# Robust automatic crater detection at all latitudes on Mars with Deep-learning

Martinez Leonard, François Andrieu, Frédéric Schmidt, Hugues Talbot, Bentley Mark

► To cite this version:

Martinez Leonard, François Andrieu, Frédéric Schmidt, Hugues Talbot, Bentley Mark. Robust automatic crater detection at all latitudes on Mars with Deep-learning. Planetary and Space Science, 2025, 260, 10.1016/j.pss.2025.106053. hal-04907508

HAL Id: hal-04907508

https://hal.science/hal-04907508v1

Submitted on 22 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Robust automatic crater detection at all latitudes on Mars with deep-learning

L. Martinez<sup>a,∗</sup>, F. Andrieu<sup>a</sup>, F. Schmidt<sup>a,b</sup>, H. Talbot<sup>c</sup>, M.S. Bentley<sup>d</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Dataset link: data link, https://doi.org/10.14768/ae042dbb-37c3-4a98-b72c-02d2a191f53b

**Keywords:**

Detection algorithm

Impact craters

Computer vision

Artificial intelligence

## ABSTRACT

Understanding the distribution and characteristics of impact craters on planetary surfaces is essential for unraveling geological processes and the evolution of celestial bodies. Several machine learning and AI-based approaches have been proposed to detect craters on planetary surface images automatically. However, designing a robust tool for an entire complex planet such as Mars, is still an open problem. This article presents a novel approach using the Faster Region-based Convolutional Neural Network (Faster R-CNN) for such a detection. The proposed method involves the pre-processing, training and crater detection steps, which are especially designed for robustness regarding latitude and complex geomorphological features. The objectives of this studies are to (i) be robust at all latitudes and (ii) for $\ge$ 1 km diameter crater sizes. (iii) To propose an open-source and re-usable algorithm that (iv) only needs an image to run. Extensive experiments on high-resolution planetary imagery demonstrate excellent performances with an average precision $AP_{50} > 0.82$ with an intersection over union criterion $IoU \ge 0.5$, irrespective of crater scale. For mid and high latitudes (higher than $48^{\circ}$ north and south), performance decreases down to $AP_{50} \sim 0.7$, which is still better than the current state of the art. Loss of performance is mostly due to strong shadowing effects. Our results also highlight the versatility and potential of our robust model for automating the analysis of craters across different celestial bodies. The automated crater detection tool presented in this article is publicly available as open-source and holds great promise for future scientific research of space exploration missions.

## 1. Introduction

Planetary surfaces undergo constant evolution driven by geological phenomena such as impacts (Leighton et al., 1965) or volcanic eruptions (Carr, 1973-07). By analyzing the distribution and the density of crater using the “crater counting” approach, it is possible to estimate the age of the planetary surface (Hartmann and Neukum, 2001; Neukum et al., 2001). This age is an valuable insight into the geological history and evolution of planetary surfaces (Benedix et al., 2020). Understanding the distribution of impact craters is thus crucial for comprehending the geological processes (Hartmann, 2005), including tectonic deformation, volcanism, erosion, transport, and impact cratering itself (Hartmann and Neukum, 2001).

Historically, crater detection has relied on manual analysis. For Mars and the Moon, in particular, several manuals databases exist (Head III et al., 2010; Robbins, 2019; Robbins and Hynek, 2012). Even with corrective analysis (Lagain et al., 2021), manual databases are all subject to human limitations. Indeed, studies have shown that human attention to repetitive tasks, such as crater counting, rapidly

decreases after 30 min (See et al., 1995), inducing errors. Moreover, human crater counting has other limitations such as subjectivity and limited scalability. These limitations can be overcome by employing approaches based on Machine Learning (ML).

For this reason, crater detection using computer vision methods has been widely explored in the last decade or so. A proof of concept was described in Vinogradova et al. (2002), achieving good results limited by the image resolution. Some studies tried to use the geometry of the problem as a criterion for detection such as Salamuniccar and Loncaric (2010), Galloway et al. (2014). This method is interesting because it does not require much computational resources but it gives rise to two problems. The first is that while it can be accurate to detect relatively fresh craters, it remains a challenging task to detect eroded craters (Salamuniccar and Loncaric, 2010). Secondly, these methods cannot make the difference between a crater and another geomorphological circular such as calderas or scoria cone that are present at the surface of Mars (Head et al., 2001). Other methods have been suggested using digital elevation models (DEM) such as Di et al. (2014),

<footnote><sup>a</sup> Université Paris-Saclay, CNRS, GEOPS, 91405, Orsay, France</footnote>

<footnote><sup>b</sup> Institut Universitaire de France (IUF), France</footnote>

<footnote><sup>c</sup> CentraleSupelec – Paris-Saclay University –Inria, Gif-sur-Yvette, France</footnote>

<footnote><sup>d</sup> European Space Astronomy Centre, Camino Bajo del Castillo, Urb. Villafranca del Castillo, 28692 Villanueva de la Canada, Madrid, Spain</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: leonard.martinez@universite-paris-saclay.fr (L. Martinez).</footnote>
Zhou et al. (2018), Chen et al. (2018), Lee (2019), Wang and Wu (2019). These methods provided valuable benefits for the community, but require a complex pre-processing step to first create the DEM, requiring multiple views and the use of stereo-vision methods; and/or lidar/radar measurements. In this article, we focus on crater detection from single images. Salamuniccar et al. (2011) (Salamunićcar et al., 2011) identified 77 different works, categorized into image-based and DEM-based approaches. Since then, many more have been proposed, particularly with the advent of deep-learning based algorithms such as R-CNN (Girshick et al., 2014) or YOLO (Redmon et al., 2016) or even simpler models such as pyramidal networks (Yang and Cai, 2022). These have enabled ground-breaking performance in detection, such as in Benedix et al. (2020), La Grassa et al. (2023). These news studies have surpassed the previous generation in performance but some limitations with robustness still remain. In particular, detection rates in latitude higher than 45° north and south have been poorer, as well as in the global planetary scale evaluation. However, these recent techniques are relevant for our objectives, offering the advantage of versatility in detecting a variety of features on planetary surfaces. Notably, deep learning algorithms have been successfully applied to identify linear surface features on Europa (Haslebacher et al., 2023), to detect rockfalls on Mars (Bickel et al., 2020), and to detect ice block falls on the Martian polar caps (Su et al., 2023). Segmentation-based approaches have also been tried, using models such as the U-Net architecture proposed in Bonneberger et al. (2015), originally for biomedical imaging. We can for example cite Silburt et al. (2019), Downes et al. (2020), Latorre et al. (2023) or Ali-Dib et al. (2020) which use a Mask-RCNN architecture developed by He et al. (2017) for this task. Another proof of concept was presented in Giannakis et al. (2024) where authors show that it is possible to use Meta's SAM (Segment Anything Model) (Kirillov et al., 2023) to detect craters. These examples highlight the adaptability and effectiveness of deep-learning based methods across diverse planetary environments and geologic contexts.

