124
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Open-source data alternatives for regenerating urban road fine-grained features

, , &
Article: 2364679 | Received 18 Mar 2024, Accepted 31 May 2024, Published online: 01 Jul 2024

References

  • Alghanim A, Jilani M, Bertolotto M, McArdle G. 2021. Leveraging road characteristics and contributor behaviour for assessing road type quality in OSM. IJGI. 10(7):436. doi: 10.3390/ijgi10070436.
  • Bao Z, Hossain S, Lang H, Lin X. (2023). A review of high-definition map creation methods for autonomous driving. Eng. Appl. Artif. Intell., 122, 106125. Doi:10.1016/j.engappai.2023.106125
  • Biljecki F, Ito K. 2021. Street view imagery in urban analytics and GIS: a review. Landsc Urban Plan. 215:104217. doi: 10.1016/j.landurbplan.2021.104217.
  • Cai S, Wakaki R, Nobuhara S, Nishino K. 2024. RGB road scene material segmentation. Image Vis Comput. 145:104970. doi: 10.1016/j.imavis.2024.104970.
  • Campbell A, Both A, Sun Q. 2019. Detecting and mapping traffic signs from google street view images using deep learning and GIS. Comput Environ Urban Syst. 77:101350. doi: 10.1016/j.compenvurbsys.2019.101350.
  • Cao R, Zhu J, Tu W, Li Q, Cao J, Liu B, Zhang Q, Qiu G. 2018. Integrating aerial and street view images for urban land use classification. Remote Sens. 10(10):1553. doi: 10.3390/rs10101553.
  • Chen X, Sun Q, Guo W, Qiu C, Yu A. 2022. GA-Net: a geometry prior assisted neural network for road extraction. Int J Appl Earth Observ. 114:103004. doi: 10.1016/j.jag.2022.103004.
  • Deng K, Yang C, Yin L, Zhao M, Jiang L, Peng D. 2021. Urban road extraction based on multi-source data. Bull Surv Map. 2021(10):60–66 + 82. doi: 10.13474/j.cnki.11-2246.2021.306.
  • Fang L, Sun T, Wang S, Fan H, Li J. 2022. A graph attention network for road marking classification from mobile LiDAR point clouds. Int J Appl Earth Obs Geoinformation. 108:102735. doi: 10.1016/j.jag.2022.102735.
  • Gong Z, Ma Q, Kan C, Qi Q. 2019. Classifying street spaces with street view images for a spatial indicator of urban functions. Sustainability. 11(22):6424. doi: 10.3390/su11226424.
  • Goodchild M. 2009. NeoGeography and the nature of geographic expertise. J Loca Based Serv. 3(2):82–96. doi: 10.1080/17489720902950374.
  • Guan H, Lei X, Yu Y, Zhao H, Peng D, Junior JM, Li J. 2022. Road marking extraction in UAV imagery using attentive capsule feature pyramid network. Int. J. Appl. Earth Obs. Geoinformation, 107,102677. doi:10.1016/j.jag.2022.102677.
  • Hu L, Wu X, Huang J, Peng Y, Liu W. 2020. Investigation of clusters and injuries in pedestrian crashes using GIS in Changsha, China. Saf Sci. 127:104710. doi: 10.1016/j.ssci.2020.104710.
  • Hu P, Chen S, Huang L, Wang G, Tang J, Luo B. 2023. Road extraction by multiscale deformable transformer from remote sensing images. IEEE Geosci Remote Sens Lett. 20:1–5. doi: 10.1109/LGRS.2023.3299985.
  • Hui Z, Hu Y, Jin S, Yevenyo YZ. 2016. Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization. ISPRS J Photogramm Remote Sens. 118:22–36. doi: 10.1016/j.isprsjprs.2016.04.003.
  • Kong Y, Wu H, Akram A, Li J, Zhao J, Wang S, Huang W, Liu C. 2023. UAV LiDAR data-based lane-level road network generation for urban scene HD maps. IEEE Geosci Remote Sens Lett. 20:1–5. doi: 10.1109/LGRS.2023.3308891.
