SharedStreets has released a new programming library for merging overlapping street network datasets, called Mashnet. Mashnet can be used to conflate two or more street networks. It reads OpenStreetMap pbf road graphs and GeoJSON LineString features by default, and can be adapted to read other graph representations. It is open source under an MIT license.
Mashnet represents the street network as a graph data structure, with streets stored as graph edges and intersections represented as graph nodes. When loading a new network, Mashnet performs an algorithm called “graph normalization”, which transforms the network into a series of edges and nodes that is consistent across sources. New streets are then proposed and Mashnet is able to incrementally normalize the graph. This allows new roads to merge into the network without breaking important connections needed for cartography and routing.
Mashnet is designed for performance, but also for partition friendly architecture. This allows conflation operations to be scaled across many computers to enable global scale networks to be merged. To achieve this, Mashnet stores geometric data in a series of R-Tree structures that allow for efficient spatial querying, cutting down on unnecessary operations.
When a new street is proposed, Mashnet first decides if the street crosses any existing intersections, and will split the proposed street into multiple chunks if needed. Next, the proposed streets are run through a neural network classifier that is pre-trained to identify whether the new street is a match for any existing streets. If it is, Mashnet can then merge the associated metadata into the existing record. This allows a road network with good metadata, such as speed limits or bike lane accessibility, to be merged into another network with good geometry, creating a new map that is better than any single source. The neural network comes with a pre-trained model based on millions of synthetic edits, generated by perturbing the street network of Hawaii in OpenStreetMap.
If no existing match is found, a new street has been identified. Mashnet is then able to merge the new geometry into the existing graph without disturbing the existing geometry. This is helpful for identifying new roads which can be committed directly to the database or logged to a task manager for manual review.
Mashnet is a new approach to street network conflation that aims to balance performance and robust merge quality, without requiring the user to manually account for every subtle edge case. We are currently testing Mashnet to merge datasets like OpenStreetMap to other road networks such as ML derived aerial networks as well as traditional road networks maintained by DOTs.
If you would like to try out Mashnet, the code is open source, and we can help you get started with questions on Github.