TY - GEN
T1 - Machine-assisted map editing
AU - Bastani, Favyen
AU - He, Songtao
AU - Abbar, Sofiane
AU - Alizadeh, Mohammad
AU - Balakrishnan, Hari
AU - Chawla, Sanjay
AU - Madden, Sam
N1 - Publisher Copyright:
© 2018 held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/11/6
Y1 - 2018/11/6
N2 - Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.
AB - Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.
KW - Automatic Map Inference
KW - Map Editing
UR - http://www.scopus.com/inward/record.url?scp=85058614361&partnerID=8YFLogxK
U2 - 10.1145/3274895.3274927
DO - 10.1145/3274895.3274927
M3 - Conference contribution
AN - SCOPUS:85058614361
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 23
EP - 32
BT - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
A2 - Xiong, Li
A2 - Tamassia, Roberto
A2 - Banaei, Kashani Farnoush
A2 - Guting, Ralf Hartmut
A2 - Hoel, Erik
PB - Association for Computing Machinery
T2 - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
Y2 - 6 November 2018 through 9 November 2018
ER -