This research project focuses on the development of a computer vision algorithm specifically designed to automatically detect craters in planetary surface images in a robust manner. Our algorithm has been designed to adapt to different latitudes and potential false positive geomorphological features, ensuring consistent performance across a range of planetary bodies. In previous such work (Benedix et al., 2020), it was shown that the crater detection algorithm efficiency decrease drastically with an increasing latitude. This problem can be either due to geographical reprojection (equatorial projection strongly affects polar features) but also to variation in illumination condition (due to lower solar elevation near the poles). The difficulties associated with illumination conditions are even more important for sun-synchronous platforms, since each latitude is observed at the very same local time. In this case latitude and solar elevation bias combine.

Furthermore, our algorithm is designed to distinguish craters from other circular features present on planetary surfaces such as calderas (Masson, 1984; Williams et al., 2007), scoria cones (Brož et al., 2014), and lava tunnels (Warner and Gregg, 2003) thanks to a global scale learning dataset.

Finally the scale of the crater seems also a key point that in-
duces bias into the detection algorithm. For instance Benedix et al.
2020 (Benedix et al., 2020) only focus on size 1.5 -10 km range for their
training database. We propose here a tool to detect craters of > 1 km
in diameter.

## 2. Data and methods

In order to detect and classify craters precisely, our machine learn-
ing algorithm needs suitable input data. In this section we will describe
the various data used to conduct this study and we will describe the
preprocessing steps.

2.1. Input images

We built our training dataset using surface images of Mars. Indeed, the complexity and the overall diversity of crater morphologies is unrivaled anywhere else in the Solar System. Thus, by building a crater detection model trained on Mars, we aim to be able to detect craters in every planetary surface in the Solar System (Fig. Sup. Mat. S5 and S6). The second reason why Mars is a privileged planet is that it is one of the most explored extraterrestrial body. Indeed, since the Mariner IV probe first imaged the Martian surface in 1964, the planet has received around fifty more visits.

We used a dataset of high-resolution images of Mars provided by the Context Camera (CTX) onboard the Mars Reconnaissance Orbiter (MRO) (Malin et al., 2007). CTX images the surface of Mars at a pixel resolution of approximately 5 m, enabling detailed observations of the Martian surface. The CTX mosaic used covers 99.5% of Mars from 88° S to 88° N (Dickson et al., 2018).

Above latitudes of 80° north and south and beyond, we encountered significant challenges in our crater detection efforts. This area presents a unique set of obstacles, primarily due to the pervasive presence of a seasonal and permanent ice caps and clouds, even during the summer months (Appéré et al., 2011; Schmidt et al., 2009). These features make the image mosaic of the surface less reliable. In addition, the global crater database contains almost no craters in this region.

Given these challenges we made the deliberate decision to limit our focus to the area between 76° N and 80° S within the CTX mosaic.

## 2.2. Improved ground truth crater database

With learning-based object detection we need to provide a reliable list which references the ground truth positions of objects that we want to detect. For Mars there are already several manual crater databases which classify craters. Among them we use here the Lagain et al. (2021) database, which was improved by manual checking from the Robbins and Hynek database (Robbins and Hynek, 2012). This database is based on the THEMIS global mosaic (Edwards et al., 2011) and consists of the exhaustive list of the 384,343 craters > 1 km diameter on Mars.

By superposing the CTX images with the crater position and radius from this database, we noticed that a relatively large number of craters appeared to be shifted from their expected location, probably due to the relative errors of pointing between CTX and THEMIS. We improve the Lagain et al. crater position and radius using the Hough transform in order to better point the crater with respect to our images. This method enables us to improve the database precision in 96% of the cases.

## 2.3. Preprocessing

Our preprocessing pipeline was inspired by our precedent work on the THEMIS dataset (Van den Bossche et al., 2018). We placed significant emphasis on data preprocessing because training on poor-quality data, which contains biases or inaccuracies, would result in the model incorporating these biases, thereby adversely affecting its performance. To facilitate data download, the CTX mosaic was divided into 3,960 quadrangles. Each quadrangle covering a latitude/longitude range of 4 × 4°. The CTX mosaic is available online (Dickson et al., 2023) in the equidistant cylindrical projection. This projection does not preserve geometry, so the circular shape appear flatten at high latitude. In our study we choose to reproject our data to a local stereoscopic projection in order to conserve crater circularity and to ensure that the model to learn only a single shape. In addition, maintaining a circular geometry is crucial to detect a proper center/radius and it might enable us to correct the issue raised by Benedix and collaborators, 2020, concerning the fact that they observe many false negative detections above 45° in latitude ((Benedix et al., 2020), Figure 1).

A second step of our prepossessing pipeline was to cut the whole-
quadrangle datasets into smaller patches of images of size 224 × 224
<figure><img src="image_4.png" /><figcaption>Fig. 1. Localization of the 3960 quadrangles attributed to train, validation and test datasets.</figcaption></figure>

pixel width, for compatibility with our neural network architecture (Ren et al., 2015). In order to be able to detect only crater above 1 km in diameter, we set up a maximum resolution of 200 m per pixel. To include the widest variety of crater sizes, we also downsampled the quadrangles into 400 meters and 800 meters per pixel patches, which effectively increased the amount of terrain covered within each patch while maintaining the required pixel dimensions. In the end, we obtain 97,423 patches of images that we split into three different datasets:

* Training dataset: 82,874 images (85%)

* Validation dataset: 4828 images (5%)

* Testing dataset: 9721 images (10%)

The training dataset is the one used to train the deep-learning method. The validation dataset is used to monitor the learning phase. The testing dataset is completely independent of the learning phase and is only used to evaluate the performance of the trained algorithm.

