meters to 1m following the downslope topography of the terrain. They display in\nmost cases an increasing width along the downslope direction. They appear as darker (DSS) and brighter (BSS) varieties.\nSlope streaks can appear anytime in the Mars year in the equatorial and subequatorial regions of Mars. Slope streaks are\ngenerally thought to form in single events, appearing to be formed within a short temporal span. They gradually fade over\ndecades.\nSS are of special interest because they represent one of the most widespread and largest active geological processes on\nthe surface of present-day Mars. The origin of slope streaks and their formation mechanism is to-date not yet understood and\nhighly debated.\nA \"wet\" mechanism suggests that a fluid, e.g. liquid water, could be responsible for their creation, possibly as a result of\ngroundwater springs, melting surface ice, or the formation of brines. In the opposing view, a \"dry\" mechanism suggests that\n“dust avalanches” are responsible. These, in turn, could be caused by air fall deposits, subsurface melting, or localized\ndisturbances – ranging from rockfalls, meteorite impacts to tectonic activity. Both of these explanations have limitations when\nit comes to explaining observed slope streaks.\nIn view of the ongoing debate on the origin of DSS and BSS any research which can shed additional light on slope streaks\nis of interest.\nRecurring slope lineae (RSL) are narrow, downslope-trending features, darker than their surroundings, that gradually extend\nprimarily in warm seasons, fade and recur annually (Dundas et al., 2017).\nDistinguishing SS from RSL, dunes, and shadowed topographical features, to name only a few geomorphic features which\ncan look very similar, can be an extremely difficult task, especially when only images are available which were taken under\nvery different observational conditions.\nThe authors (Bickel and Valantinas) use grayscale images for their investigation generated by the Context Camera (CTX) with a resolution of up to ca. 6 m/px onboard NASA’s Mars Reconnaissance Orbiter (operating from Nov. 2006 until today), and state in their paper that they could use a deep learning-driven detector which is able to localize about 20 % of all BSS and 55 % of all DSS in a given CTX image. Based on this, they conclude that the actual number of bright and dark streaks observable at the CTX-scale on the surface of Mars are ~65,000 and ~970,000. Finding that their detection boxes contain an average of 2.33 streaks due to overlap and collocation they claim that the total number of BSS and DSS on Mars at the CTX resolution should amount to 150’000 and 2’260’000 BSS and DSS, respectively. Using their global gestatistical findings,\nthey furthermore claim that their research\n- shows that slope streaks modify less than 0.1 % of the Martian surface, but transport several global storm equivalents of\ndust per Mars year, potentially playing a major role in the surface-to-atmosphere dust exchange.\n- challenges “wet streak formation models” and present compelling evidence for “dry streak formation”, driven by seasonal\ndust delivery and energetic processes like wind and meteoritic impacts.\n- demonstrates that they have identified a global, spatiotemporal relation between recurring slope lineae formation, seasonal\ndust deposition, and dust devil activity.\n- suggests that modern Mars’ slopes do not commonly experience transient flows of water or brines.\nData used in investigation:\n86,546 CTX images from the 86,571 separate CTX images used to produce the CTX mosaic (V1) were processed to locate\ndark and bright streaks in these individual images. The images used when downloaded are already individually ISIS\nprocessed.\nMethod Used:\nDetection of dark and bright is achieved by using a downloadable Yolov5x convolutional neural network (CNN) in the\nlatitude range from -40 degree N to -20 degree S.\nIndividual CTX image files are tiled into 1000x1000 pixel patches and fed into the YOLOX network. Upon detection, the\ngeographic location of the detection is reconstructed using the image’s metadata.\nTraining is done by using 40 MRO CTX images. Using these images a training dataset of 1007 dark and 147 bright slope\nstreaks is produced.\nDetections, as well as their associated metadata, are stored (the diameter of the bounding box, and ‘thumbnails’).\nRelevance of research\nProducing an unbiased catalogue of dark and bright SS, identified on the surface of Mars, which contains the exact center\ncoordinates and the surface area coverage of the streaks, together with the corresponding thumbnails and the CTX file\nidentifier on which the SS were found, would be a highly desirable endeavor, being useful for various research purposes in\nview of the ongoing debate on the geologic and climate-related settings under which these SS occur.\nEvaluation of Paper\nFormal appearance of paper\nThe manuscript by Bickel & Valantinas is well written, easy to read and contains many graphically appealing illustrations. It\ncontains an extended list of references.\nAppraisal of submitted work\nThe majority of the conclusions of the Bickel and Valantinas paper are based on their global geostatistical detection of\nMartian SS. The precise information of the located SSs has been collected in a BSS and DSS catalogue. As these\ncatalogues are then used for the statistical analysis on which the argumentation for their findings is based, the reviewer has\nfocused and tried to verify the reproducibility, accuracy and unbiased collection of the catalogues’ SS information from the\ndata pool utilized.\nThe production of unbiased catalogues of DSS and BSS requires one to have images available on which the SS can be\ndetected and that their location can accurately be reconstructed.\nPrior work by Wang, I. et al., 2017 (reference not mentioned in manuscript) has demonstrated that an automatic detection\nand an accurate size measurement of Martian dark SS is possible.\nTo generate unbiased catalogues of DSS and BSS for a given surface region requires one to have an image mosaic\navailable which was constructed with the required resolution and which has no areas on which the detection method cannot\nbe applied.\nThe construction of surface mosaics for Mars is for different reasons a nontrivial complex task. The MRO satellite orbits Mars\nin a sun‐synchronous orbit, such that illumination conditions between overlapping CTX images should be comparable.\nHowever, due to the existence of the Martian atmosphere with its changing clouds and dust cycles, images can have a very\ndifferent quality, so that the ability to recognize surface features on images mapping the same surface area can be very\ndifferent.\nTo circumvent the problem of choosing the best images for the SS detection and to avoid the problems involved in\nconstructing regional mosaics, Bickel and Valantinas choose only the images which were used in the “Dickson Mosaic”\nwhich was generated using the CTX images at a resolution of ≈5 m. Using such a mosaic would, in principle, ensure that SS\nare not missed or that individual SS are not counted multiple times. However, there are various issues which complicate\nsuch an approach.\nAs the manuscript describes, they download all the 86,546 CTX used for the “Dickson Mosaic”. It is not clear to the reader\nhow they can avoid that the detector sees multiple times overlapping image regions, thereby increasing the probability to\nsee the same SS several times. To do this correctly they would have to use the shape information from the 4x4 degree tiles\nof the “Dickson Mosaic” to guarantee an equal scan probability of each surface patch of the image (this information is in the\nDickson Mosaic, in principle, fully accessible). The reviewer has Investigated the given thumbnails (although not complete in\nthe requested areas) and identified from the names of the images on which the streaks were detected that there must have\nbeen an overlap of images. There is no explanation in the Method section how they avoided the overlap using the available\nshape files (if they were used at all). The thumbnail also reveals photometric issues which are not discussed but could be\nresponsible for some false detection that the authors notice.\nIf the idea of the authors was to eliminate multiple identified SS after they were detected, then this would require that multiple\ndetected SS would need to have sufficiently accurate coordinates on the different images on which they were detected. Due\nto the fact that the “Dickson Mosaic” is not using a controlled network approach one has to expect shifted coordinate\nlocations for the same SSs found on different images. As the CNN is operating on the 1000x1000 patches of a single CTX\nimage, the manuscript lacks a discussion of the above-mentioned problematic and how it can be solved.\nThe reviewer can agree with the manuscripts’ authors that the YOLO family of networks is one of many CNNs that offers a\ngood balance of speed and accuracy, enabling the rapid and reliable identification of objects in images. One should,\nhowever, keep in mind that among the various object detection algorithms available for such an investigation, each method\npresents unique trade-offs between speed, accuracy, and complexity, catering to different application needs and\ncomputational constraints. The YOLO family of networks has indeed been used on very diverse set of images, ranging from\nvehicles, pedestrians, face recognition to agriculture, to detect and classify crops or anatomical features in the medical field.\nYOLOX was published in July 2021 and uses YOLOV3 from Ultralytics. Compared to the earlier YOLO versions it has\nimplemented important changes (e.g. anchor-free architecture, a decoupled head, advanced label assignment and strong\naugmentation). Bickel & Valantinas write, e.g. how they apply image augmentation, not discussing at all how their additional\naugmentations affect their performance. Does it bring any additional improvement after YOLOV3’s revisions?\nProduction of training set for the investigation\nAs SSs gradually fade from dark to bright, a clear definition needs to be given in what in the classification algorithm\nconsiders to be a DSS or BSS. The authors use 40 CTX images given in Table S2 for training and testing in what they call a\nglobally-distributed data set. The reviewer’s analysis of these 40 images, their distribution on a surface map of Mars and the\nrecognized SS in those images reveal a variety of problems. The reviewer assumes that the fact that many of the 40 images\noverlap originates from the wish to include newly-created SS with older SS in the training set, thus to somehow acquire\nexamples from dark and bright SS in the training data set (In the supplement the authors show the newly-created SS taken\nfrom some of the images). After studying the image collection used, the reviewer does not agree with the authors that they\nwere able to produce a globally-distributed data set.\nConcerning Few-Shot Learning\nAI techniques have problems to rapidly generalize from a few examples. In addition, AI methods rely on learning from large-\nscale data. The question of how to find models of categories from very few instances has been discussed for more than\ntwenty years and has recently found renewed interest in the literature due to the success of large-language models. Few-\nShot Learning is of course of particular interest in cases where we have very few images which can be used as training\nexamples. When it comes to SS the question must be asked whether Few-Shot Learning really brings an advantage in view\nof the fact that with the collection of high-quality Mars images available we have hundreds of thousands of very easy\nrecognizable SS at hand. It may be that Few-Shot Learning really is able to find new categories of SS not observed before,\nbut it may also be possible that a Few-Shot Learning approach may severely underperform a conventional approach to\nbuilding an image collection for training the CNN. Keeping in mind that the authors want to draw some far-reaching\nconclusions from their research, the reviewer is surprised to see that they just assume that Few-Shot Learning is the way to\ngo. If they want to use Few-Shot Learning they need to bring forward the evidence that it is useful in this particular case (the\nreviewer has looked at the cited papers of the authors).\nAs the authors to not communicate their reasoning for choosing their approach, and given the relevance of the training set for\ntheir investigation they need to outline a sampling plan for the inclusion of images and, in particular, need to provide a clear-\ncut definition of what dark and bright means. They also should make the training data set (the 1007 and 147 DDS and BSS)\navailable to the community.\nSeparability of individual SSs\nAs the author show in Fig. 9, SSs can manifest themselves as isolated features on a slope or in groups, where individual\nSSs can be very close. This is visible when one compares patch 3 with patch 6 in Fig. 9. It makes obviously a difference for\nthe statistical consideration whether many SSs which are close to each other are counted as individual streaks, or a whole\ngroup is detected as a single SS. Computing the mean number of streaks in the boxes may be an oversimplified way to\nestimate the real number of streaks, as in many cases SS are not fully contained in the bounding boxes. The reviewer has\nnot found any discussion on the separability of individual SSs and what is required for their separability. How this can affect\nthe different research questions they want to analyze in the paper is not adequately discussed.