Maphill is the web’s largest map gallery. Our goal is to redefine the experience of discovering the world through the maps.
We turn data into pictures. We succeeded in automating the process of transforming geographic data into map graphics. This means that we can produce maps in higher quality, faster and cheaper than was possible before.
We haven't created more than one hundred millions of maps to keep them to ourselves. You can embed all maps into your website very easily. Just like any other images. Maphill changes the way map graphics is accessible.
Maphill was launched in July 2013. The original idea to build a map gallery is, however, much older. In the meantime, the internet and mapping technologies have evolved much more than the world itself.
Some things do not lose value over time. We believe that many millions of accessible and easy to use map images belong among them.
We use Amazon Web Services to process the data, to generate the maps and to run this website from multiple locations at the same time. It would be much more challenging to create Maphill in the times when cloud infrastructure services were not yet available.
Processing terabytes of data and generating many millions of maps wouldn't be possible without the help of advanced technologies. These technologies are the result of continuous research and development. Thanks to many great people and the open source community, we were able to solve complex tasks more easily.
Above all, we would like to thank the following (in no particular order): cURL (Daniel Stenberg), Bootstrap (Mark Otto and Jacob Thornton), Hadoop (Doug Cutting, Michael Cafarella), RRDTool (Tobias Oetiker), Sed (Lee E. McMahon), awk (Alfred Aho, Peter Weinberger and Brian Kernighan), grep (Ken Thompson), Perl (Larry Wall), ImageMagick (John Cristy, Anthony Thyssen), MapReduce (Jeffrey Dean and Sanjay Ghemawat), Ubuntu (Linux core developers, Debian developers, Mark Shuttleworth, the Ubuntu Community), Redis (Salvatore Sanfilippo), Apache Web Server (Robert McCool), collectd (Florian Forster), GDAL (Frank Warmerdam), Ghostscript (Peter Deutsch), MySQL (Michael Widenius, David Axmark, Allan Larsson), Memcached (Brad Fitzpatrick), Font Awesome (Dave Gandy), GMT (Pål Wessel, Walter Smith and the GMT team), XFS (Adam Sweeney, Doug Doucette, Wei Hu, Curtis Anderson, Michael Nishimoto, Geoff Peck), PHP (Rasmus Lerdorf, Andi Gutmans, Zeev Suraski, the PHP Community), GD (Thomas Boutell), Python (Guido van Rossum), ExifTool (Phil Harvey), mootools (Valerio Proietti and The MooTools Dev Team), jQuery (John Resig), PIG (Alan Gates, Olga Natkovich and Yahoo Research Team), LESS (Alexis Sellier), node.js (Ryan Dahl), netCDF (Glenn Davis, Russ Rew, Ed Hartnett, John Caron, Dennis Heimbigner, Steve Emmerson, Harvey Davies, Ward Fisher).
A map is only as good as the data used to create it. Our goal is to be able to produce maps with the same level of detail for the entire world. It took us a lot of effort to obtain, process and compile the data from various sources into a single data layer suitable for generating the maps.
In order to provide maps for free, we need to keep data license costs down. For the time being, the quality of the source data significantly reduces the level of detail we are able to cover in our maps. We can easily recreate all maps again at any time, when better or more detailed data become available.
High-resolution digital topographic database of Earth generated as the result of international research effort spearheaded by the U.S. National Geospatial-Intelligence Agency and the U.S. National Aeronautics and Space Administration. Data were collected in February 2000 during the 11-day mission of the space shuttle Endeavour.
Seafloor topography data set produced from combination of shipboard depth soundings with gravity data derived from satellite altimetry.
High-resolution shoreline data set amalgamated from two databases in the public domain.
Series of images that show the color of the Earth’s surface for each month of 2004. Our satellite maps are based on image taken in July 2004.
Vector-based collection of data with global coverage divided into thematic layers. We use road and railway networks data and outlines of urban areas.