We attributed a class (Training, Validation, Testing) randomly to the 3960 quadrangles in a way that all latitude and all geological units are represented in the 3 datasets (see Fig. 1). This ensures a robust training and learning to discard potential false-positive features. In addition, all latitude are present up to 76° N and 80° S, ensuring a robustness to illumination conditions.

In order to design a detector robust to varying illumination and acquisition conditions, we performed a data augmentation step by randomly performing the following transformations, which are implemented using the albumentations library, which is a popular library for image augmentation in machine learning (Buslaev et al., 2020):

* Flip: horizontal, vertical or both flips with a probability of 0.5.

* Random Rotate: rotation by 90, 180 or 270 degrees, with a probability of 0.5

* Motion blur: motion blur with a random-sized kernel, with a probability of 0.2

* Median blur: median blur with a random aperture linear size, with a probability 0.1.

Deep learning algorithms are well-suited for object detection across multiple scales, allowing them to identify objects of varying sizes within an image. However, in practice, it is essential to establish a minimum scale threshold. This is due to the fact that even in the highest-resolution images, there exist craters smaller than 1 kilometer in diameter, which are not included in our ground truth database. Thus,

we exclude all craters that are smaller than 10 pixels in diameter from the training dataset. This size criterion has been consistently applied during the inference phase as well.

This 10-pixel size constraint is motivated by the fact that craters are generally detected via their rim. Experiments with state-of-the art detectors for thin features such as vessels (Xiuqin et al., 2019) have shown that lines thinner than 3 pixels cannot be detected reliably. Therefore, for a circle, a 9 or 10-pixel diameter should be seen as an absolute minimum. By implementing this constraint, we aim to strike a balance between capturing the diversity of crater sizes and maintaining the practical relevance of our detection. It allows us to concentrate our algorithm's training and evaluation on craters of substantial scientific interest while mitigating potential noise from smaller features. As a result, our approach provides a more accurate and relevant assessment of crater detection performance across a range of scales.

### 3. Crater detection algorithm

In this subsection we provide some details about the deep-learning model that we used for the detection task. Then, we explain the training process and give the result of the test step which is essential to evaluate the performances of our algorithm.

#### 3.1. Faster-RCNN

Faster-RCNN is a popular object detection algorithm used in many computer vision tasks (Ren et al., 2015). More recent algorithm, such as YOLO (Redmon et al., 2016), are faster for detections since they are designed for video analysis but not necessarily more accurate. Since computation time is not a limiting factor in this study, we chose Faster-RCNN, which demonstrated better performance in object detection in exchange for a longer computational time (Zaidi et al., 2022). Its main goal is to accurately identify and localize objects within an image. To understand how this object detection algorithm works, it can be decomposed into three stages (see Fig. 2):

1. **Backbone**: A residual Convolutional Neural Network (CNN) is used for the backbone. The first component of Faster-RCNN is a Resnet50 CNN backbone. This model will process the entire input image and extracts high-level features. The CNN backbone typically consists of multiple convolutional and pooling layers with skip connexions, which progressively learns to recognize
<figure><img src="image_5.png" /><figcaption>Fig. 2. Faster R-CNN architecture as described by Ren et al. (2015) with our image configuration.</figcaption></figure>

complex patterns and objects in the image. The purpose of the CNN backbone is to encode the image into a set of feature maps that preserve spatial information (LeCun et al., 1998).

2. **Region Proposal Network (RPN):** The second step of Faster R-CNN involves generating potential Region Of Interest (ROI) in an image that are likely to contain objects. This is done using a RPN which takes an input image and scans it with a set of predefined anchor boxes. These anchor boxes with different sizes and aspect ratios could be considered as local projections. For each anchor box, the RPN predicts whether it contains an object or not, as well as adjusts its position and size to tightly fit the object, if it is present. The RPN generates a set of proposed ROI, called region proposals, based on these predictions.

3. **Detector: ROI Head** This part make the ROI Classification and Bounding Box Regression: The final step involves classifying the proposed ROI and refining their bounding boxes. The feature maps generated by the CNN backbone are used to extract the features for each proposed region. These features are then fed into two separate fully connected layers: one for object classification (determining if the object is a crater) and the other for bounding box regression (refining the location and size of the bounding box). The classification layer produces an index that can be interpreted as a likelihood of membership to an object class for each region proposal, while the regression layer predicts adjustments to the initial bounding box coordinates.

Our Faster-RCNN implementation has 41,299,161 parameters to estimate. For more information, please refer to Ren et al. (2015). It outputs the final set of detected objects: bounding box coordinates and likelihood index.

## 3.2. Training

The general idea of this training process is to teach the model how to recognize craters in images. Each time the model sees a batch of images, it compares its prediction to the ground truth and calculate an error materialized as a loss function. The aim of the training is to optimize the model parameters by minimizing this loss through a gradient descent.

The loss function $L$ is a sum of regression and classification losses. The regression loss (first term) assesses the precision of bounding box adjustments, while the classification loss (second term) measures the accuracy of crater predictions:

$$L(p_i, t_i) = \frac{\lambda}{N_{\text{reg}}} \sum_i p_i^* L_{\text{reg}}(t_i, t_i^*) + \frac{1}{N_{\text{cls}}} \sum_i L_{\text{cls}}(p_i, p_i^*) \quad (1)$$

Where: $p_i$ is the predicted probability of anchor $i$ being an object, $p_i^*$ the ground-truth label (1 if the anchor is positive, else 0), $t_i^*$ the ground-truth box associated with a positive anchor, $t_i$ is a vector representing the four coordinates of the predicted bounding box, $L_{\text{cls}}(p_i, p_i^*) = -p_i^* \log(p_i) - (1 - p_i^*) \log(1 - p_i)$, $L_{\text{reg}}(t_i, t_i^*) = L_1^{\text{smooth}}(t_i - t_i^*)$, $\frac{1}{N_{\text{reg}}} \frac{\lambda}{N_{\text{cls}}}$ have the same value. This way, the two terms of the equation are equally weighted.

To train the RPN, the goal is to assign ROI in such a way to minimize the regression loss value. Anchors are local projections at varying scales and multiple aspect ratios. We used typically $N_{\text{reg}} \approx 2400$ anchors. ROI are computed for each anchor together with a score.

In a training phase, anchors are learned using the ground-truth box using the following scheme:

* Anchors are assigned a positive class label (object) if they meet one of two conditions: (i) They have the highest Intersection-over-Union (IoU) overlap with a ground-truth box. (ii) They have an IoU overlap higher than 0.7 with any ground-truth box (A single ground-truth box might assign positive labels to multiple anchors.).

