Open Cartography: How MountainCarto Maps Are Made

This is the first blog post from MountainCarto, and I want to kick it off by sharing how my work comes to life: from open geographic data (opendata) to the tools I use to process it, all the way to the final result—my printed maps. A process that blends technique and vision, made possible by a solid foundation of open data and tools.

To give a concrete example of this approach, I’ll showcase in this article some images from the map currently in progress, dedicated to the Jungfrau–Aletsch area in the Bernese Alps. (By pure coincidence, the map includes the area affected by the massive landslide caused by the collapse of the Birch glacier, which buried around 90% of the village of Blatten in the Lötschental valley).

This isn’t a step-by-step technical guide but rather a tribute to the “open” philosophy that makes it all possible: open data and open-source software that transform raw data into rich, detailed representations of the landscape. A small example to convey the spirit behind MountainCarto and the creative freedom born from sharing.

The open data revolution

Over the last twenty years, the open data movement has radically transformed how we access and use information, especially geographic data. The spread of open geodata—freely available and reusable mapping data—has unlocked incredible possibilities, allowing anyone with expertise and curiosity to explore, analyze, and depict the terrain in detailed and creative ways.

MountainCarto was born in this very context, making the most of this wealth of information.

The three data sources behind the maps

Every MountainCarto map is primarily built on three types of open data:

1. The digital elevation model (DEM)

The digital elevation model is essentially a 3D representation of the Earth's surface. In English, it’s referred to as Digital Elevation Model (DEM) and is usually represented as a grayscale image, where each pixel contains an elevation value.

In DEMs, darker colors represent lower elevations, while lighter colors (up to white) indicate higher elevations. This structure allows for precise reconstruction of the terrain’s morphology: ridges, valleys, slopes, and every detail that characterizes mountainous landscapes.

From the very beginning, I’ve been fascinated by the ability to visualize mountains in 3D. Today, thanks to Blender, I can create extremely realistic representations. To do this, I prepare elevation data with GIS tools like QGIS and GDAL, optimizing it for import into the rendering engine. In Blender, I mainly use two sources: elevation data to model the shapes and land cover data, which enriches the scene with information about the terrain’s nature.

2. Land cover

Land cover data indicates what covers each part of the terrain: forests, pastures, rocks, glaciers, agricultural areas. These details are essential for representing not just the shape but also the very nature of the landscape.

By integrating this information with the terrain model, the maps come to life, becoming realistic and detailed depictions of mountainous scenery.

3. Human-made data: OpenStreetMap

Finally, human-made elements are represented by data from OpenStreetMap (OSM), the global collaborative project that aims to accurately map details like roads, trails, buildings, and place names.

I’ve personally contributed to OSM since 2010, focusing mainly on trails, hiking routes, and mountain place names. In Italy, the collaboration between OpenStreetMap Italy and the Italian Alpine Club (CAI) ensures constant qualitative growth of this data, which is essential for hikers and those who experience the mountains in all their facets (learn more here).

The tools of the trade

To transform data into maps, I use a set of digital tools, and much of this work relies on an open ecosystem: open data on one side and open-source software on the other. It’s precisely the intersection of these two worlds that gives rise to the freedom to experiment, customize, and create original representations of the terrain.

Tools like QGIS, PostGIS, GDAL, and Blender provide power, flexibility, and full control over workflows, perfectly adapting to the needs of mountain cartography. Alongside these, I also use proprietary software in the final production phase, mainly for handling place names and other informational layers, graphic refinement, and print optimization.

Open source

• QGIS: spatial data analysis and visualization
• PostGIS: advanced geographic data management
• GDAL: raster and vector conversion and manipulation
• Blender: 3D terrain modeling and rendering

Proprietary software

• Adobe Illustrator and Photoshop: graphic refinement and final design
• Avenza MAPublisher: management and optimization of cartographic elements within Illustrator

Looking ahead

MountainCarto is the result of the intersection between a passion for mountains, digital technologies, and smart use of open data.

In this blog, I’ll share reflections, insights, and stories related to cartography and mountains, aiming to convey the essence and vision behind every map.

Welcome to MountainCarto!