\nBickel & Valantinas evaluate the performance of the trained detector on the testing dataset using three common metrics:\nrecall, precision and average precision AP, and the mean average precision for the two DSS and BSS classes. They seem\nto be satisfied with reporting their identified metrics, including numerous confidence scores. They use these numbers to\ncompute the actual number of SS on the surface at the available image resolution. They do not discuss at all what needs to\nbe taken into account when dealing with the special class of images they investigate. There is no “gold standard” collection\nof DSS and BSS images against which they can compare how good their detection algorithm is (like the CIFAR, VOC,\nCOCO, etc. image datasets with their clearly defined object classes which have to be detected). Their metric is tied to their\nown training set. If they want to demonstrate that their detector has the capabilities to be used for the task given, it would be\nmuch easier to choose a few selected diverse regions on the surface of Mars, run the detector, and compare the detector’s\nfinding with other methods (be it human and/or another image related method), which nobody can dispute.\nIn order to verify the accuracy of the reported SS (the reviewer has only investigated the DSS catalogue), the reviewer has\napplied three different approaches. First, coordinates of the DDS from the catalogue were projected onto the CTX mosaic\n(V1) available on the ACT Mars platform and also on parts of the Fully Controlled 6 Meters per Pixel Equatorial Mosaic of\nMars (Robbins et al., 2023); second, it was investigated how the information in the catalogue compares to published case\nstudies (e.g. Stillman et al., 2024, CTX image N11_066613, many more published cases were looked at) and thirdly, areas\nwhere CTX images and HiRise images are simultaneously available were compared. The biggest problem arose from the\nfact that a huge number of DSS locations were found where it is extremely difficult to associate the location as given in the\ncatalogue with the location where the DSS are visible on the images. A proper quantitative analysis was not possible for the\nrequested longitude interval as not all thumbnails were provided by the authors.\nCandidate review and verification of the mapping results\nThis part of the manuscript is one of the weakest sections of the paper. The reviewer could live with some of the statements\nmade in this part if the paper would be a case study but not in view of the far-reaching conclusions they want to derive from\ntheir observations. They base their comments on the quality of the catalogue, the CTX images used, the performance of the\ndetector, and the quality of the expert review. On the issues with images and the performance of the detector the reviewed\nhas already commented above, but not on the candidate and expert review.\nWhen the authors write: “The testset precision of the detector indicates that all detections made by the detector should be\ncorrect. However, past experience with object detectors being applied to extremely large and heterogeneous orbital image\ndatasets has shown that the testset performance is not always fully representative of the ‘real world’ performance of the\ndetector in the full dataset, due to the inherently limited representability of the strictly size-constrained training and testing”,\nthen they implicitly indicate that they agree with what the reviewer has written about using Few-Shot Learning and a missing\nsampling plan to acquire the necessary training images and the use of the standard metrics.\nThe authors write “We do not find any direct or indirect evidence for any negative effect of the scanned CTX images on the\nmapping outputs, such as potentially caused by cloud cover, shadowing, haze, and data artifacts. We point out that Dickson\net al. (2023) carefully selected the images that make up the global mosaic to minimize any such negative impacts”. Cloud\ncover, dust storms, etc. are all seasonal effects. As the authors have looked in their investigation for these effects and could\nnot find any, the reader is interested that they share their findings with her/him and the reader would also probably like to\nlearn how they could conclude this.\nFurthermore, the authors write: “The extremely wide range of slope streak morphologies identified by the detector (Fig. S1)\nprovides evidence for a robust and capable detector”. The reviewer needs to remind the authors that you can have a detector\ncapable of seeing a diverse variety of features (classes) but which can completely underestimate/overestimate specific\ncategories of morphologies.\nIn the following section of the manuscript the authors make several statements in which they either seem to contradict some\nof their own earlier statements made in their manuscript or alternatively were not able to express what they wanted to\ncommunicate to the reader:\n“The increased false positive rate as achieved by the bright streak detector (Fig. 9C) can likely be attributed to a) the smaller\ntraining dataset, which in turn is caused by the relative scarcity of bright streaks (as noted by other authors, such as, e.g.,\nSchorghofer et al., 2007; Valantinas et al., 2021), and b) the more subtle radiometric contrast between bright streaks and the\nsurrounding slope (compared to dark streaks). Notably, the preferably more equatorward distribution and W-facing\norientation of bright slope streaks is likely to be attributed to a bright slope streak detector that is more successful in\nretrieving bright streaks on well-illuminated, Sun-facing slopes, given the very faint appearance of bright slope streaks (Fig.\n1A,C) and MRO’s sun-synchronous orbit (imaging takes place in the early local afternoon)”.\nIt is not clear at all to the reviewer how the above-manuscript section can fit with their description of a “perfect and robust”\ndetector.\nIf the authors write “In order to remove all obvious false detections and ensure the integrity of the scientific analysis, we\nmanually review all CNN-identified candidates”, does this mean that the authors have realized that their detection algorithm\ndoes not deliver on their expectations?\nThe reviewer also believes that a reader is much more interested in understanding the details on how the authors want to\n“save” the SS-proposals produced by their network with an “expert review” than being given all the details on how much time\nthe reviewing process needed.\nThe statement “We note a number of false detections, but all false positives closely resemble the visual appearance of\nstreaks, such as wind streaks, linear shadowed topography, dunes, and mountain ridges (Fig. S10), i.e., do not suggest a\nfundamental problem with the detector”, indicates that Bickel and Valantinas should really have a look at the paper of Wang,\nI. et al., 2017 which shows what a simple adaboost algorithm can achieve in terms of SS recognition.\nBased on the above problems with the methodology used the reviewer has a hard time following\nBickel and Valantinas in their argumentation when they start to identify global-scale, statistically significant relations to argue\nfor or against the various hypotheses which could generate streaks.\nMany of the variables they use (e.g., Equivalent Hydrogen (WEH), wind speed, etc.) to correlate their SS occurances are\nquantities which are not only difficult to measure, but rely on many assumptions. In addition these quantities are known at\ncompletely different resolution compared to the mapping of SSs with the help of the CTX images. For example, WEH\ndetermination is in itself an extremely difficult process which in essence requires the estimation of the soil’s water content\nfrom measurements of neutrons. In order to map the water abundance in a given subsurface layer from orbital neutron\nmeasurements one has to develop a procedure which restores the water abundance at different points of the soil’s volume\non the basis of the measured neutrons (of different energies at different orbital points). Besides a good model for the detector\na model for the atmosphere and a model for the Martian subsurface is needed. These maps which the authors are using\nwere produced as global maps! They have to be used with uttermost care when you look into specific regions.\nCorrelating the SS observations found with wind speeds one should also not forget that the seasonal wind intensities are\nobtained using what is called a “climatology average solar” scenario. Due to the coarse resolution of the General Circulation\nModel (GCM), small-scale variations in topography remain unconsidered in the simulation runs. Values are often estimated\nfor daytime-maximum surface temperatures for a given study site using average solar conditions and typical amounts and\ndistributions of suspended dust in the atmosphere.\nConclusion of review\nBickel & Valantinas have taken up an interesting topic and have written an easy to comprehend article with really very\nappealing illustrations. In view of the far-reaching conclusions, they want to draw from their analysis of CTX images, it would\nhave been much better to produce an unedited CNN based catalogue as a supplement to the paper with all the necessary\ninformation produced by the CNN (exact coordinates, thumbnails, image ID, confidence, etc.) A second catalogue which is\nthe result of what they call the expert review could have then been released. Based on this information a detailed and\nreproducible discussion could have been started in which they have a chance to win support for the various claims they\nmake in their work.\nUnfortunately, the authors use many of the data sets which they need in their argumentation too \"literally\". I would\nrecommend that they include some colleagues in their work who have worked on the analysis of nuclear spectroscopic\nmethods.\nIn the present form the article does not provide the reproducible evidence for the conclusions they draw. I suggest to the\nauthors that they use part of their work to investigate their hypotheses on a more local scale where they are able to check\ntheir findings in more detail (many of the papers in their reference list have done this)."}, "author_response": "Response to Reviewers\nWe thank the two reviewers for their detailed and constructive feedback. We carefully considered all\ncomments and revised the manuscript and supplementary information accordingly (text, figures, and\ntables). Changes in the manuscript are highlighted in green color. Please note that the manuscript –\nafter an extensive set of revisions – reached the maximum permissible word count for the Online\nMethods section.\nPlease find our replies (blue bold) to the reviewers’ comments (italic) below.\nAdditionally, we have incorporated several improvements and clarifications that go beyond the\nscope of the editor's and reviewers' suggestions, including updated references and improved\ncontextualization of our findings, as detailed below.\nValentin Bickel (on behalf of all co-authors)\n1\n--- --- ---\nGeneral updates\n- We replaced three placeholder (LPSC abstract) references with an actual journal reference\nwhich is now in press at GRL (Bickel et al., 2024a, Geophysical Research Letters)\nBickel, V.T., Daubar, I., Sokolowska, A., Bonab, A., Haut, I., Conway, S. (2024a). The First Global Catalog of Rockfall Locations on Mars.\nGeophysical Research Letters – in press.\n- We added new references to underline YOLOv5’s applicability to map features of interest\nin planetary image data\n- We added new references to support our overall interpretations and enrich the discussion\n(e.g., Heyer et al., 2024; Daubar et al., 2016)\n- We added a comparison of our slope streak counts to the number estimates derived by\nAharonson et al. (2003) to the manuscript:\nWe note that the deep learning-predicted detections contain an average of 2.33 individual\nstreaks per detection box due to streak co-location and overlap (see Fig. 1A,1B,S1), indicating\nthere might be up to ~140,000 and ~1,900,000 individual, CTX-scale bright and dark slope\nstreaks on Mars, which is several factors more than was previously estimated (~800,000 dark\nstreaks, Aharonson et al., 2003).\n- Added three new supplementary figures that showcase recently-acquired HiRISE, CaSSIS,\nand CTX images that represent examples of newly-formed impact craters as well as nearby\nslopes with newly-formed slope streaks, e.g. Fig. S10:\n- Updated our reference of slope streak formation seasonality in the Introduction section\nfrom Ls ~180°-~270° to Ls ~140°-~270° to better reflect the findings of Heyer et al. (2019)\nand Stillman et al. (2024)\n- Added “Data availability” and “Code availability” sections, per editorial request.\n2\n--- --- ---\nReviewer #1\nSummary\nMachine learning methods were applied to a global CTX image mosaic to develop an inventory of\nslope streak occurrence. Statistics were gathered to count streak occurrences, size, area, and\ndistribution. These data were correlated with geologic units and global geophysical datasets such as\nelevation, minerology, surface albedo, thermal properties, and atmospheric properties. The new\ncatalog was compared to previous regional or local catalogs to validate the machine learning results.\nThe result of having a global inventory of slope streak features, including RSL, provides a powerful\ndataset for a more complete analysis of the surface and atmospheric characteristics that influence\nslope streak formation.\nThis study provides the first global inventory of slope streak features for Mars, using rigorous machine\nlearning techniques to fully take advantage of the many years of MRO observations from CTX and\nHiRISE. This study's results extend previous studies that examined local or regional populations of\nslope streaks and RSL. The main result, that slope streaks and RSL are the result of dry surface-\natmosphere interactions, agrees with several previous works, and provides strong evidence against\naqueous processes.\nNew findings reported here include the identification of previously unidentified bright streak\n\"clusters\" in east Kasei Valles, northwest Arabia Terra, Syrtis Major, and dark streaks in Lunae\nPlanum.