* Negative Anchors: Anchors are assigned a negative class label (not an object) if they are not positive and their IoU ratio with all ground-truth boxes is lower than 0.3.

* Neutral anchors: these anchors belong neither to an object or to the background and are not taken into account during training.

Then, the classifier learns the class by minimizing the classification loss value, to ensure that the predicted $p_i^*$ is close to the real $p_i$ classification.

The training process is influenced by several hyper-parameters that can affect the loss function or the gradient descent process. In this study, we apply the same weight to every component of the loss function. For the gradient descent, we use the ADAM stochastic optimization algorithm, primarily controlled by the learning rate (LR). The LR, which defines the step size for each iteration, is kept constant at $10^{-5}$. This is a critical factor because a learning rate that is too large can cause the algorithm to learn quickly at first but fail to converge to a relevant minimum of the loss function. On the other hand, a too small learning rate can significantly prolong the training time and may cause the algorithm to become stuck in an irrelevant local minimum.

## 3.3. Evaluation metrics

In order to evaluate our algorithm, we will use the usual precision and recall metrics.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Evolution of the AP<sub>50</sub>, the AR<sub>50</sub> and the F<sub>1</sub> score during the training phase.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Precision as a function of recall for six different IoU thresholds.</figcaption></figure>

The choice of the evaluation metrics was enlightened by Vinogradova & al. in 2002 (Vinogradova et al., 2002). Indeed, in this study, the authors state that “Many pattern recognition algorithms could be applied to this problem [i.e.: detecting craters in an automated way] but a systematic validation process is required to select the best method for each scientific application and to determine its reliability for scientific use.” This is a point which was as it was also underlined by C. Lee in his comparison between Benedix 2020 (Benedix et al., 2020) and Lee & Hogan 2021 (Lee and Hogan, 2021): “The apparent performance of automatic crater detection algorithms is sensitive to the choice of metric used to gauge the performance.”. Indeed, in all those study, authors fine-tune Machine Learning tools and thus a fair comparison of the results require the use of the same training and evaluation method. In our study we decided to take the same evaluation metrics as in previous studies (Benedix et al., 2020; La Grassa et al., 2023; Ali-Dib et al., 2020; Silburt et al., 2019).

<figure><img src="image_8.png" /><figcaption>Fig. 5. Precision vs Recall curves for an IoU = 0.5 and for different bounding box sizes of the test dataset. Each curve represents the precision with respect to the recall for three bbox size. The orange curve corresponds to bbox with a size of 32px² which corresponds to every crater with a diameter of less than 6.4 km. The brown curve corresponds to bbox with a size between 32px² and 96px² which corresponds to a diameter size between 6.4 and 19.2 km. The red curve corresponds to bbox size of more than 96px² which corresponds to a crater size of more than 19.2 km in diameter.</figcaption></figure>

The precision *P* measures the proportion of correctly identified craters among all the objects classified as craters (Eq. (2)):

$$P = \frac{TP}{TP + FP} \qquad (2)$$

True Positives (TP) refers to the number of correctly identified craters, while False Positives (FP) indicates the number of non-crater objects mistakenly classified as craters.

The recall *R* or the sensitivity measures the proportion of correctly identified craters among all the actual craters present in the dataset (Eq. (3)):

$$R = \frac{TP}{TP + FN} \qquad (3)$$
False Negatives (FN) represent the number of craters that were not detected by the algorithm.

Since it is also commonly used in the literature, we have calculated the $F_1$ score, which is the harmonic mean of precision and recall:

$$F_1 = \frac{2 \cdot AP_{50} \cdot AR_{50}}{AP + AR} \qquad (4)$$

To specify true positive (TP) vs. false negative (FN) detections, we use the Intersection over Union (IoU) criteria. IoU is a measurement of the overlap between the predicted bounding box and the ground truth bounding box. If the IoU value is greater than a threshold fixed by the user (usually 0.5), the detection is considered a TP, else it is a FN. This IoU threshold is an important criterion because the higher it is, the more stringent we are in considering what a TP is. The figure Fig. 3 show the evolution of the Average Precision (AP$_{50}$) and the Average Recall (AR$_{50}$) at IoU 50 with respect to the training steps. We also added the $F_1$ score.

In order to evaluate the global performances, we measure the precision and the recall for all images and calculate the average precision and the average recall on the whole testing dataset. Since the IoU threshold commonly used in the literature for evaluating computer vision model is 0.5, we will use average precision and recall with IoU = 0.5 (AP$_{50}$ and AR$_{50}$). It is necessary to use these two metrics to evaluate our algorithm since they assess different aspects of the model's performance. In summary, precision measures the proportion of true positives among all detections, while recall evaluates how comprehensively the model identifies relevant instances. It is always possible to improve one metric to the detriment of the other. To integrate these together, we calculate the Precision-Recall (PR) curve and introduce a new metric based on the work of Saito et al. (2015), which indicates that the Area Under the PR Curve (AUPR) is an effective way to combine precision and recall. Additionally, it states that for binary classification problems, the AUPR is equivalent to the Average Precision (AP) (Saito and Rehmsmeier, 2015).

# 4. Results

## 4.1. Global crater detection

As introduced in Section 3.3, Fig. 4 is a graph called Precision vs Recall Curve (PR curve) that depicts how precision changes as the recall varies (and the likelihood index) at various Intersection-over-Union (IoU) thresholds. In this way, we can observe the performance of the algorithm across different levels of detection. As expected the curves appear better as the IoU threshold decreased. On the contrary, when the threshold varies from 0.7 to 0.9, i.e. when being more demanding on how well the bounding boxes must fit the craters, the performance decreases.

In order to achieve a comprehensive and precise performance metric, we calculated the area under the Precision vs Recall curve. This area gives us the mean Average Precision (mAP), which summarizes the algorithm's ability to accurately detect objects across various precision-recall trade-offs. A higher mAP indicates better detection performance. Figure Sup. Mat. S1 shows the computed mAP values corresponding to different IoU thresholds. The algorithm is robust from IoU 0 to ~ 0.7 with a mAP > 0.8 but drastically decreases above IoU > 0.7. We obtain a mAP at IoU = 0.5 (AP$_{50}$) of 0.82 which is in the range of the best values from the literature for crater detection on Mars (Benedix et al., 2020). We note however that our value is achieved on the entire planet.

