research-article

Machine-assisted map editing

Authors:

Favyen Bastani,

Mohammad Alizadeh,

Hari Balakrishnan,

Sam MaddenAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 23 - 32

https://doi.org/10.1145/3274895.3274927

Published: 06 November 2018 Publication History

Abstract

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.

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Cited By

Sui HZhou NZhou MGe L(2023)Vector Road Map Updating from High-Resolution Remote-Sensing Images with the Guidance of Road Intersection Change Detection and Directed Road TracingRemote Sensing10.3390/rs1507184015:7(1840)Online publication date: 30-Mar-2023
https://doi.org/10.3390/rs15071840
Yin YHu WTran AZhang YWang GKruppa HZimmermann RNg S(2023)Multimodal Deep Learning for Robust Road Attribute DetectionACM Transactions on Spatial Algorithms and Systems10.1145/36181089:4(1-25)Online publication date: 2-Sep-2023
https://dl.acm.org/doi/10.1145/3618108
Hu WZhang YLiang YYin YGeorgescu ATran AKruppa HNg SZimmermann RMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite ImageryProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548102(6155-6164)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548102
Show More Cited By

Index Terms

Machine-assisted map editing
1. Applied computing
  1. Computers in other domains
    1. Cartography
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2018

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Sui HZhou NZhou MGe L(2023)Vector Road Map Updating from High-Resolution Remote-Sensing Images with the Guidance of Road Intersection Change Detection and Directed Road TracingRemote Sensing10.3390/rs1507184015:7(1840)Online publication date: 30-Mar-2023
https://doi.org/10.3390/rs15071840
Yin YHu WTran AZhang YWang GKruppa HZimmermann RNg S(2023)Multimodal Deep Learning for Robust Road Attribute DetectionACM Transactions on Spatial Algorithms and Systems10.1145/36181089:4(1-25)Online publication date: 2-Sep-2023
https://dl.acm.org/doi/10.1145/3618108
Hu WZhang YLiang YYin YGeorgescu ATran AKruppa HNg SZimmermann RMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite ImageryProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548102(6155-6164)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548102
Rolnick DDonti PKaack LKochanski KLacoste ASankaran KRoss AMilojevic-Dupont NJaques NWaldman-Brown ALuccioni AMaharaj TSherwin EMukkavilli SKording KGomes CNg AHassabis DPlatt JCreutzig FChayes JBengio Y(2022)Tackling Climate Change with Machine LearningACM Computing Surveys10.1145/348512855:2(1-96)Online publication date: 7-Feb-2022
https://dl.acm.org/doi/10.1145/3485128
He SBalakrishnan H(2022)Lane-Level Street Map Extraction from Aerial Imagery2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00156(1496-1505)Online publication date: Jan-2022
https://doi.org/10.1109/WACV51458.2022.00156
Vargas-Munoz JSchibli DTuia D(2022)Towards Efficient Correction of Coconut Tree Detection ErrorsIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS46834.2022.9883076(5065-5068)Online publication date: 17-Jul-2022
https://doi.org/10.1109/IGARSS46834.2022.9883076
Watkinson KHuck JHarris A(2022)Centaur VGI: An Evaluation of Engagement, Speed, and Quality in Hybrid Humanitarian MappingAnnals of the American Association of Geographers10.1080/24694452.2022.2058907112:8(2373-2392)Online publication date: 13-Jun-2022
https://doi.org/10.1080/24694452.2022.2058907
Bastani FHe SJagwani SPark EAbbar SAlizadeh MBalakrishnan HChawla SMadden SSadeghi M(2021)Inferring and improving street maps with data-driven automationCommunications of the ACM10.1145/345035164:11(109-117)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1145/3450351
Vargas-Munoz JSrivastava STuia DFalcao A(2021)OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote SensingIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2020.29941079:1(184-199)Online publication date: Mar-2021
https://doi.org/10.1109/MGRS.2020.2994107
Comandur BKak A(2021)Semantic Labeling of Large-Area Geographic Regions Using Multiview and Multidate Satellite Images and Noisy OSM Training LabelsIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2021.306694414(4573-4594)Online publication date: 2021
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