Open-source tool automates volcanic cone analysis on Mars and Earth

Scientists measuring volcanic cones on Mars and Earth have long faced a frustrating bottleneck: the landscape is full of cones, but characterizing them reliably means hours of manual measurement in geographic information systems, with results that vary from expert to expert. A new open-source software package, MarsCONE 2.0, aims to break that bottleneck by turning what was once a specialist scripting task into an accessible desktop application that can measure hundreds of thousands of conical landforms consistently across two planets.

The software, described in the journal SoftwareX, was developed by Jakub Śledziowski, Bartosz Pieterek, and Thomas J. Jones. It extends an earlier command-line tool called MarsCONE, which was designed to automatically extract morphometric measurements of pitted cones from digital elevation models, or DEMs, of the Martian surface. The new version wraps the validated computational core in a graphical user interface, adds quality-control and manual-correction tools, and broadens support from Mars alone to both Earth and Mars, including the coordinate reference system complications that come with comparing data across planetary bodies.

Pitted cones are among the most abundant landforms on Mars. Hundreds of thousands have been cataloged globally, yet their origins remain contested. Morphologically, they resemble volcanic scoria cones, spatter cones, tuff cones, and rootless cones formed where lava interacted with water ice in the subsurface. Some researchers argue that many are actually mud volcanoes, formed by sedimentary rather than igneous processes. Because these different origins produce similar shapes from orbit, planetary geologists increasingly rely on precise, quantitative measurements of parameters such as cone basal width, summit crater width, cone height, crater depth, and flank slope to discriminate among formation mechanisms. The problem is that manual measurements depend heavily on how an individual researcher delineates a cone’s base or crater rim, introducing subjectivity that limits reproducibility and makes it difficult to combine results across studies.

MarsCONE 2.0 preserves the three-stage architecture of its predecessor. The Generator module reads a DEM along with cone locations, either as polygons or, preferably, as point markers of cone centers, and cuts radial cross-sections around each landform, extracting elevation profiles along eight transects. The Finder module then analyzes those profiles to detect the characteristic topographic break points: the cone base on either side, the summit rim, and the central depression. The Analyzer module aggregates detections across all transects into cone-level metrics, including height, crater depth, base and crater diameters, slope statistics, shape descriptors, and dimensionless morphometric ratios. The revised Finder offers configurable profile smoothing and a lower-edge protection option to guard against false detections caused by noisy terrain or edge effects near DEM boundaries.

Where the new version differs most visibly is in how users interact with that pipeline. The original MarsCONE required users to edit Python scripts; its predecessors in terrestrial volcanology, MORVOLC and NETVOLC, were likewise command-line tools. MarsCONE 2.0, built with the PySide6 toolkit, places the entire workflow in a desktop interface. Users configure the coordinate reference system, processing parameters, and export options in one panel, launch the Generator, Finder, and Analyzer as separate processes, and monitor logs and preview results in the same window. Results are exported in interoperable formats—CSV tables, GeoPackage or GeoJSON spatial layers, and PNG diagnostic images—so they can flow directly into QGIS, ArcGIS, R, or Python-based analyses.

A suite of new modules handles cases that automated pipelines typically struggle with. The Cross-section module lets users inspect the detected points on individual profiles; in one demonstration, a cone in Arizona’s San Francisco Volcanic Field showed marked asymmetry, with left- and right-flank heights of 264 and 289 meters and differing relative crater depths. The Manual Fix module allows researchers to reposition points on problematic profiles, but crucially, corrections are stored as separate override records rather than overwriting automatic detections, keeping every intervention transparent and auditable. A DEM Overlay module projects detections onto hillshade imagery, and the Elevation Explorer permits freeform profiling in any orientation, useful for breached cones, disrupted rims, and overflow pathways that standard radial metrics cannot capture.

Perhaps the most significant new capability for planetary science is the Complex Cones module. Volcanic fields frequently contain overlapping edifices with multiple summit craters, landforms that earlier workflows either excluded from datasets or analyzed separately, precisely because automated tools cannot reliably define their boundaries. Rather than attempting automatic classification, MarsCONE 2.0 lets users group cones into composite systems based on geological interpretation; the software then records those relationships explicitly and generates morphometric summaries for both individual cones and the grouped system.

The team verified the software against the original validated MarsCONE workflow using 58 Martian rootless cones, confirming close correspondence of outputs. Because the original tool had been tested only on Martian data, the authors added a terrestrial validation: a single experienced volcanologist manually measured 27 scoria cones in ArcGIS Pro from the highest-resolution DEMs available, and those measurements were compared with MarsCONE 2.0’s automated outputs. The agreement was strong for cone height and crater width, with Pearson correlation coefficients of 0.93 and 0.928 respectively, regression values of 0.86, and symmetric mean absolute percentage errors of 9.3 and 13.2 percent. Basal width showed weaker agreement, with a correlation of 0.76 and a positive bias of roughly 167 meters, about 11.7 percent of the mean manually measured width—a difference the authors attribute to the inherent ambiguity of manually defining a cone’s base.

The software also quantifies a subtle but important issue: DEM resolution. Comparing 11 co-registered scoria cones in the San Francisco Volcanic Field imaged at 1-, 10-, and 30-meter resolution, the team found that height and crater depth matched the 1-meter reference most closely across resolutions, while basal width was the most sensitive parameter, deviating most at 30 meters per pixel. The practical recommendation is straightforward: use the highest-resolution DEM available, and interpret basal dimensions with particular caution at coarse resolutions.

To demonstrate transplanetary applicability, the team ran the workflow on scoria cone fields in Ulysses Colles and Hydraotes Colles on Mars, using DEMs derived from Context Camera imagery at 12 meters per pixel and HiRISE imagery at 1 meter per pixel, and on terrestrial fields in San Francisco, Arizona, and Michoacán-Guanajuato, Mexico, with datasets ranging from 30 meters down to 1.5 meters per pixel. In the integrated multi-dataset comparison module, Martian cones showed distinctly larger basal widths and proportionally smaller craters relative to their bases than their terrestrial counterparts, consistent with expectations for volcanic eruptions under Mars’s lower atmospheric pressure and gravity, where ballistic clasts travel farther and build wider edifices. The module supports scatter plots with dataset centroids and convex-hull envelopes, pairwise similarity heatmaps, and principal component or UMAP embedding projections, letting researchers compare cone populations across planets within a standardized metric space.

Every cone processed by the Analyzer receives explicit quality-assurance metadata—a quality flag rated good, moderate, or poor, a numeric quality score, and diagnostic reason codes computed from user-configurable thresholds and transect-dispersion statistics. That uncertainty screening matters when applying the same workflow to DEMs that differ in resolution, noise structure, and acquisition source, whether from NASA orbiters or national mapping agencies.

The software is released under the MIT License on GitHub, with a Conda environment file supporting Windows, Linux, and macOS, a video tutorial, and a demonstration dataset archived on Zenodo alongside all the DEMs used in the study. Funded by Poland’s National Science Centre and a UK Research and Innovation Future Leaders Fellowship, the project reflects a broader push in planetary science toward tools that satisfy FAIR principles—making data findable, accessible, interoperable, and reusable.

For a field whose interpretations increasingly hinge on morphometric comparisons across hundreds of thousands of landforms on multiple worlds, the significance goes beyond convenience. The authors argue that progress depends not just on better algorithms but on transparent, reproducible tools that convert expert visual interpretation into reusable datasets. By lowering the programming barrier and embedding quality control into the workflow itself, MarsCONE 2.0 moves