## 4.2. Size dependence

Crater distributions on planetary surfaces are close to a power law (Hartmann and Neukum, 2001) with a -2 slope, meaning that there are 100 times more craters > 1 km than craters > 10 km. To accurately evaluate the performance of our detection algorithm across this wide range of sizes, we perform the same evaluation (at IoU threshold = 0.5) on three different bounding boxes size range with limits of 32² and 96² pixel size. We can see in Fig. 5 that our algorithm is robust to crater scale because the AP$_{50}$ scores are equally good.

<figure><img src="image_9.png" /><figcaption>Fig. 6. Precision vs recall curve at IoU = 0.5. Each curve represents the precision with respect to the recall for a given latitude range. More is described in Sup. Mat. table 1.</figcaption></figure>

## 4.3. Latitude/illumination dependence

To assess the robustness of our crater detection algorithm across different latitudes/illumination conditions, we conducted experiments using subsets of the test dataset. The images were categorized into ten latitude groups as shown in Fig. 6 (more details in Sup. Mat. table 1), all evaluated using the standard metrics AP$_{50}$ described above.

The experimental results showed in Fig. 6 and Sup. Mat. table 1 demonstrate that our crater detection algorithm exhibits robustness across different latitudes, with varying degrees of performance. The algorithm achieved excellent performance in equatorial regions (from 48° N to 48° S), accurately identifying a significant number of craters with AP$_{50}$ up to 0.86. At higher latitudes, the algorithm maintained an acceptable level of performance with AP$_{50}$ > ~ 0.7.

In Fig. 7, we present an example image from the mid-latitude in old Noachian terrain, demonstrating the effectiveness of our algorithm. Despite the presence of challenging surface features and illumination conditions, the algorithm successfully identifies all the craters within the image. This case is an example of perfect detection (AP$_{50}$ = 1). Figure S2 shows another example with AP$_{50}$ = 0.9, more representative of the global results. Fig. 8 shows a case with relatively apparent poor result (AP$_{50}$ = 0.5) and will be discussed in the next section.

## 4.4. Crater-like feature

In this subsection we will investigate the ability of our model to distinguish craters from other geomorphological circular features commonly found on planetary surfaces. This analysis aims to determine the algorithm's robustness in detecting craters, rather than miss-classifying non-crater circular features.

We evaluate special cases with other circular features, such as scoria cones, (Fig. 8), calderas (Sup. Mat. Fig. S3), or lava tubes (Sup. Mat. Fig. S4). Results show that our detector do not detect circular morphological features which are not craters, as expected. For the image in Fig. 8, none of the numerous scoria cones are detected as crater.

This results means that we have gained valuable robustness on many of those features. However, as we can see in Sup. Mat. Fig. S4 challenges arise when distinguishing calderas and, to a lesser extent, lava tubes from craters. One may note that caldera are circular hole features that can be hard to distinguish from crater, even for planetary scientist. Additionally, small errors in recognizing lava tubes highlight a need
<figure><img src="image_10.png" /><figcaption>Fig. 7. Inference in Noachis Terra region (Mars). The lower right corner of this 4° ×4° quadrangle is located at 44° W, 20° S. The yellow boxes represent the ground truth information and the blue boxes the predictions ones. The mAP for this image is 0.999. Please note that crater smaller than 10 pixels in diameter are ignored.</figcaption></figure>

for further refinement, even though the model generally performs well in this regard.

This finding highlights the algorithm's effectiveness in capturing the unique characteristics and morphology associated with craters.

## 4.5. Miss-classification

For the image in Fig. 8, mAP50 is 0.5 with the ground truth filtered at 10 pixels (100 m). Unfortunately the proposed crater F (with a confident score of 0.992) is actually a real one but the ground truth crater is not present in the crater database because its size is below 1 km in diameter (below 10 pixels). Our method estimates crater features with minimum 10 pixels radius. By including crater F, the mAP increases to 0.8, which is representative of the average results error. This effect of size threshold (correct detection but classified as incorrect) could decrease the score, making our algorithm not as good as it actually is.

Another source of miss-classification is due to interpretation of complex geological features. For instance, the proposed crater G (with a confident score 0.617) has no equivalence in the ground truth. This apparent incorrect detection is debatable because there is indeed a circular shape in the North East of the scoria cone but its interpretation is difficult at this spatial resolution. Is it a real crater in the hummocky terrain covered by the cone formation? Is it a part of the scoria cone? This case typically illustrates the limitation of the ground truth interpretation due to human interpretation.

## 4.6. Test on other planets

In this subsection, we endeavor to extend the applicability of our model to planetary surfaces beyond Mars. In this study, the aim is not to build a complete database of the whole solar system but more to demonstrate the robustness of our crater detector applying it to different bodies. Specifically, we turn our attention to two celestial bodies, Mercury and the Moon, both of which offer comprehensive mosaic datasets for our evaluation. For the lunar surface assessment, we leverage the Wide Angle Camera (WAC) dataset obtained by the Lunar Reconnaissance Orbiter (LRO) mission (Robinson et al., 2010). This dataset provides high-resolution imagery with a remarkable level of detail, boasting a resolution of 100 meters per pixel. In the case of Mercury, we employ the MESSENGER global mosaic dataset (Becker et al., 2009). MESSENGER, the Mercury Surface, Space Environment, Geochemistry, and Ranging mission, has contributed a wealth of data, including imagery at a resolution of 166 meters per pixel.

Our aim is to apply our raw crater detection model without any optimization to these distinct planetary surfaces, each presenting its own unique geologic characteristics and challenges. By doing so, we seek to assess the model's versatility and its potential utility in automating crater detection tasks on multiple celestial bodies. On each images we built handmade database in order to measure the precision of our algorithm.

As can be seen in 9 and 10, our algorithm is also able to detect crater in an efficient way on other planetary bodies with scores. Results on Mercury are excellent (AP<sub>50</sub> = 0.92) and on the Moon the performances are similar to those on Mars (AP<sub>50</sub> = 0.78). Both these surfaces are very old, much more cratered than Mars, with overlapping and buried
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Inferences on Ulysses scoria cones region (Mars). Some volcanic cones are present in the center of image. The lower right corner of this 0.8" ×0.8" quadrangle is located at 122° 24′W, 05° 24′N on Arisia Mons slopes. Here, the model is not inferring crater where there is no crater demonstrating its robustness. Proposed craters F and G are discussed in Section 4.5.</figcaption></figure>

craters that are well detected. Buried craters correspond to ≈6% of the training database (Lagain et al., 2021), a better transfer learning may improve this performance. Crater overlap makes the detection harder because their shape is not always fully circular. Nevertheless, our detection model is able to detect craters on the Moon and Mercury in a satisfactory way.