\nThe accuracy of the deep learning algorithm is carefully quantified by the authors, with uncertainties\nappropriately noted concerning image resolution, false positives and false negatives. The\nuncertainties in correlating the slope streak features with other global datasets at lower spatial and\ntemporal resolution are also considered.\nThe figures are clear and well-designed and succeed in illustrating the results of the global analyses.\nSome corrections are noted below.\nOur reply: We thank the reviewer for their assessment and productive feedback. We carefully\naddressed all of the corrections and suggestions, as discussed in the point-by-point replies below.\nThe methodology and data used in the analyses are described in detail. It would be beneficial to the\ncommunity and for reproducibility for the authors to provide their database of slope streak\nidentifications as a linked dataset to this publication.\nOur reply: We agree, of course, and stress that publishing and openly sharing our dataset (i.e., the\ncatalogs of dark and bright slope streaks as well as image thumbnails of all slope streak detections)\nwith the community is one of the main goals of this overall publication and manuscript.\nWe originally refrained from making the dataset publicly/openly accessible to preserve the\noriginality and overall impact of the manuscript – and to avoid the dataset being used by the\ncommunity without a proper journal reference.\nWe now made the full dataset openly available in the University of Bern’s BORIS archive:\nhttps://doi.org/10.48620/75966\nconducted on a mosaic of CTX images acquired at many different seasons and Mars years without\ndistinction, convincing conclusions about temporal phenomena are harder to make.\nOur reply: We agree, and tried to address the various, potential issues of using a mosaic in e.g. the\n“Verification of the mapping results” and “Seismic & impact event distance analysis” sections, in\nchapter 4 “Online Methods”:\nThe quality and reliability of the produced global slope streak dataset rely on the a) combined quality\nof the used CTX images, b) performance of the slope streak detector, and c) quality of the manual\nexpert review. We do not find any direct or indirect evidence for any negative effect of the scanned\nCTX images on the mapping outputs, such as potentially caused by cloud cover, shadowing, haze, and\ndata artifacts. We point out that Dickson et al. (2024) careful selected the images that make up the\nglobal mosaic to minimize any such negative impacts.\n[…]\nWe note that our mapping approach is relying on a stitched mosaic consisting of CTX images taken\nseveral years apart (2006 to 2020, Dickson et al., 2024), and at different times/seasons; as a result, our\nslope streak catalog does not represent one snapshot in time, but a wide time-range.\n[…]\nWe note that this method is strongly affected by the quality and spatiotemporal availability of the\nimpact and marsquake data; Daubar et al. (2022) acknowledge that their impact catalog is likely\nincomplete, particularly in the southern highlands; in addition, a CTX image used in the mosaic might\nhave been taken prior to an impact even at the same location.\n[…]\nIn turn, a substantial fraction of the CTX images in the global mosaic were acquired prior to the InSight\nmission (late 2018 to late 2022), making a meaningful comparison with the seismic data challenging.\nDickson, J., Ehlmann, B., Kerber, L., Fassett, C. (2024). The Global Context Camera (CTX) Mosaic of Mars: A Product of Information-Preserving Image Data\nProcessing. Earth and Space Science 11 (7).\nWe add a new paragraph to the Discussion section to make this limitation more obvious to a\nreader and point at future work:\nWe note that one of the main limitations of this study is the lack of information on the timing of\nglobal-scale streak and RSL formation. Globally-distributed information about slope streak and RSL\nformation would allow for a more robust statistical comparison of streak and RSL occurrence and\npotential drivers.\nMinor corrections/revisions suggested:\nFigure 1.F) Should be 10°x10° quadrangles? Ratio numbers are hard to read. Perhaps indicate the\nvalue ranges on the legend instead of writing them within the grid squares. Do keep the \"bright >>\ndark\" etc. annotation on the legend. It is very helpful for interpreting the map.\nOur reply: Yes, thank you for pointing this out. We corrected the caption accordingly. We tried to\nincrease the font size of the ratio numbers, but ran into issues with the visual appearance because\nneighboring numbers overlap. We added a color bar, too, as suggested, thank you.\nLines 65–69: Did the authors test the assumption that there should be \"14x more streaks resolvable at\nthe HiRISE-scale\"? That may be true for features that approach the limits of HiRISE resolution, such as\nRSL, but is not likely to be true for the larger features that are clearly resolved at CTX scale, such as\nthose in Figure S1. However, it may be true for some cases such as the images shown in Figure S7.\nBergonio et al. (2013, see graph below) who identified a power law relation between image\nresolution and number of resolved slope streaks using MRO HiRISE and Viking Orbiter Camera\nimages (which overlaps with the spatial resolution of MRO CTX as used in this study):\n[REDACTED]\nWe modified the language of the respective sentence in the manuscript to better communicate the\nuncertainty of this estimate (please note that the estimated total streak numbers were updated):\nApplying the power law relation between the number of resolved streaks and image resolution as\nestablished by Bergonio et al. (2013), we estimate there might be ~14 times more streaks resolvable at\nthe HiRISE-scale (High Resolution Imaging Science Experiment, ~0.3 m, McEwen et al., 2007), i.e.,\n~2,000,000 bright and ~28,000,000 dark streaks.\nLines 127–131 (and in the Abstract) seem to imply that estimated volumes of dust mobilized by slope\nstreaks are a potential source for the global atmospheric dust cycle. As noted by the authors in the\nconcluding sentence of this paragraph, this assumption is not supported by observational evidence. If\nthe dust were being mobilized into the atmosphere, as suggested, slope streaks would probably not\nretain topographic expression (note the assumed streak thickness of 1 m, and caveat noted in lines\n338–342,) unlike other mass wasting events that have been observed by HiRISE (e.g., NPLD scarp\navalanches) which leave no topographic signature but do display dust dispersing into the atmosphere.\nSuggest withdrawing this claim (that X amount of dust is being mobilized back into the atmosphere)\nand just noting the estimated mass movement of dust downslope.\nOur reply: Thank you for raising that concern. We reworked the structure and language of the\nrespective section to better communicate a) the lack of direct evidence and b) the hypothetical\nnature of our thought experiment:\nThe injection of dust into the atmosphere by slope streaks has not been observed yet, but would make\nslope streaks an important contributor to the martian dust cycle and climate, potentially accounting\nfor a significant fraction of the surface-atmosphere dust exchange budget.\nWe modified the respective sentence in the abstract, too:\nWe show that slope streaks modify less than 0.1 % of the martian surface, but transport several global\nstorm equivalents of dust per Mars year, potentially playing a major role in the martian dust cycle.\nThe point made in the Methods section (lines 485–488) about the lack of temporal information in the\nsurvey should be moved to the Discussion section. The authors did conduct a few case studies\nshowing positive and negative streak formation post-impact in the vicinity of known impacts (Figs. S7\nand S8). Although this case-by-case analysis is not the main point of the paper, it is a much stronger\nanalysis than comparing the entire CTX dataset to the known recent impacts and seismic events,\n5\nregardless of timing (as noted in lines 186–188). The correlation to impacts is not meaningful if the\ntiming of the image acquisition (before or after the possible date of impact) is not taken into\nconsideration.\nOur reply: We agree and added the lack of temporal information to the Discussion section, as\nrequested:\nWe note that one of the main limitations of this study is the lack of information on the timing of\nglobal-scale streak and RSL formation. Globally-distributed information about slope streak and RSL\nformation would allow for a more robust statistical comparison of streak and RSL occurrence and\npotential drivers.\nWe share the reviewer’s overall concern about the limited expressivity of our spatial comparison\nof streak and new impact/quake epicenter distributions, without including event and formation\ntimes. Despite those concerns, we are convinced that our global-scale, statistical analysis is\nimportant and justified, and represents a vital contribution to the manuscript:\n1. important because impacts are one of the most promising, potential (dry) drivers of streak\nand RSL formation, as supported by the various supplementary case studies and figures\n(e.g. Fig S7 – S11), and\n2. justified because both our slope streak and the dated impact datasets span over several\ndecades and feature significant temporal and spatial overlap; this means that even if not\nall of the actual spatiotemporal co-locations of streaks and dated impacts are resolved by\nthe data and our analysis, there still should be a recognizable, statistically-significant\namount of instances which do resolve that relation, as is implied by our results (see e.g.\nFigure 4).\nWe are currently working on deriving spatiotemporal global streak formation information to better\naddress those uncertainties in the future.\nWe tried to further address the reviewer’s and our own remaining concerns by re-working some of\nthe language related to the potential role of impacts in driving streak and RSL occurrence\nthroughout the manuscript, for example:\nWe observe that dark streaks and date-constrained impact events (Daubar et al., 2022) are partially\nco-located in dusty regions (Fig. 4A).\nAnd\nWe identify three global-scale, statistically significant relations that support dry formation hypotheses\nfor slope streaks: streak populations a) tend to be slightly closer located to new impacts than to the\nsame number of randomly placed locations in some regions (Fig. 4B); […]\nFigure 2.B) Degree symbol on Bright streak values for TES albedo (0.27) and OMEGA nanophase ferric\noxide (1.02) are not necessary? Units measured are not degrees.\nOur reply: We thank reviewer 1 for highlighting those issues. We corrected the incorrect labels.\nFigure 5. White points for bright slope streaks and white lines for valley networks are confusing.\nConsider using a different color (light blue?) to indicate valley networks.\nOur reply: We agree, we modified the valley network line color (white  blue) and updated the\ncaption accordingly.\nReferences\n6\n- indicate (a) and (b) for references from the same author and same year (e.g., Dundas 2020a and\nDundas 2020b)\nOur reply: Done, thank you.\nChojnacki et al. 2016 Nature Geosciences https://doi.org/10.1002/2015JE004991 should be\nreferenced in the Introduction.\nOur reply: We agree, thank you for pointing this out.\nLine 356: Suggest adding citation to Dundas et al., 2017 (Nature Geosci.)\nhttps://doi.org/10.1038/s41561-017-0012-5.\nOur reply: Done, thank you.\n7\n--- --- ---\nReviewer #3\nReview of manuscript: Streaks on Martian Slopes are Dry\nBy V. T. Bickel & A. Valantinas\nGeneral Overview\nSlope streaks (SS) are narrow, elongated, fan-shaped features which range from about a few meters\nto several kilometers in length with a width in the range of tens of meters to 1m following the\ndownslope topography of the terrain. They display in most cases an increasing width along the\ndownslope direction. They appear as darker (DSS) and brighter (BSS) varieties. Slope streaks can\nappear anytime in the Mars year in the equatorial and subequatorial regions of Mars. Slope streaks\nare generally thought to form in single events, appearing to be formed within a short temporal span.\nThey gradually fade over decades.\nSS are of special interest because they represent one of the most widespread and largest active\ngeological processes on the surface of present-day Mars. The origin of slope streaks and their\nformation mechanism is to-date not yet understood and highly debated.\nA \"wet\" mechanism suggests that a fluid, e.g. liquid water, could be responsible for their creation,\npossibly as a result of groundwater springs, melting surface ice, or the formation of brines. In the\nopposing view, a \"dry\" mechanism suggests that “dust avalanches” are responsible. These, in turn,\ncould be caused by air fall deposits, subsurface melting, or localized disturbances – ranging from\nrockfalls, meteorite impacts to tectonic activity. Both of these explanations have limitations when\nit comes to explaining observed slope streaks.\nIn view of the ongoing debate on the origin of DSS and BSS any research which can shed additional\nlight on slope streaks is of interest.\nRecurring slope lineae (RSL) are narrow, downslope-trending features, darker than their surroundings,\nthat gradually extend primarily in warm seasons, fade and recur annually (Dundas et al., 2017).\nDistinguishing SS from RSL, dunes, and shadowed topographical features, to name only a few\ngeomorphic features which can look very similar, can be an extremely difficult task, especially when\nonly images are available which were taken under very different observational conditions.