Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
Route planning and map inference with global positioning traces
Computer Science in Perspective
Mining GPS Traces for Map Refinement
Data Mining and Knowledge Discovery
Scalable, Distributed, Real-Time Map Generation
IEEE Pervasive Computing
Roads Digital Map Generation with Multi-track GPS Data
ETTANDGRS '08 Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 01
GPS Community Map Generation for Enhanced Routing Methods Based on Trace-Collection by Mobile Phones
SPACOMM '09 Proceedings of the 2009 First International Conference on Advances in Satellite and Space Communications
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Map generation system with lightweight GPS trace data
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Probabilistic modeling of traffic lanes from GPS traces
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Robust Inference of Principal Road Paths for Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Mining large-scale, sparse GPS traces for map inference: comparison of approaches
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
CrowdAtlas: self-updating maps for cloud and personal use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
coRide: carpool service with a win-win fare model for large-scale taxicab networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Mining large-scale gps streams for connectivity refinement of road maps
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Segmentation-based road network construction
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Image driven GPS trace analysis for road map inference
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Proceedings of the 4th Annual Symposium on Computing for Development
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This paper describes a process for automatically inferring maps from large collections of opportunistically collected GPS traces. In this type of dataset, there is often a great disparity in terms of coverage. For example, a freeway may be represented by thousands of trips, whereas a residential road may only have a handful of observations. Additionally, while modern GPS receivers typically produce high-quality location estimates, errors over 100 meters are not uncommon, especially near tall buildings or under dense tree coverage. Combined, GPS trace disparity and error present a formidable challenge for the current state of the art in map inference. By tuning the parameters of existing algorithms, a user may choose to remove spurious roads created by GPS noise, or admit less-frequently traveled roads, but not both. In this paper, we present an extensible map inference pipeline, designed to mitigate GPS error, admit less-frequently traveled roads, and scale to large datasets. We demonstrate and compare the performance of our proposed pipeline against existing methods, both qualitatively and quantitatively, using a real-world dataset that includes both high disparity and noise. Our results show significant improvements over the current state of the art.