**5. Discussion**

Our findings concur with the observations made by Benedix et al. 2020 in their work (Benedix et al., 2020), suggesting that detecting craters becomes notably more challenging beyond 45° latitude. In response, we have endeavored to enhance our model by implementing a specialized re-projection technique within a local stereoscopic projection and by taking special care to include high-latitude samples, with extreme illumination condition, in the training phase. These modifications improved our algorithm's detection performance, especially in regions where traditional methods encounter difficulties.

As the latitude increases, cast shadow become larger in the observations, making the detection slightly more difficult. The overall decrease in performance with latitude that can be seen on Fig. 6 is expected. This effect is limited and the performance of the detector remain good even at high latitudes. In the southern hemisphere, 11 (B), shows a high concentration of eroded crater in the whole −30°/−60°

latitudinal band. These older terrains, i.e.: in the Noachian geologic terrain more present in the south hemisphere of Mars, also have a high density of craters as seen on 11 (A), and thus a high number of superimposed craters. Knowing this, we expect our detector to exhibit lower performance in these areas where the craters significantly more difficult to detect, which is the case for the 48°/−64° in Fig. 6. This highlights the fact that working on eroded and superimposed craters would be a key to significantly improve the performances of future detectors.

One other notable contribution of our research lies in the algorithm's ability to differentiate between craters and other circular geomorphic features such as scoria cones. Although our results indicate some success in this regard, we recognize that refining this capability is an ongoing endeavor. In particular, distinguishing craters from calderas remains a challenge that requires further attention and development. In practice, this limitation is not crucial since calderas are very uncommon in the Solar System.

The findings of our study underscore the potential and limitations of employing a Faster-RCNN model for automated crater detection on a planetary scale. The application of this computer vision algorithm has delivered promising results, particularly in its ability to perform well across various latitudes, crater scales, and to distinguish craters from other circular geomorphologic features. However, as illustrated in the inferences shown in Fig. 12, the detection process can be compromised
<figure><img src="image_12.png" /><figcaption>Fig. 9. Example on the Moon. The lower right corner of this 8° × 8° quadrangle is located at 77,78° E, 4,24° S near mare Smythii (in the eastern part of the Moon). The yellow boxes represent the ground truth information and the blue boxes the predictions ones. The mAP for this image is 0.775. Please note that crater smaller than 10 pixels in diameter are ignored.</figcaption></figure>

when a crater is incomplete within the image frame, excessively eroded, or smaller than the 10-pixel threshold required for detection.

## 6. Conclusion

The successful development and evaluation of a robust computer vision algorithm for automated crater detection on global scale planet marks a significant achievement in our study. Leveraging a state-of-the-art model, we embarked on a comprehensive assessment of the algorithm's performance across varying latitudes, crater scale and its capacity to discriminate craters from other circular features. The insights gained shed light on both the algorithm's effectiveness and the remaining challenges, propelling us toward valuable avenues of further research and improvement.

Since the algorithm is open-source, future development would be a more comprehensive and fair comparison with other algorithms.

Looking ahead, our work lays the foundation for the development of next-generation algorithms capable of cross-instrument, cross-planetary body, and cross-observation condition crater detection. In conclusion, while our Faster RCNN-based crater detection algorithm represents a significant step forward, it also highlights the complexities and ongoing challenges in the field. Continued research and collaboration are essential for overcoming these challenges and furthering the capabilities of automated crater detection systems.

## CRediT authorship contribution statement

L. Martinez: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. F. Andrieu: Writing – review & editing, Supervision, Software, Methodology, Investigation, Formal analysis, Conceptualization. F. Schmidt: Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. H. Talbot: Writing – review & editing, Software, Methodology, Conceptualization. M.S. Bentley: Writing – review & editing, Supervision, Project administration, Funding acquisition.

## Declaration of competing interest

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

## Acknowledgments

We acknowledge support from ESA through the ESA Space Science Faculty - funding reference 4000139120/22/ES/CM. We acknowledge support from the "Institut National des Sciences de l'Univers" (INSU), the "Centre National de la Recherche Scientifique" (CNRS) and "Centre National d'Etudes Spatiales" (CNES) through the "Programme National de Planétologie". This work also benefits from discussions with Alexandre Boucaud.
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Example on Mercury. The lower right corner of this 9.5° ×9.5° quadrangle is located at 121° W, 5° S near Verda-Da-Silva Crater (Beethoven, quadrangle, Mercury). The yellow boxes represent the ground truth information and the blue boxes the predictions ones. The mAP for this image is 0.926. Please note that crater smaller than 10 pixels in diameter are ignored.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Crater density map of (A) All craters classified as ‘Valid’ by Lagain et al. (2021) and (B) the craters classified as ‘Burried/Eroded’ by Lagain et al. (2021). This crater density map is built with healpix polygons of respectively 1200² and 10,380 km² each for (A) and (B).</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Some crater detection inference conducted on CTX images of the Arabia Terra region on Mars, specifically located at 04° W, 08° S. These images were examined at various zoom levels and corresponding downsampled resolutions. Each image patch has a dimension of 224 × 224 pixels. The blue images represent a resolution of 100 meters per pixel (m/px), the green images have a resolution of 200 m/px, and the yellow images are at 400 m/px resolution.</figcaption></figure>

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.pss.2025.106053.

### Data availability

The raw training dataset used for our analysis is available online, as detailed in the publication by Dickson et al. (2023). For further information and access to the dataset, please refer to the following: datalink. Additionally, the source code developed for this study and the model weight are publicly available at https://doi.org/10.14768/ae042dbb-37c3-4a98-b72c-02d2a191f53b.

### References

* Ali-Dib, M., Menou, K., Jackson, A.P., Zhu, C., Hammond, N., 2020. Automated crater shape retrieval using weakly-supervised deep learning. Icarus 345, 113749.