\nThe authors (Bickel and Valantinas) use grayscale images for their investigation generated by the\nContext Camera (CTX) with a resolution of up to ca. 6 m/px onboard NASA’s Mars Reconnaissance\nOrbiter (operating from Nov. 2006 until today), and state in their paper that they could use a deep\nlearning-driven detector which is able to localize about 20 % of all BSS and 55 % of all DSS in a given\nCTX image. Based on this, they conclude that the actual number of bright and dark streaks observable\nat the CTX-scale on the surface of Mars are ~65,000 and ~970,000. Finding that their detection boxes\ncontain an average of 2.33 streaks due to overlap and collocation they claim that the total number of\nBSS and DSS on Mars at the CTX resolution should amount to 150’000 and 2’260’000 BSS and DSS,\nrespectively. Using their global gestatistical findings, they furthermore claim that their research\n- shows that slope streaks modify less than 0.1 % of the Martian surface, but transport several global\nstorm equivalents of dust per Mars year, potentially playing a major role in the surface-to-\natmosphere dust exchange.\n8\n- challenges “wet streak formation models” and present compelling evidence for “dry streak\nformation”, driven by seasonal dust delivery and energetic processes like wind and meteoritic\nimpacts.\n- demonstrates that they have identified a global, spatiotemporal relation between recurring slope\nlineae formation, seasonal dust deposition, and dust devil activity.\n- suggests that modern Mars’ slopes do not commonly experience transient flows of water or brines.\nData used in investigation:\n86,546 CTX images from the 86,571 separate CTX images used to produce the CTX mosaic (V1) were\nprocessed to locate dark and bright streaks in these individual images. The images used when\ndownloaded are already individually ISIS processed.\nMethod Used:\nDetection of dark and bright is achieved by using a downloadable Yolov5x convolutional neural\nnetwork (CNN) in the latitude range from -40 degree N to -20 degree S.\nIndividual CTX image files are tiled into 1000x1000 pixel patches and fed into the YOLOX network.\nUpon detection, the geographic location of the detection is reconstructed using the image’s\nmetadata.\nTraining is done by using 40 MRO CTX images. Using these images a training dataset of 1007 dark\nand 147 bright slope streaks is produced.\nDetections, as well as their associated metadata, are stored (the diameter of the bounding box, and\n‘thumbnails’).\nRelevance of research\nProducing an unbiased catalogue of dark and bright SS, identified on the surface of Mars, which\ncontains the exact center coordinates and the surface area coverage of the streaks, together with the\ncorresponding thumbnails and the CTX file identifier on which the SS were found, would be a highly\ndesirable endeavor, being useful for various research purposes in view of the ongoing debate on the\ngeologic and climate-related settings under which these SS occur.\nEvaluation of Paper\nFormal appearance of paper\nThe manuscript by Bickel & Valantinas is well written, easy to read and contains many graphically\nappealing illustrations. It contains an extended list of references.\nAppraisal of submitted work\nThe majority of the conclusions of the Bickel and Valantinas paper are based on their global\ngeostatistical detection of Martian SS. The precise information of the located SSs has been collected in\na BSS and DSS catalogue. As these catalogues are then used for the statistical analysis on which the\nargumentation for their findings is based, the reviewer has focused and tried to verify the\nreproducibility, accuracy and unbiased collection of the catalogues’ SS information from the data pool\nutilized.\nOur reply: We thank the reviewer for their assessment and feedback. We carefully addressed all of\nthe concerns and suggestions, as discussed in the point-by-point replies below.\nwhich the SS can be detected and that their location can accurately be reconstructed.\nPrior work by Wang, I. et al., 2017 (reference not mentioned in manuscript) has demonstrated that an\nautomatic detection and an accurate size measurement of Martian dark SS is possible.\nOur reply: Thank you for pointing out the work by Wang et al. (2017). We studied the publication\nand used it to contextualize the performance of our detector by adding a thorough discussion (with\na focus on the relative detection performance) to the Online Methods section, e.g.:\nYOLOv5 is a well-established, verified, cutting-edge object detection framework that has been utilized\nfor an extremely wide range of scientific applications, such as food science, robotics, medical image\nprocessing, and planetary science (e.g., Hossain et al., 2022; Jubayer et al., 2021; Song et al., 2021;\nMills et al., 2024; Bickel et al., 2024b). YOLOv5 represents the best compromise between state-of-the-\nart performance and reliability, and has been shown to significantly outperform (performance and\nspeed) earlier machine learning algorithms such as based on AdaBoost, Histogram of Oriented\nGradients, and Region-based CNNs (e.g. Diwan et al., 2022; Reddy & Premkumar, 2022; Kaur & Singh,\n2023) as were used by earlier studies that reported on the automated detection of slope streaks\n(Wang et al., 2017).\n[…]\nAt a score of 0.6 our detector outperforms (for dark streaks only, recall ~85 %) the performance as\nreported by earlier studies that used a combination of feature selection and machine learning (binary)\nclassification techniques and HiRISE images (Wang et al., 2017, recall ~79 %), at a continuously high\nlevel of precision (~94 %). However, given the significant number of expected slope streak candidates\nwe deliberately choose a confidence score of 0.8 for this study, to maximize the precision of the\ndetector and minimize the amount of time required for the subsequent review efforts, as described\nbelow.\n[…]\nTo generate unbiased catalogues of DSS and BSS for a given surface region requires one to have an\nimage mosaic available which was constructed with the required resolution and which has no areas\non which the detection method cannot be applied.\nThe construction of surface mosaics for Mars is for different reasons a nontrivial complex task. The\nMRO satellite orbits Mars in a sun‐synchronous orbit, such that illumination conditions between\noverlapping CTX images should be comparable. However, due to the existence of the Martian\natmosphere with its changing clouds and dust cycles, images can have a very different quality, so that\nthe ability to recognize surface features on images mapping the same surface area can be very\ndifferent.\nTo circumvent the problem of choosing the best images for the SS detection and to avoid the\nproblems involved in constructing regional mosaics, Bickel and Valantinas choose only the images\nwhich were used in the “Dickson Mosaic” which was generated using the CTX images at a resolution\nof ≈5 m. Using such a mosaic would, in principle, ensure that SS are not missed or that individual SS\nare not counted multiple times. However, there are various issues which complicate such an\napproach.\nAs the manuscript describes, they download all the 86,546 CTX used for the “Dickson Mosaic”. It is not\nclear to the reader how they can avoid that the detector sees multiple times overlapping image\nregions, thereby increasing the probability to see the same SS several times. To do this correctly they\nwould have to use the shape information from the 4x4 degree tiles of the “Dickson Mosaic” to\nguarantee an equal scan probability of each surface patch of the image (this information is in the\nDickson Mosaic, in principle, fully accessible). The reviewer has Investigated the given thumbnails\n10\n(although not complete in the requested areas) and identified from the names of the images on which\nthe streaks were detected that there must have been an overlap of images. There is no explanation in\nthe Method section how they avoided the overlap using the available shape files (if they were used at\nall). The thumbnail also reveals photometric issues which are not discussed but could be\nresponsible for some false detection that the authors notice.\nIf the idea of the authors was to eliminate multiple identified SS after they were detected, then this\nwould require that multiple detected SS would need to have sufficiently accurate coordinates on the\ndifferent images on which they were detected. Due to the fact that the “Dickson Mosaic” is not using\na controlled network approach one has to expect shifted coordinate locations for the same SSs found\non different images. As the CNN is operating on the 1000x1000 patches of a single CTX image,\nthe manuscript lacks a discussion of the above-mentioned problematic and how it can be solved.\nOur reply: We share the overall concerns of the reviewer regarding the limitations emplaced by the\nused CTX global mosaic. We would like to point out that we conducted the scan of the CTX mosaic\nat a time when the final Dickson mosaic – and its metadata, Dickson et al. (2024) – was not yet\npublished nor openly available. We received a list of the CTX image IDs that make up the final\nmosaic directly from J. Dickson (pers. comm.) in late 2022, but without the mosaic itself and\nwithout any of the vector- and meta-information that is associated with the recently published\nmosaic (Dickson et al., 2024). In addition, we needed access to the temporal information\nembedded in the original CTX images, to enable future investigations into slope streak formation\nrates (forthcoming).\nWe clarified that important contextual information in the Online Methods section using the phrase\n“pre-v01” to describe the intermediate state of the mosaic image data we used (post-beta [Dickson\net al., 2018] yet pre-final [Dickson et al., 2024]):\nThe pipeline successively downloads all of the 86,546 CTX images contained in the post-beta (Dickson\net al., 2018), pre-v01 version of the global CTX mosaic (pers. comm. J. Dickson), which was later\nofficially published in Dickson et al. (2024) at https://image.mars.asu.edu/stream/, and tiles them into\n1000x1000 pixel patches (Fig. 9A). At the time of data processing, the final version of the Dickson et al.\n(2024) mosaic was not yet available, which required us to process the individual CTX images that made\nup the pre-v01 version of the mosaic instead.\nWe ran extensive tests to verify that our scan of the martian surface is as complete and consistent\nas possible using the pre-v01 version of the mosaic (vs the final Dickson et al., 2024, mosaic):\n1) CTX mosaic image quality: all images used in the global CTX mosaic were specifically\nselected by Dickson et al. (2024) to not contain significant artefacts or atmospheric\nfeatures that could obscure the surface of Mars. Dickson et al. (2024) state that clouds\nwere a small issue, and only at very high latitudes, i.e., regions that do not host slope\nstreaks. During the extensive, month-long review of the slope streak dataset we did not\nidentify a single artefact or major atmospheric obscuration in the Dickson mosaic that\nwould significantly impact streak detection. We are convinced that any artefact or\natmospheric phenomenon – if present – does not have any significant impact on our\ndataset and interpretations.\nWe acknowledge the remaining uncertainty in various sections of the manuscript, such as\nin the Online Methods section:\nThe quality and reliability of the produced global slope streak dataset rely on the a) combined quality\nof the used CTX images, b) performance of the slope streak detector, and c) quality of the manual\nexpert review. We do not find any direct or indirect evidence for any negative effect of the scanned\ndata artifacts. We point out that Dickson et al. (2018,2024) carefully selected the images that make up\nthe global mosaic to minimize any such negative impacts.\nAnd:\nWe note that our mapping approach is relying on a stitched mosaic consisting of CTX images taken\nseveral years apart (2006 to 2020, Dickson et al., 2018,2024), and at different times/seasons; as a\nresult, our slope streak catalog does not represent one snapshot in time, but a wide time-range.\nWe note that any apparent photometric issues (e.g. saturation) in the preview thumbnails\nare not caused by the CTX data itself but by our processing and file storage pipeline. The\nthumbnails are not used for detection and the scientific analysis in any way – they are\npreview/quick-view files only.\nDickson, J., Ehlmann, B., Kerber, L., Fassett, C. (2024). The Global Context Camera (CTX) Mosaic of Mars: A Product of Information-Preserving Image Data\nProcessing. Earth and Space Science 11 (7).\n2) Image overlap and double detections: we agree with the reviewer that the geo-location\nand co-registration of the globally-available CTX images is not always accurate, and that\nthe individual images that make up the Dickson mosaic overlap to some extent. As a result,\nthere is a chance that our database includes a small number of duplicate detections in\nregions of image overlap. We note that the potential for image overlap is largely\nindependent of the geographic location, which means that any duplicate detections would\nnot systematically affect specific geographic sections of our dataset, if present, and thus\nwould not systematically affect the science analysis. Unfortunately, the slight image\nlocation inaccuracies paired with the extremely large number of slope streak detections\nimpedes an automated or manual detection and removal of duplicates, which is why they\nare not specifically identified and removed. We point out that all earlier global or regional\nsurveys included a certain amount of duplicate detections as well – caused by image\noverlaps – such as Aharonson et al. (2003) and Schorghofer et al. (2002,2007).\nHowever, we agree that a thorough case study of the presence of duplicates is necessary\nand performed a study in the Tharsis region. The results show that there are 70 duplicate\ndetections in a population of 1086 slope streaks, i.e. about 6 % of the sub-dataset in the\narea. These duplicates are homogeneously distributed across the region of image overlap.