Machine-assisted map editing
© 2018 held by the owner/author(s). Publication rights licensed to ACM. 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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM
2021
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| Online Access: | https://hdl.handle.net/1721.1/137386 |
| _version_ | 1826192937808035840 |
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| author | Bastani, Favyen He, Songtao Abbar, Sofiane Alizadeh, Mohammad Balakrishnan, Hari Chawla, Sanjay Madden, Sam |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Bastani, Favyen He, Songtao Abbar, Sofiane Alizadeh, Mohammad Balakrishnan, Hari Chawla, Sanjay Madden, Sam |
| author_sort | Bastani, Favyen |
| collection | MIT |
| description | © 2018 held by the owner/author(s). Publication rights licensed to ACM. 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. |
| first_indexed | 2024-09-23T09:31:09Z |
| format | Article |
| id | mit-1721.1/137386 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:31:09Z |
| publishDate | 2021 |
| publisher | ACM |
| record_format | dspace |
| spelling | mit-1721.1/1373862023-02-01T21:16:55Z Machine-assisted map editing Bastani, Favyen He, Songtao Abbar, Sofiane Alizadeh, Mohammad Balakrishnan, Hari Chawla, Sanjay Madden, Sam Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2018 held by the owner/author(s). Publication rights licensed to ACM. 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. 2021-11-04T18:18:45Z 2021-11-04T18:18:45Z 2018-11-06 2019-05-02T16:29:40Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137386 Bastani, Favyen, He, Songtao, Abbar, Sofiane, Alizadeh, Mohammad, Balakrishnan, Hari et al. 2018. "Machine-assisted map editing." en 10.1145/3274895.3274927 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM MIT web domain |
| spellingShingle | Bastani, Favyen He, Songtao Abbar, Sofiane Alizadeh, Mohammad Balakrishnan, Hari Chawla, Sanjay Madden, Sam Machine-assisted map editing |
| title | Machine-assisted map editing |
| title_full | Machine-assisted map editing |
| title_fullStr | Machine-assisted map editing |
| title_full_unstemmed | Machine-assisted map editing |
| title_short | Machine-assisted map editing |
| title_sort | machine assisted map editing |
| url | https://hdl.handle.net/1721.1/137386 |
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