* Appéré, T., Schmitt, B., Langevin, Y., Douté, S., Pommerol, A., Forget, F., Spiga, A., Gondet, B., Bibring, J.-P., 2011. Winter and spring evolution of northern seasonal deposits on mars from OMEGA on mars express. J. Geophys. Research: Planets 116 (E5).

* Becker, K., Robinson, M., Becker, T., Weller, L., Turner, S., Nguyen, L., Selby, C., Denevi, B., Murchie, S., McNutt, R., et al., 2009. Near global mosaic of mercury. In: AGU Fall Meeting Abstracts. 2009, pp. P21A–1189.

* Benedix, G., Lagain, A., Chai, K., Meka, S., Anderson, S., Norman, C., Bland, P., Paxman, J., Towner, M., Tan, T., 2020. Deriving surface ages on mars using automated crater counting. Earth Space Sci. 7 (3), e2019EA001005.

* Bickel, V., Conway, S., Tesson, P.-A., Manconi, A., Loew, S., Mall, U., 2020. Deep learning-driven detection and mapping of rockfalls on mars. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. PP, 1. http://dx.doi.org/10.1109/JSTARS.2020.2991588.

* Brož, P., Čadek, O., Hauber, E., Rossi, A.P., 2014. Shape of scoria cones on mars: Insights from numerical modeling of ballistic pathways. Earth Planet. Sci. Lett. 406, 14–23.

* Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A., 2020. Albumentations: Fast and flexible image augmentations. Information 11 (2), http://dx.doi.org/10.3390/info11020125, URL https://www.mdpi.com/2078-2489/11/2/125.

* Carr, M.H., 1973-07. Volcanism on mars. J. Geophys. Res. 78 (20), 4049–4062. http://dx.doi.org/10.1029/jb078i020p04049.

* Chen, M., Liu, D., Qian, K., Li, J., Lei, M., Zhou, Y., 2018. Lunar crater detection based on terrain analysis and mathematical morphology methods using digital elevation models. IEEE Trans. Geosci. Remote. Sens. 1–12. http://dx.doi.org/10.1109/tgrs.2018.2806371.

* Di, K., Li, W., Yue, Z., Sun, Y., Liu, Y., 2014. A machine learning approach to crater detection from topographic data. Adv. Space Res. 54 (11), 2419–2429.

* Dickson, J.L., Ehlmann, B.L., Kerber, L.H., Fassett, C.I., 2023. Release of the Global CTX Mosaic of Mars: An Experiment in Information-Preserving Image Data Processing. In: 54th Lunar and Planetary Science Conference. In: LPI Contributions, vol. 2806, p. 2353.

* Dickson, Kerber, L., Fassett, C., Ehlmann, B., 2018. A global, blended ctx mosaic of mars with vectorized seam mapping: a new mosaicking pipeline using principles of non-destructive image editing. In: 49th Lunar and Planetary Science Conference. URL https://www.hou.usra.edu/meetings/lpsc2018/pdf/2480.pdf.
Downes, L., Steiner, T.J., How, J.P., 2020. Deep learning crater detection for lunar terrain relative navigation. In: AIAA SciTech 2020 Forum. p. 1838.

Edwards, C.S., Nowicki, K.J., Christensen, P.R., Hill, J., Gorelick, N., Murray, K., 2011. Mosaicking of global planetary image datasets: 1. Techniques and data processing for thermal emission imaging system (THEMIS) multi-spectral data. J. Geophys. Res. 116 (E10), http://dx.doi.org/10.1029/2010je003755.

Galloway, M.J., Benedix, G.K., Bland, P.A., Paxman, J., Towner, M.C., Tan, T., 2014. Automated crater detection and counting using the hough transform. In: 2014 IEEE International Conference on Image Processing. ICIP, IEEE, pp. 1579–1583.

Giannakis, I., Bhardwaj, A., Sam, L., Leontidis, G., 2024. A flexible deep learning crater detection scheme using segment anything model (SAM). Icarus 408, 115797. http://dx.doi.org/10.1016/j.icarus.2023.115797, URL https://www.sciencedirect.com/science/article/pii/S0019103523003755.

Girshick, R., Donahue, J., Darrell, T., Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 580–587.

Hartmann, W.K., 2005. Martian cratering 8: Isochron refinement and the chronology of mars. Icarus 174 (2), 294–320.

Hartmann, W.K., Neukum, G., 2001. Cratering chronology and the evolution of mars. In: Chronology and Evolution of Mars: Proceedings of an ISSI Workshop, 10–14 April 2000, Bern, Switzerland. Springer, pp. 165–194.

Haslebacher, C., Thomas, N., Bickel, V.T., 2023. LineaMapper: A deep learning-powered tool for mapping linear surface features on Europa. Icarus 115722. http://dx.doi.org/10.1016/j.icarus.2023.115722, URL https://www.sciencedirect.com/science/article/pii/S0019103523002993.

He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017. Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2961–2969.

Head, J., Greeley, R., Golombek, M., Hartmann, W., Hauber, E., Jaumann, R., Masson, P., Neukum, G., Nyquist, L., Carr, M., 2001. Geological processes and evolution. Space Sci. Rev. 96, 263–292.

Head III, J.W., Fassett, C.I., Kadish, S.J., Smith, D.E., Zuber, M.T., Neumann, G.A., Mazarico, E., 2010. Global distribution of large lunar craters: Implications for resurfacing and impact populations. Science 329 (5998), 1504–1507.

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al., 2023. Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026.

La Grassa, R., Cremonese, G., Gallo, I., Re, C., Martellato, E., 2023. YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces. Remote. Sens. 15 (5), 1171. http://dx.doi.org/10.3390/rs15051171.

Lagain, A., Bouley, S., Baratoux, D., Marmo, C., Costard, F., Delaa, O., Rossi, A.P., Minin, M., Benedix, G., Ciocco, M., et al., 2021. Mars crater database: A participative project for the classification of the morphological characteristics of large martian craters. Geoscienceworld.

Latorre, F., Spiller, D., Sasidharan, S., Basheer, S., Curti, F., 2023. Transfer learning for real-time crater detection on asteroids using a fully convolutional neural network. Icarus 394, 115434. http://dx.doi.org/10.1016/j.icarus.2023.115434.

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86 (11), 2278–2324.

Lee, C., 2019. Automated crater detection on mars using deep learning. Planet. Space Sci. 170, 16–28. http://dx.doi.org/10.1016/j.pss.2019.03.008.