\nThis case study helps to put a quantitative constraint on the presence of duplicate\ndetections and informs the discussion of their potential effect on the further science\nanalysis. Despite the presence of duplicates, their homogeneous distribution and small\nnumber strongly suggest that duplicates do not negatively affect the global-scale,\nstatistical results of this work:\nfootprints) used for the case study; note that both images intersect an area of slope streak-bearing terrain (white area)\noutlined by white dashed lines. B) zoom-in on a small sub-section of the region with overlapping CTX coverage; several pairs\nof duplicate detections are highlighted by white ellipses. Note the slight spatial offsets between slope streak and detection\ncenters (black dots) that impede a reliable automated removal of duplicates. In a pool of 1086 slope streak detections, the\nimage overlap leads to 70 duplicates, i.e., ~6 % of the total count. Blended global CTX mosaic (Dickson et al., 2024) and\nHRSC-MOLA shaded relief in the background.\nWe added the figure to the supplementary information and a new paragraph/sentence to\nthe main text and Online Methods section that report on our case study and detail the\npotential for duplicate detections and their potential consequences:\nThe deep learning-driven detector is able to identify about 20 % and 55 % of all bright and dark streaks\nin a given CTX image, on average; at the same time, we estimate that about 6 % of all detections are\n13\nduplicates caused by the spatial overlap of some of the CTX images that make up the mosaic (Fig. S3),\nsuggesting that the actual number of bright and dark streaks observable on the surface of Mars are\n~60,000 and ~830,000 (at the CTX-scale, ~6 m, ‘statistically-corrected population’, p ).\nC\nand\nDue to the potential for geo-location and co-registration inaccuracies as well as partial overlap of the\nCTX images that make up the pre-v01 version of the global CTX mosaic (pers. comm. J. Dickson) there\nis a small chance that slope streaks located along the seams of individual images are detected multiple\ntimes (‘duplicates’). However, a case study in the Tharsis region suggests that the number of potential\nduplicate detections in the overall dataset is expected to be small, around 6 % of the total streak count\n(Fig. S3). The slight location inaccuracies paired with the large number of slope streak detections\nimpedes an automated and manual detection and removal of duplicates, which is why they are not\nspecifically identified and removed. We note that duplicate detections are evenly distributed across\nthe mosaic and globe and, thus, do not systematically affect the geostatistical science analysis.\nIn addition, we used that quantitative constraint to correct our estimates of the total\nnumber of slope streaks present on the martian surface:\nWe identify a total of 13,026 bright and 484,019 dark slope streaks …\n[…]\nThe deep learning-driven detector is able to identify about 20 % and 55 % of all bright and dark streaks\nin a given CTX image, on average; at the same time, we estimate that about 6 % of all detections are\nduplicates caused by the spatial overlap of some of the CTX images that make up the mosaic (Fig. S3),\nsuggesting that the actual number of bright and dark streaks observable on the surface of Mars are\n~60,000 and ~830,000 …\n[…]\nWe note that the deep learning-predicted detections contain an average of 2.33 individual streaks per\ndetection box due to streak co-location and overlap (see Fig. 1A,1B,S1), indicating there might be up to\n~140,000 and ~1,900,000 …\n[…]\nApplying the power law relation between the number of resolved streaks and image resolution as\nestablished by Bergonio et al. (2013), we estimate there might be ~14 times more streaks resolvable at\nthe HiRISE-scale (High Resolution Imaging Science Experiment, ~0.3 m, McEwen et al., 2007), i.e.,\n~2,000,000 bright and ~28,000,000 dark streaks.\nThe reviewer can agree with the manuscripts’ authors that the YOLO family of networks is one of\nmany CNNs that offers a good balance of speed and accuracy, enabling the rapid and reliable\nidentification of objects in images. One should, however, keep in mind that among the various object\ndetection algorithms available for such an investigation, each method presents unique trade-offs\nbetween speed, accuracy, and complexity, catering to different application needs and computational\nconstraints. The YOLO family of networks has indeed been used on very diverse set of images, ranging\nfrom vehicles, pedestrians, face recognition to agriculture, to detect and classify crops or anatomical\nfeatures in the medical field.\nYOLOX was published in July 2021 and uses YOLOV3 from Ultralytics. Compared to the earlier YOLO\nversions it has implemented important changes (e.g. anchor-free architecture, a decoupled head,\nadvanced label assignment and strong augmentation). Bickel & Valantinas write, e.g. how they apply\nimage augmentation, not discussing at all how their additional augmentations affect their\nperformance. Does it bring any additional improvement after YOLOV3’s revisions?\nperformance, and infrastructure-compatibility. Based on our extensive experience over the past\nyears and months, the overall performance of YOLOv5 is currently unrivaled. The image\naugmentation we perform during training is strictly following computer vision best practices and\nthe state-of-the-art, as detailed in the Online Methods section:\nWe apply substantial label augmentation during training to prevent model overfitting and minimize\nany negative impact of the relatively small number of labels, as is common practice in computer vision\nand few shot learning, utilizing label rotation, up-down flipping, shearing, up- and down-scaling, and\nbrightness and contrast modifications. We do not apply left-right flipping to preserve a proper\nrepresentation of the illumination conditions governed by MRO’s sun-synchronous orbit in the training\ndata.\nProduction of training set for the investigation\nAs SSs gradually fade from dark to bright, a clear definition needs to be given in what in the\nclassification algorithm considers to be a DSS or BSS. The authors use 40 CTX images given in Table S2\nfor training and testing in what they call a globally-distributed data set. The reviewer’s analysis of\nthese 40 images, their distribution on a surface map of Mars and the recognized SS in those images\nreveal a variety of problems. The reviewer assumes that the fact that many of the 40 images overlap\noriginates from the wish to include newly-created SS with older SS in the training set, thus to\nsomehow acquire examples from dark and bright SS in the training data set (In the supplement the\nauthors show the newly-created SS taken from some of the images). After studying the image\ncollection used, the reviewer does not agree with the authors that they were able to produce a\nglobally-distributed data set.\nOur reply: As the reviewer mentions, slope streaks slowly and continuously fade, where their\nappearance is dominated by their physical state, but also by the imaging geometry and\nillumination, as thoroughly discussed by e.g. Valantinas et al. (2021,\nhttps://doi.org/10.1016/j.pss.2021.105373). Thus, there is no clear boundary between dark and\nbright slope streaks. We added a sentence to the Results section to clarify our understanding of\ndark and bright streaks:\nHere, ‘dark’ streaks include recently-formed streaks that appear dark in CTX images, and ‘bright’\nstreaks refer to old, partially-faded streaks that appear bright in CTX images (Fig. 1A,1B).\nOur training and testing dataset is constructed using CTX images taken across the three main slope\nstreak hotspots: Amazonis, Tharsis, and Arabia, as identified by numerous previous, manual (and\nunbiased) surveys. Importantly, no earlier study has found any indications that slope streak\nmorphology would vary geographically. Other criteria we considered when constructing our\ntraining and testing datasets are 1) a range of observation and illumination geometries that reflect\nthe properties of the CTX images used by the global mosaic and 2) a range of image quality states,\nsuch as haze and oversaturation. We point out that the geographic distribution of CTX images used\nby earlier work that looked into the automated detection of slope streaks such as Wang et al.\n(2017) is nearly identical to our selection. For those reasons we are convinced that our training and\ntesting datasets are indeed globally-distributed – and more importantly – globally representative.\nWe added a new table to the supplementary information that details the various illumination and\nimaging geometry properties of both the training & testing dataset as well as the scanned pre-v01\nversion global CTX mosaic images (Table S3) to underline our approach and argumentation with\nactual data:\nCTX mosaic (pre-v01). CTX images are openly available here: https://viewer.mars.asu.edu/viewer/ctx.\nDataset Property Min. value Max. value Median\nSpatial resolution 5.4 m/pix 6 m/pix 5.4 m/pix\nEmission angle 0.1° 13.1° 4.4°\nTraining & testing dataset Incidence angle 34.9° 68.7° 47.2°\nPhase angle 29.8° 70.3° 48.3°\nSolar longitude 15° 350° 112°\nSpatial resolution 4.9 m/pix 12.5 m/pix 5.4 m/pix\nEmission angle 0° 52.3° 1.4°\nPre-v01 global CTX mosaic\nIncidence angle 28.7° 91.4° 54.2°\n(Dickson et al., 2018,2024)\nPhase angle 2.6° 112.3° 54.4°\nSolar longitude 0° 360° 143°\nConcerning Few-Shot Learning\nAI techniques have problems to rapidly generalize from a few examples. In addition, AI methods rely\non learning from large-scale data. The question of how to find models of categories from very few\ninstances has been discussed for more than twenty years and has recently found renewed interest in\nthe literature due to the success of large-language models. Few-Shot Learning is of course of\nparticular interest in cases where we have very few images which can be used as training examples.\nWhen it comes to SS the question must be asked whether Few-Shot Learning really brings an\nadvantage in view of the fact that with the collection of high-quality Mars images available we have\nhundreds of thousands of very easy recognizable SS at hand. It may be that Few-Shot Learning really\nis able to find new categories of SS not observed before, but it may also be possible that a Few-Shot\nLearning approach may severely underperform a conventional approach to building an image\ncollection for training the CNN. Keeping in mind that the authors want to draw some far-reaching\nconclusions from their research, the reviewer is surprised to see that they just assume that Few-Shot\nLearning is the way to go. If they want to use Few-Shot Learning they need to bring forward the\nevidence that it is useful in this particular case (the reviewer has looked at the cited papers of the\nauthors).\nAs the authors to not communicate their reasoning for choosing their approach, and given the\nrelevance of the training set for their investigation they need to outline a sampling plan for the\ninclusion of images and, in particular, need to provide a clear-cut definition of what dark and bright\nmeans. They also should make the training data set (the 1007 and 147 DDS and BSS) available to the\ncommunity.\nOur reply: Pre-trained object detectors are able to achieve remarkable performance even when\ntrained with extremely few labels, see e.g. https://doi.org/10.48550/arXiv.2203.04291 - in\naddition, CTX might have imaged hundreds of thousands of slope streaks (as our work proves for\nthe first time), but manually labeling a substantial number of those slope streaks in images is\nextremely time-consuming. We invested a significant amount of time to create a very large training\nand testing dataset with 1200 individual features extracted from 40 CTX images, which is notably\nlarger than the dataset created and used by e.g. Wang et al. (2017, 888 labels in 28 images).\nOur usage of the term “few-shot learning” is meant to communicate the fact that we employ a\ndeep learning method, but without having access to the very large label datasets usually used for\ndeep learning models (with hundreds of thousands of labels). The studies referenced in the\nmanuscript have proven numerous times that a few-shot learning approach – with as few labels for\n16\ntraining of a detector as 61 labels (Bickel et al., 2024b, https://doi.org/10.1038/s41597-024-03685-\n3) – can be used to successfully map geomorphic and atmospheric features across various planets,\nas referenced in the manuscript. We added a new sentence to the Online Methods section that\nspecifies our usage of the term “few-shot learning”:\nHere, the usage of the term ‘few-shot learning’ accounts for the fact that the number of slope streak\nlabels available for training and testing is lower than in traditional deep learning which commonly uses\ntens of thousands of labels. Importantly, past studies have demonstrated that a few-shot learning\napproach can successfully map geomorphic and atmospheric features across various planetary bodies\nand datasets (Bickel et al., 2020b; 2022; 2023; 2024a,b; Conway, Bickel et al., 2024; Ruesch & Bickel,\n2023; Mills et al., 2024).\nWe will make our training and testing datasets available to the public after acceptance of the\nmanuscript, on the University of Bern’s BORIS archive (in addition to the slope streak dataset).\nSeparability of individual SSs\nAs the author show in Fig. 9, SSs can manifest themselves as isolated features on a slope or in groups,\nwhere individual SSs can be very close. This is visible when one compares patch 3 with patch 6 in Fig.\n9. It makes obviously a difference for the statistical consideration whether many SSs which are close\nto each other are counted as individual streaks, or a whole group is detected as a single SS.\nComputing the mean number of streaks in the boxes may be an oversimplified way to estimate the\nreal number of streaks, as in many cases SS are not fully contained in the bounding boxes. The\nreviewer has not found any discussion on the separability of individual SSs and what is required for\ntheir separability. How this can affect the different research questions they want to analyze in the\npaper is not adequately discussed.\nOur reply: We added a paragraph to the Online Methods section:\nMultiple, spatially co-located slope streaks are not always easily discernible, such as visualized by Fig.\n1A,B, S7. We note that the absolute counts of individual slope streaks as derived in this work might\nunder- or over-estimate the actual number of slope streaks in a given region.\nBickel & Valantinas evaluate the performance of the trained detector on the testing dataset using\nthree common metrics: recall, precision and average precision AP, and the mean average precision for\nthe two DSS and BSS classes. They seem to be satisfied with reporting their identified metrics,\nincluding numerous confidence scores. They use these numbers to compute the actual number of SS\non the surface at the available image resolution. They do not discuss at all what needs to be taken\ninto account when dealing with the special class of images they investigate. There is no “gold\nstandard” collection of DSS and BSS images against which they can compare how good their\ndetection algorithm is (like the CIFAR, VOC, COCO, etc. image datasets with their clearly defined\nobject classes which have to be detected). Their metric is tied to their own training set. If they want\nto demonstrate that their detector has the capabilities to be used for the task given, it would be\nmuch easier to choose a few selected diverse regions on the surface of Mars, run the detector, and\ncompare the detector’s finding with other methods (be it human and/or another image related method),\nwhich nobody can dispute.\nOur reply: As the reviewer states, there is no community benchmark we could use to validate our\ndetector. There is only one study that looked into the automated detection of slope streaks on\nMars (Wang et al., 2017) but the authors did not make their data available to the public. This\nforces us to create and use our own, independent benchmark. We hope that our training and\ntesting data, once public, can serve as a benchmark for future studies, however.\nmanual surveys of slope streaks across Mars (e.g. Fig. S2, Schorghofer et al., 2002; Aharonson et\nal., 2003; Valantinas et al., 2021), as discussed in the manuscript, which suggests that the detector\nperforms very well.\nBelow, we provide two screengrabs of the (bright and dark) slope streaks mapped by Valantinas et\nal. (2021, orange hexagons) using a grid-mapping approach in Arabia Terra and the streaks\ndetected by our workflow (black dots), which show excellent agreement. We note that the CNN-\nderived dataset is significantly more accurate and complete, such as evident by e.g. the slope\nstreak detections in the southern part of Arabia (all manually verified), which are not included in\nValantinas et al.’s manually derived catalog:\n[REDACTED]\ncatalogue), the reviewer has applied three different approaches. First, coordinates of the DDS from\nthe catalogue were projected onto the CTX mosaic (V1) available on the ACT Mars platform and also\non parts of the Fully Controlled 6 Meters per Pixel Equatorial Mosaic of Mars (Robbins et al., 2023);\nsecond, it was investigated how the information in the catalogue compares to published case\nstudies (e.g. Stillman et al., 2024, CTX image N11_066613, many more published cases were looked at)\nand thirdly, areas where CTX images and HiRise images are simultaneously available were compared.\nThe biggest problem arose from the fact that a huge number of DSS locations were found where it is\nextremely difficult to associate the location as given in the catalogue with the location where the DSS\nare visible on the images. A proper quantitative analysis was not possible for the requested longitude\ninterval as not all thumbnails were provided by the authors.\nOur reply: We are not able to follow the various verification steps conducted by the reviewer,\nunfortunately. However, it is not surprising that our detections of slope streaks that were derived\nfrom map-projected CTX image data do not perfectly match the locations of their respective slope\nstreak counterparts in other derived products and mosaics such as the fully controlled global CTX\nmosaic (which we would like to note was not available at the time of data processing). Slight\nspatial offsets can result from various issues, such as mosaic stitching, image projection\ninaccuracies, et cetera. When using our dataset in combination with the Dickson et al. (2024)\nmosaic the spatial agreement is exceedingly good, such as evident from the screengrab below\n(Tharsis, black dots are streak detections, note the minimal spatial offset for some of the streak\ndetections):\nWe would like to stress that spatial offsets of a few tens of meters do not affect any of the global-\nscale science results, statistics, and interpretations of this study in any way, especially as many of\nthe used auxiliary datasets like topography consist of pixels with sizes of hundreds of meters or\neven kilometers.\nThis part of the manuscript is one of the weakest sections of the paper. The reviewer could live with\nsome of the statements made in this part if the paper would be a case study but not in view of the far-\nreaching conclusions they want to derive from their observations. They base their comments on the\nquality of the catalogue, the CTX images used, the performance of the detector, and the quality of the\nexpert review. On the issues with images and the performance of the detector the reviewed\nhas already commented above, but not on the candidate and expert review.\nWhen the authors write: “The testset precision of the detector indicates that all detections made by\nthe detector should be correct. However, past experience with object detectors being applied to\nextremely large and heterogeneous orbital image datasets has shown that the testset performance is\nnot always fully representative of the ‘real world’ performance of the detector in the full dataset, due\nto the inherently limited representability of the strictly size-constrained training and testing”, then\nthey implicitly indicate that they agree with what the reviewer has written about using Few-Shot\nLearning and a missing sampling plan to acquire the necessary training images and the use of the\nstandard metrics.\nOur reply: The cited statement in the manuscript reflects a well-documented phenomenon of\napplications of deep learning to extremely large planetary remote sensing datasets. Past surveys\nusing deep learning to map features in extremely large image datasets on the Moon, Mars,\nMercury, and other bodies have repeatedly shown that the testset performance is usually better\nthan the ‘real world’ performance, i.e., the performance of the detector in the full image datasets,\nwhich might feature hundreds of thousands of images or more. The reason for the observed drop\nof performance is the fact that no training and testing dataset with ~hundreds to ~thousands of\nlabels is possibly able to fully reflect all the geomorphic features, terrains, image artefacts, and\nvarious oddities (cosmic ray, bit flips, transmission errors, sensor noise, etc.) contained in a real,\nglobal, multi-decade image dataset. We include the cited statement in the manuscript to make a\nreader aware of that important fact.\nWe note that once a first global scan is completed, as represented by this work, that earlier\nknowledge gap concerning many of the above mentioned artefacts and oddities is closed, which in\nturn will uniquely enable future, better, and more accurate mapping efforts.\nThe authors write “We do not find any direct or indirect evidence for any negative effect of the\nscanned CTX images on the mapping outputs, such as potentially caused by cloud cover, shadowing,\nhaze, and data artifacts. We point out that Dickson et al. (2023) carefully selected the images that\nmake up the global mosaic to minimize any such negative impacts”. Cloud cover, dust storms, etc. are\nall seasonal effects. As the authors have looked in their investigation for these effects and could not\nfind any, the reader is interested that they share their findings with her/him and the reader would\nalso probably like to learn how they could conclude this.\nFurthermore, the authors write: “The extremely wide range of slope streak morphologies identified by\nthe detector (Fig. S1) provides evidence for a robust and capable detector”. The reviewer needs to\nremind the authors that you can have a detector capable of seeing a diverse variety of features\n(classes) but which can completely underestimate/overestimate specific categories of morphologies.\nOur reply: The cited statement is our way of sharing the results of our manual, in-depth search for\npotentially negative effects of CTX image quality on our mapping results (see our reply to a similar\ncomment above). We did not identify any such negative impacts, despite significant efforts, as\nreported in the manuscript.\nseem to contradict some of their own earlier statements made in their manuscript or alternatively\nwere not able to express what they wanted to communicate to the reader:\n“The increased false positive rate as achieved by the bright streak detector (Fig. 9C) can likely be\nattributed to a) the smaller training dataset, which in turn is caused by the relative scarcity of bright\nstreaks (as noted by other authors, such as, e.g., Schorghofer et al., 2007; Valantinas et al., 2021),\nand b) the more subtle radiometric contrast between bright streaks and the surrounding slope\n(compared to dark streaks). Notably, the preferably more equatorward distribution and W-facing\norientation of bright slope streaks is likely to be attributed to a bright slope streak detector that is\nmore successful in retrieving bright streaks on well-illuminated, Sun-facing slopes, given the very faint\nappearance of bright slope streaks (Fig. 1A,C) and MRO’s sun-synchronous orbit (imaging takes place\nin the early local afternoon)”.\nIt is not clear at all to the reviewer how the above-manuscript section can fit with their description of\na “perfect and robust” detector.\nOur reply: The term “perfect” is not used in the manuscript at all. The term “robust” is specifically\nused for the dark slope streak detector, not the bright slope streak detector:\nWe do not expect any observational bias for the dark slope streak class; the extremely wide range of\nslope streak morphologies identified by the detector (Fig. S1) provides evidence for a robust and\ncapable detector.\nWe are convinced that usage of the term “robust” is perfectly appropriate for the dark slope streak\ndetector, due to its outstanding testset performance as well as its global mapping results that very\nclosely resemble the dark slope streak bearing regions identified by earlier surveys, among other\nreasons (see replies above).\nThe term “robust” is used a second time, in another paragraph, where it relates to the\nperformance of the overall processing pipeline, which includes the less capable bright slope streak\ndetector. We agree with the reviewer that the usage of the term “robust” might be inappropriate\nin this particular case – we removed the respective sentence from the manuscript.\nIf the authors write “In order to remove all obvious false detections and ensure the integrity of the\nscientific analysis, we manually review all CNN-identified candidates”, does this mean that the\nauthors have realized that their detection algorithm does not deliver on their expectations?\nThe reviewer also believes that a reader is much more interested in understanding the details on how\nthe authors want to “save” the SS-proposals produced by their network with an “expert review” than\nbeing given all the details on how much time the reviewing process needed.\nOur reply: This particular sentence is simply a description of an important part of our workflow\n(and its justification), the manual review of all slope streak candidates. No human or automated\nmapping is perfect, and there will always be false detections, regardless of human or detector\nperformance. The manual review of all candidates ensures that any such false detections do not\ninfluence any of the scientific analyses, independent of the performance of the detectors.\nWe added a new sentence that provides more information on what the term “manual/expert\nreview” specifically includes:\nThe review consists of a human expert looking at every single slope streak candidate thumbnail, one-\nby-one.\nvisual appearance of streaks, such as wind streaks, linear shadowed topography, dunes, and\nmountain ridges (Fig. S10), i.e., do not suggest a fundamental problem with the detector”,\nindicates that Bickel and Valantinas should really have a look at the paper of Wang, I. et al., 2017 which shows\nwhat a simple adaboost algorithm can achieve in terms of SS recognition.\nBased on the above problems with the methodology used the reviewer has a hard time following\nBickel and Valantinas in their argumentation when they start to identify global-scale, statistically\nsignificant relations to argue for or against the various hypotheses which could generate streaks.\nOur reply: We agree with the reviewer that this specific paragraph is not particularly well-worded.\nAfter careful consideration, we also think the location of the paragraph in the manuscript is not\nappropriate either.\nWe moved the discussion of false positives and their removal to the “Candidate review” section,\nand renamed the subsequent section to “Verification of the reviewed mapping results” to make it\nobvious and intuitive that the dataset is free of false detections at this section of the manuscript.