Lee, C., Hogan, J., 2021. Automated crater detection with human level performance. Comput. Geosci. 147, 104645.

Leighton, R.B., Murray, B.C., Sharp, R.P., Denton Allen, J., Sloan, R.K., 1965. Mariner IV Photography of Mars: Initial Results. Science 149, 627–630. http://dx.doi.org/10.1126/science.149.3684.627.

Malin, M.C., Bell III, J.F., Cantor, B.A., Caplinger, M.A., Calvin, W.M., Clancy, R.T., Edgett, K.S., Edwards, L., Haberle, R.M., James, P.B., Lee, S.W., Ravine, M.A., Thomas, P.C., Wolff, M.J., 2007. Context camera investigation on board the mars reconnaissance orbiter. J. Geophys. Research: Planets 112 (E5), http://dx.doi.org/10.1029/2006JE002808, URL https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2006JE002808.

Masson, P., 1984. Géologie et morphologie de la planète mars (geology and morphology of planet mars). Bull. de L' Assoc. de Géographes Fran c Cais 61 (504), 237–244.

Neukum, G., Ivanov, B.A., Hartmann, W.K., 2001. Cratering records in the inner solar system in relation to the lunar reference system. In: Chronology and Evolution of Mars: Proceedings of an ISSI Workshop, 10–14 April 2000, Bern, Switzerland. Springer, pp. 55–86.

Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 779–788.

Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28.

Robbins, S.J., 2019. A new global database of lunar impact craters > 1–2 km: 1. Crater locations and sizes, comparisons with published databases, and global analysis. J. Geophys. Research: Planets 124 (4), 871–892.

Robbins, S.J., Hynek, B.M., 2012. A new global database of mars impact craters ≥ 1 km: 1. Database creation, properties, and parameters. J. Geophys. Research: Planets 117 (E5).

Robinson, M.S., Brylow, S., Tschimmel, M., Humm, D., Lawrence, S., Thomas, P., Denevi, B.W., Bowman-Cisneros, E., Zerr, J., Ravine, M., et al., 2010. Lunar reconnaissance orbiter camera (LROC) instrument overview. Space Sci. Rev. 150, 81–124.

Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, pp. 234–241.

Saito, T., Rehmsmeier, M., 2015. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One 10 (3), e0118432.

Salamuniccar, G., Loncaric, S., 2010. Method for crater detection from martian digital topography data using gradient value/orientation, morphometry, vote analysis, slip tuning, and calibration. In: Geoscience and Remote Sensing, IEEE Transactions on DOI - 10. 1109/ TGRS. 2009. 2037750. Geosci. Remote. Sens. IEEE Trans. 48 (5), 2317–2329. http://dx.doi.org/10.1109/TGRS.2009.2037750.

Salamuniccar, G., Lončarić, S., Pina, P., Bandeira, L., Saraiva, J., 2011. MA130301GT catalogue of martian impact craters and advanced evaluation of crater detection algorithms using diverse topography and image datasets. Planet. Space Sci. 59 (1), 111–131.

Schmidt, F., Douté, S., Schmitt, B., Vincendon, M., Bibring, J.-P., Langevin, Y., 2009. Albedo control of seasonal south polar cap recession on mars. Icarus 200 (2), 374–394. http://dx.doi.org/10.1016/j.icarus.2008.12.014, URL http://www.sciencedirect.com/science/article/B6WGF-4V76270-1/2/60231d55061d19abebb5344bd9e40e2b.

See, J.E., Howe, S.R., Warm, J.S., Dember, W.N., 1995. Meta-analysis of the sensitivity decrement in vigilance. Psychol. Bull. 117 (2), 230.

Silburt, A., Ali-Dib, M., Zhu, C., Jackson, A., Valencia, D., Kissin, Y., Tamayo, D., Menou, K., 2019. Lunar crater identification via deep learning. Icarus 317, 27–38.

Su, S., Fanara, L., Xiao, H., Hauber, E., Oberst, J., 2023. Detection of detached ice-fragments at martian polar scars using a convolutional neural network. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 16, 1728–1739.

Van den Bossche, J., Boucaud, A., Schmidt, F., Lemaitre, G., Lagain, A., Meresescu, A., Kegl, B., 2018. Automated detection of planetary craters: open and reproducible benchmark platform for the martian surface. In: European Planetary Science Congress, 16-21 Septembre 2018, Berlin. vol. 12, pp. EPSC2018-679, URL https://meetingorganizer.copernicus.org/EPSC2018/EPSC2018-679-1.pdf.

Vinogradova, T., Burl, M., Mjolsness, E., 2002. Training of a crater detection algorithm for mars crater imagery. In: Proceedings, Ieee Aerospace Conference. vol. 7, IEEE, p. 7.

Wang, Y., Wu, B., 2019. Active machine learning approach for crater detection from planetary imagery and digital elevation models. IEEE Trans. Geosci. Remote. Sens. 57 (8), 5777–5789. http://dx.doi.org/10.1109/tgrs.2019.2902198.

Warner, N.H., Gregg, T.K., 2003. Evolved lavas on mars? Observations from southwest Arsis Mons and sabancaya volcano, peru. J. Geophys. Research: Planets 108 (E10).

Williams, D.A., Greeley, R., Zuschneid, W., Werner, S.C., Neukum, G., Crown, D.A., Gregg, T.K., Gwinner, K., Raitala, J., 2007. Hadriaca patera: insights into its volcanic history from mars express high resolution stereo camera. J. Geophys. Research: Planets 112 (E10).

Xiuqin, P., Zhang, Q., Zhang, H., Li, S., 2019. A fundus retinal vessels segmentation scheme based on the improved deep learning U-Net model. IEEE Access 7, 122634–122643. http://dx.doi.org/10.1109/ACCESS.2019.2935138.

Yang, S., Cai, Z., 2022. High-resolution feature pyramid network for automatic crater detection on mars. IEEE Trans. Geosci. Remote Sens. 60, 1–12. http://dx.doi.org/10.1109/TGRS.2021.3104925.

Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B., 2022. A survey of modern deep learning based object detection models. Digit. Signal Process. 126, 103514.

Zhou, Y., Zhao, H., Chen, M., Tu, J., Yan, L., 2018. Automatic detection of lunar craters based on DEM data with the terrain analysis method. Planet. Space Sci. 160, 1–11. http://dx.doi.org/10.1016/j.pss.2018.03.003.