\nThe modified “Candidate review” section now reads:\nCandidate review\nWe note a number of false detections among the bright and dark slope streak detection candidates,\nwhich closely resemble the visual appearance of streaks, such as wind streaks, linear shadowed\ntopography, dunes, and mountain ridges (Fig. S14). Past experience with object detectors being\napplied to extremely large and heterogeneous orbital image datasets has shown that the testset\nperformance is not always fully representative of the ‘real world’ performance of the detector in the\nfull dataset, due to the inherently limited representability of the strictly size-constrained training and\ntesting datasets as well as unknown/unexpected anomalies such as related to imaging artefacts,\nweather-related phenomena (clouds, etc.), and a-priori unknown look-alikes (see, e.g., Bickel et al.,\n2022; Ruesch and Bickel, 2023; Conway, Bickel, et al., 2024a,b). The increased number of false\ndetections as achieved by the bright streak detector (Fig. 9C) can likely be attributed to a) the smaller\ntraining dataset, which in turn is caused by the relative scarcity of bright streaks (as noted by other\nauthors, such as, e.g., Schorghofer et al., 2007; Valantinas et al., 2021), and b) the more subtle\nradiometric contrast between bright streaks and the surrounding slope (compared to dark streaks).\nThe presence of false positives in the output of both detectors underlines the importance of the\nmanual review of all candidates and the removal of all false positives.\n[…]\nMany of the variables they use (e.g., Equivalent Hydrogen (WEH), wind speed, etc.) to correlate their\nSS occurances are quantities which are not only difficult to measure, but rely on many assumptions.\nIn addition these quantities are known at completely different resolution compared to the mapping of\nSSs with the help of the CTX images. For example, WEH determination is in itself an extremely difficult\nprocess which in essence requires the estimation of the soil’s water content from measurements of\nneutrons. In order to map the water abundance in a given subsurface layer from orbital neutron\nmeasurements one has to develop a procedure which restores the water abundance at different\npoints of the soil’s volume on the basis of the measured neutrons (of different energies at different\norbital points). Besides a good model for the detector a model for the atmosphere and a model for\nthe Martian subsurface is needed. These maps which the authors are using were produced as global\nmaps! They have to be used with uttermost care when you look into specific regions.\nCorrelating the SS observations found with wind speeds one should also not forget that the seasonal\nwind intensities are obtained using what is called a “climatology average solar” scenario. Due to the\ncoarse resolution of the General Circulation Model (GCM), small-scale variations in topography\nremain unconsidered in the simulation runs. Values are often estimated for daytime-maximum\n22\nsurface temperatures for a given study site using average solar conditions and typical amounts and\ndistributions of suspended dust in the atmosphere.\nOur reply: We recognize the concerns of the reviewer regarding the assumptions and limited\nspatial resolution of many of the used auxiliary datasets, such as WEH and environmental (MCD)\nparameters. However, these datasets 1) represent some of the most relevant, potential drivers of\nstreak formation, such as wind action, and 2) are the state-of-the-art, or in other words, the best\ndata we currently have access to. We briefly express these deliberations in the manuscript, e.g.:\nNote that the relatively low spatial resolution of most of the global thermophysical datasets might blur\ncertain relations, most prominently for the MOLA slope angle dataset, which largely underestimates\nthe actual slope angle.\nHowever, we agree with the reviewer that there needs to be a specific, detailed discussion of any\npotential negative impact on the scientific results of this work. We added a dedicated paragraph to\nthe “Geostatistical analysis & auxiliary dataset limitations” section:\nImportantly, many of the used global auxiliary datasets feature spatial resolutions several factors\nbelow the size of CTX-detected slope streaks and/or depend on various assumptions and models, such\nas the MCD-derived outputs. For example, the global, km-scale, MCD-derived wind speed maps do not\nfully capture the complex influence of small-scale topography on wind speed and direction. We\nunderline the importance of local, site-specific studies to verify the global-scale observations and\ninterpretations made by this work.\nWe note that the results related to WEH specifically are not vital to the scientific conclusions of this\nwork; they are merely complimentary and were integrated into the manuscript following\ncommunity feedback as provided for some of our preliminary slope streak mapping results.\nConclusion of review\nBickel & Valantinas have taken up an interesting topic and have written an easy to comprehend\narticle with really very appealing illustrations. In view of the far-reaching conclusions, they want to\ndraw from their analysis of CTX images, it would have been much better to produce an unedited CNN\nbased catalogue as a supplement to the paper with all the necessary information produced by the\nCNN (exact coordinates, thumbnails, image ID, confidence, etc.) A second catalogue which is\nthe result of what they call the expert review could have then been released. Based on this information a\ndetailed and reproducible discussion could have been started in which they have a chance to win\nsupport for the various claims they make in their work.\nUnfortunately, the authors use many of the data sets which they need in their argumentation too\n\"literally\". I would recommend that they include some colleagues in their work who have worked on\nthe analysis of nuclear spectroscopic methods.\nIn the present form the article does not provide the reproducible evidence for the conclusions they\ndraw. I suggest to the authors that they use part of their work to investigate their hypotheses on a\nmore local scale where they are able to check their findings in more detail (many of the papers in their\nreference list have done this).\nOur reply: In response to your concern we now made the full, expert-reviewed dataset openly\navailable in the University of Bern’s BORIS archive, which includes the exact image coordinates of\ndetections, their respective confidence scores, etc., as requested: https://doi.org/10.48620/75966\nWe are convinced that an expert-reviewed and science-ready catalog offers the greatest scientific\nvalue and reproducibility. This curated dataset can be directly overlaid on the Dickson global CTX\n23\nmosaic for independent verification and scrutinization, providing researchers with the most\nreliable foundation for further analyses.\nWe acknowledge the suggestion about adding further nuclear spectroscopy expertise, but\nunderline that the scope and methodology of our study focuses primarily on geomorphological\nmapping and geostatistical analyses. The WEH results merely serve as complementary supporting\nevidence rather than primary conclusions and were integrated into the manuscript following\ncommunity feedback as provided for some of our preliminary slope streak mapping results, as\ndetailed above.\nWe would like to underline that our study represents a truly holistic, thorough, complete, global-\nscale, and geostatistical analysis of the relation of slope streak distribution and the potential\ndrivers of their formation. Thus, by definition, this study needs to include an extremely wide and\nheterogeneous range of global-scale datasets, including modelled MCD outputs which rely on\nvarious assumptions. While many of those datasets might not be fully representative at the slope\nstreak-scale, they represent the state-of-the-art; in other words, these datasets are the best we\ncurrently have available. In addition, all of the concerns put forward by the reviewer regarding the\nmethodology such as duplicate detections or image quality issues – if present at all – would not\nsystematically affect the results of this work.\nFurther, we would like to stress that the results of this work are fully reproducible – the used\nobject detector and data are openly available, and all plots in the manuscript can be reproduced\nusing the associated dataset and the various, publicly available auxiliary datasets.\nThis work represents a significant leap forward, lifting slope streak-related research from an\nimage-level (thousands of streak-bearing images) to an individual streak-level (hundreds of\nthousands of individual slope streaks). We integrated substantial revisions of the text, figures, and\ntables in both the main text, Online Methods section, as well as the supplementary information,\nand – notably – slightly exceed the maximum permissible word count for the Online Methods\nsection. We hope that our revisions can address all of the reviewer’s concerns about our dataset\nand the associated results and interpretations.\n24"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you for providing thorough responses to all reviewer comments. I am satisfied with the changes made, or rationale for keeping certain statements in the manuscript, with appropriate caveats. I recommend this work for publication without further review.", "reviewer_3": "Positive is that with the revised manuscript comes now the requested data catalogue including the thumbnails.\nHowever, most questions which the reviewer asked are not really answered in the sense that authors investigated the impact on their conclusions which they have drawn from the distribution of the streaks.\nI understand that answering some of the raised questions would mean having to do some additional work.", "reviewer_4": "General overview:\nStreaks on Martian slopes are dark albedo features that form spontaneously and fade over time. In this paper, the authors use YOLO v5 to create a global catalogue of Martian slope streaks and challenge the wet streak formation model through global geostatistical analyses, presenting new evidence for dry streak formation.\nThe mechanism of Martian slope streak formation has been a focus of research. Whether the formation of these streaks is related to water or brine has important implications for understanding the current habitability of Mars. In addition, the presence of liquid water or brine on the surface of Mars would raise serious concerns for planetary protection.\nThe researchers selected 40 images from the globally distributed MRO CTX images and cropped them into image patches of about 1000x1000 pixels for creating the training dataset. In the training, they labelled a total of 1007 dark streaks and 147 light streaks. The YOLOv5x CNN was fine-tuned using the training dataset and the model performance was evaluated using the test dataset. Metrics such as recall, precision and average precision (AP) were used for performance evaluation.\nThe researchers identified 13,026 bright stripes and 484,019 dark stripes, which are distributed across multiple regions of Mars. The streaks are mainly located at low latitudes on Mars, and the dark streaks are systematically shifted northward relative to the light streaks. Some interesting findings are presented such as a weak linear relationship between stripe length and width, and the frequency distribution of stripe area follows a power-law distribution. The results challenge the wet-mechanism model of stripe formation associated with liquid or saline water, and propose a dry-mechanism model in which stripe formation is driven by seasonal dust transport and energetic triggers.\nThe study provides comprehensive insights into the mechanisms of Martian slope streaking, emphasizes the dominant role of dry mechanisms in streak formation. The results contribute to a better understanding of the climate, surface evolution and geological activity of Mars.\nFollowing are some details:\n1. There might be no standard for distinguish bright streaks and dark ones. Are there any ambiguous situation encountered while establishing the training set as well as implementing the detection? Or maybe albedo value can be used as criteria.\nDetection of dark streaks performs significantly better than bright ones on recall and precision. If the criteria changes, e.g. the albedo value to distinguish bright and dark streaks varies, the detection performance may change accordingly.\n2. Sometimes the bright and dark streaks appear in piles so close together that it is difficult to separate them one from the other when identifying them. Comparing to other deep learning algorithms, Yolo 5 has a balanced precision and speed. As the authors stated in the manuscript, there might be several slopes streaks in one bounding box, therefore reasonable estimation of the real number of slope streaks has been made. And for the duplicate detection that may appear along the seams of individual images. Does this mean the 1000X1000 pixel patch images or each CTX image? For patch images, the duplicate detection can be found and removed by NMS (Non-Maximum Suppression). However, NMS can not solve the duplicate detection if it is caused by adjacent CTX images with displacement due to orbital errors and image rectification errors (or not even been rectified). Please note that the orbital errors vary along latitude. Usually error increases with latitude. Undoubtedly this will bring new problems that will require an overall adjustment of the CTX images used in this manuscript, which of course is not the main focus of this paper. For the purposes of this paper, the results of the analysis are plausible if the duplicate detections have been analyzed and quantitatively predicted.\nOne interesting point is that for the estimation of the slope length, width and area, how to distinguish multiple detections in one bounding box?\n3. Line 474-476. The paper introduces how to correct false detections by checking every thumbnail by expert. However, the missing ones are not in one of these thumbnails. What is the strategy to remedy and supplement the missed slope streaks? If a significant number of results are missed, (obviously the bright slope streaks are easier to be missed due to the lower recall), this can challenge the analysis results.\n4. Please check Fig.10 caption. The description of Fig.10 A does not match the real figure."}, "author_response": "Response to Reviewers\nWe thank the reviewers for their detailed and constructive feedback. We carefully considered all\ncomments and revised the manuscript and supplementary information accordingly (text and figures).\nChanges in the manuscript are highlighted in pink color (in addition to the original changes in green\ncolor).\nPlease find our replies (blue bold) to the reviewers’ comments (italic) below.\nAdditionally, we have updated several references throughout the manuscript (e.g., for manuscripts\nthat were recently accepted or published) and added a new supplementary figure.\nValentin Bickel (on behalf of all co-authors)\n1\n--- --- ---\nGeneral updates\n- We added one new supplementary figures that showcases recently-acquired HiRISE and\nCTX images that represent examples of a newly-formed impact crater as well as nearby\nslopes with newly-formed slope streaks (in Cerberus Fossae):\n2\n--- --- ---\nReviewer #1\nThank you for providing thorough responses to all reviewer comments. I am satisfied with the\nchanges made, or rationale for keeping certain statements in the manuscript, with appropriate\ncaveats. I recommend this work for publication without further review.\nOur reply: We thank the reviewer for their assessment and positive feedback.\n3\n--- --- ---\nReviewer #3\nPositive is that with the revised manuscript comes now the requested data catalogue including the\nthumbnails.\nHowever, most questions which the reviewer asked are not really answered in the sense that authors\ninvestigated the impact on their conclusions which they have drawn from the distribution of the\nstreaks.\nI understand that answering some of the raised questions would mean having to do some additional\nwork.\nOur reply: We thank the reviewer for their assessment and feedback. We are still convinced that\nwe addressed all of reviewer #3’s concerns and that both our method and scientific results are\nrobust and relevant.\n4\n--- --- ---\nReviewer #4\nGeneral overview:\nStreaks on Martian slopes are dark albedo features that form spontaneously and fade over time. In\nthis paper, the authors use YOLO v5 to create a global catalogue of Martian slope streaks and\nchallenge the wet streak formation model through global geostatistical analyses, presenting new\nevidence for dry streak formation.\nThe mechanism of Martian slope streak formation has been a focus of research. Whether the\nformation of these streaks is related to water or brine has important implications for understanding\nthe current habitability of Mars. In addition, the presence of liquid water or brine on the surface of\nMars would raise serious concerns for planetary protection.\nThe researchers selected 40 images from the globally distributed MRO CTX images and cropped them\ninto image patches of about 1000x1000 pixels for creating the training dataset. In the training, they\nlabelled a total of 1007 dark streaks and 147 light streaks. The YOLOv5x CNN was fine-tuned using\nthe training dataset and the model performance was evaluated using the test dataset. Metrics such\nas recall, precision and average precision (AP) were used for performance evaluation.\nThe researchers identified 13,026 bright stripes and 484,019 dark stripes, which are distributed across\nmultiple regions of Mars. The streaks are mainly located at low latitudes on Mars, and the dark\nstreaks are systematically shifted northward relative to the light streaks. Some interesting findings\nare presented such as a weak linear relationship between stripe length and width, and the frequency\ndistribution of stripe area follows a power-law distribution. The results challenge the wet-mechanism\nmodel of stripe formation associated with liquid or saline water, and propose a dry-mechanism model\nin which stripe formation is driven by seasonal dust transport and energetic triggers.\nThe study provides comprehensive insights into the mechanisms of Martian slope streaking,\nemphasizes the dominant role of dry mechanisms in streak formation. The results contribute to a\nbetter understanding of the climate, surface evolution and geological activity of Mars.\nOur reply: We thank the reviewer for their detailed assessment and constructive feedback.\nFollowing are some details:\n1. There might be no standard for distinguish bright streaks and dark ones. Are there any ambiguous\nsituation encountered while establishing the training set as well as implementing the detection? Or\nmaybe albedo value can be used as criteria.\nDetection of dark streaks performs significantly better than bright ones on recall and precision. If the\ncriteria changes, e.g. the albedo value to distinguish bright and dark streaks varies, the detection\nperformance may change accordingly.\nOur reply: Slope streaks form as dark albedo features and turn bright over time, i.e., over years\nand decades, before fading entirely. In addition, both the slope angle and the geometry of the\nacquired image affect the visual appearance of slope streaks. This means that the transition\nbetween dark and bright streaks is continuous – and that there is no clear, universally/globally\napplicable, quantitative way to separate “dark” from “bright” (especially in CTX data).\nWe clarify this important point in section 2:\nstreaks refer to old, partially-faded streaks that appear bright in CTX images (Fig. 1A,1B).\nFurther, examples of “bright” and “dark” streaks are displayed in Figure 1A and 1B.\nThe used training and evaluation data clearly separates bright and dark slope streaks – as\nthoroughly as possible –, carefully applying the separation introduced above and showcased in\nFigure 1. In response to your comment, we added a new remark to the methods section to\nunderline this fact:\nDuring labeling, we carefully separate bright and dark slope streaks following the definition provided\nin the main text and showcased in Fig. 1.\n2. Sometimes the bright and dark streaks appear in piles so close together that it is difficult to\nseparate them one from the other when identifying them. Comparing to other deep learning\nalgorithms, Yolo 5 has a balanced precision and speed. As the authors stated in the manuscript, there\nmight be several slopes streaks in one bounding box, therefore reasonable estimation of the real\nnumber of slope streaks has been made. And for the duplicate detection that may appear along the\nseams of individual images. Does this mean the 1000X1000 pixel patch images or each CTX image?\nFor patch images, the duplicate detection can be found and removed by NMS (Non-Maximum\nSuppression). However, NMS can not solve the duplicate detection if it is caused by adjacent CTX\nimages with displacement due to orbital errors and image rectification errors (or not even been\nrectified). Please note that the orbital errors vary along latitude. Usually error increases with latitude.\nUndoubtedly this will bring new problems that will require an overall adjustment of the CTX images\nused in this manuscript, which of course is not the main focus of this paper. For the purposes of this\npaper, the results of the analysis are plausible if the duplicate detections have been analyzed and\nquantitatively predicted.\nOne interesting point is that for the estimation of the slope length, width and area, how to distinguish\nmultiple detections in one bounding box?\nOur reply: Our analysis of potential duplicate detections exclusively refers to CTX image seams, not\ntile seams, as described in the methods section:\nDue to the potential for geo-location and co-registration inaccuracies as well as partial overlap of the\nCTX images that make up the pre-v01 version of the global CTX mosaic (pers. comm. J. Dickson) there\nis a small chance that slope streaks located along the seams of individual images are detected multiple\ntimes (‘duplicates’).\nAs the reviewer suspects, our workflow already uses NMS, as is common and best practice in\ncomputer vision. We added a specific mention of NMS to the revised manuscript:\nWe use YOLOv5’s default Non-Maximum-Suppression (NMS) algorithm to remove duplicate\npredictions in each patch, following computer vision best practice.\nWe thank the reviewer for acknowledging our approach to quantifying the error that is associated\nwith potential duplicate detections along the seams of CTX images. As the reviewer suggests, any\nfuture scans of the martian surface will use the new global CTX mosaic, which will help remove any\npotential duplicate detections. We note that, unfortunately, this global mosaic was not yet\navailable when we conducted the scan for this particular manuscript.\nAs detailed in the methods section, we rest our estimate of streak length, width, and area on a)\nbounding box sizes predicted by the neural network which were calibrated with b) manually\nmeasured (single) streak properties, i.e., using a statistical approach. For bounding boxes with\n6\nseveral streaks, the longest streak will dominate the size of the bounding box, which means that\nour size estimates might not be entirely representative for bounding boxes that contain multiple\nstreaks with distinct length differences. We added this note/caveat to the manuscript:\nAs the size of each bounding box is dominated by the largest contained streak, this relation might not\nbe entirely representative for bounding boxes that include multiple streaks with distinct streak length\ndifferences.\n3. Line 474-476. The paper introduces how to correct false detections by checking every thumbnail by\nexpert. However, the missing ones are not in one of these thumbnails. What is the strategy to remedy\nand supplement the missed slope streaks? If a significant number of results are missed, (obviously the\nbright slope streaks are easier to be missed due to the lower recall), this can challenge the analysis\nresults.\nOur reply: We showcase a selection of false detections (=false positives) in the supplementary\ninformation, Fig. S15, also attached below for convenience.\nAs a fundamental issue to any object detection task, the number of false negatives, i.e. the number\nof missed detections, will always remain unknown – unless a human expert would carefully map\nevery single slope streak on Mars, which would take several years, if at all possible (which is\nprobably why it has not been done in the past). It is best practice to assess the number of false\nnegatives during the evaluation of the detector, using a testset. The metric of choice is “recall”,\nas detailed in the manuscript:\nWe evaluate the performance of the trained detector using the testing dataset and the three most\ncommon metrics used for object detector evaluation: recall (i.e., the number of detected streaks, e.g.\n8/10), precision (i.e., the number of correctly detected streaks, e.g. 9/10), and average precision (AP,\nrelation of recall and precision over a wide range of model confidence scores, i.e., the area under the\nprecision-recall curve, which summarizes the performance of a detector in one single number, Fig. 9C).\nDue to the extreme amount of slope streaks on Mars, and due to our requirement of wanting to\nverify every single detection to ensure the robustness of the scientific analysis, we choose a model\nconfidence score of 0.8, which helps to limit the number of overall detections that need to be\nreviewed, as described in the manuscript. At a score of 0.8, the recall of our detector is ~20 % for\nbright and ~55% for dark streaks, which implies that the detector is likely missing several bright\nslope streaks (= difficult to detect due to the poor contrast to the surrounding) but should identify\nthe majority of dark streaks. Importantly, the performance of the detector is always identical (=\n“consistent”), and the appearance of slope streaks is very similar across Mars (see Fig. S1),\nstrongly\nsuggesting that the recall of the detector does not negatively affect any of the scientific results\npresented in this study. In other words, the detector consistently samples slope streaks across the\nplanet (with the exact same recall and performance), meaning that we get a very robust, global,\nstatistical sample. We added further clarification to the manuscript:\nThis indicates that the detector can be expected to make none to very few mistakes, while being able\nto (consistently) identify about 20 and 55 % of all bright and dark slope streaks observed by CTX, at a\nconfidence score of 0.8 and higher.\n7\n4. Please check Fig.10 caption. The description of Fig.10 A does not match the real figure.\nOur reply: Thank you for pointing the caption of Fig. 10 out to us! We corrected the caption:\nFigure 10 | Schematic of statistical analysis of streak and seismic/impact event co-location. A) schematic map of\nstreaks (black dots, n = 22), impacts (blue, n = 4), and synthetic impacts (orange, n = 4); B) assessment of\nminimum distance between streaks and (real/synthetic) events; C) assessment of number of streaks in n° bins\naround (real/synthetic) events. A relation between an impact/seismic event and streaks would result in smaller\nminimum distances and increased numbers of streaks closer to the events. D) real example from Arabia Terra,\nincluding bright (white dots) and dark streak detections (black dots); diameter of circles does not properly reflect\nthe 0.5, 2, and 5° radii used throughout this study. Note how the individual streak detections cluster along\ntopographic inclines, mostly along crater walls. Viking merged color mosaic in the background.\n8"}]

Decision letter