  • Latsaheb B, Sharma S, Hasija S. 2024. Semantic road segmentation using encoder-decoder architectures. Multimed Tools Appl. pp. 1-23, doi: 10.1007/s11042-024-19175-y.
  • Li P, Li Y, Cai L, Dong Y, Fan H. 2020. Road green belt extraction and dynamic analysis based on vehicle LiDAR points cloud. J Geo-Inform Sci. 22(2):268–278. doi: 10.12082/dqxxkx.2020.190196.
  • Lian R, Wang W, Mustafa N, Huang L. 2020. Road extraction methods in high-resolution remote sensing images: a comprehensive review. IEEE J Sel Top Appl Earth Observ Remote Sens. 13:5489–5507. doi: 10.1109/JSTARS.2020.3023549.
  • Lin GX, Zhang JL. 2017. Object-based morphological building index for building extraction from high-resolution remote sensing imagery. Acta Geod Cartogr Sin. 46(6):724–733. doi: 10.11947/j.AGCS.2017.20170068.
  • Liu D, Zhang J, Liu K, Zhang Y. 2022. Aerial remote sensing image cascaded road detection network based on edge sensing module and attention module. IEEE Geosci Remote Sens Lett. 19:6513605. doi: 10.1109/LGRS.2022.3190495.
  • Liu L, Yang Z, Li G, Wang K, Chen T, Lin L. 2023. Aerial images meet crowdsourced trajectories: a new approach to robust road extraction. IEEE Trans Neural Netw Learning Syst. 34(7):3308–3322. doi: 10.1109/TNNLS.2022.3141821.
  • Liu XJ, Wang MZ, Zhen Y, Lu Y. 2011. Geometric measurement based on single image: a survey. Geomat Inf Sci Wuhan Univ. 36(8):941–947. doi: 10.13203/j.whugis2011.08.003.
  • Lu X, Zhong Y, Zheng Z, Chen Y, Ma A, Zhang L. 2022. Cascaded multi-task road extraction network for road surface, centerline, and edge extraction. IEEE Trans Geosci Remote Sens. 60:1–14. doi: 10.1109/TGRS.2022.3165817.
  • Lu Y, Lu J, Zhang S, Hall P. 2018. Traffic signal detection and classification in street views using an attention model. Comp Visual Media. 4(3):253–266. doi: 10.1007/s41095-018-0116-x.
  • Lv YL, Li YQ, Fan HL, Li PP. 2019. Research on road boundary extraction based on conjunction feature of vehicle-borne LiDAR point cloud. Geogr Geo-Inform Sci. 35(1):36–43. doi: 10.3969/J.issn.1672-0504.2019.01.005.
  • Meijer JR, Huijbregts MAJ, Schotten KCGJ, Schipper AM. 2018. Global patterns of current and future road infrastructure. Environ Res Lett. 13(6):064006. doi: 10.1088/1748-9326/aabd42.
  • Ministry of Housing and Urban Rural Development. 2010. Specification for design of intersections on urban roads (CJJ 152-2010). Beijing, China: China Building Industry Press.
  • Ministry of Housing and Urban Rural Development. 2012. Code for design of urban road engineering (CJJ/37-2012). Beijing, China: China Architecture & Building Press.
  • Ministry of Housing and Urban Rural Development, General Administration of Quality Supervision. 2015. Code for layout of urban traffic signs and markings (GB51038-2015). Beijing, China: China Planning Press.
  • Ning H, Ye X, Chen Z, Liu T, Cao T. 2022. Sidewalk extraction using aerial and street view images. Environ Plann B Urban Anal City Sci. 49(1):7–22. doi: 10.1177/2399808321995817.
  • Qiu L, Yu D, Zhang C, Zhang X. 2023. A semantics-geometry framework for road extraction from remote sensing images. IEEE Geosci Remote Sens Lett. 20:6004805. doi: 10.1109/LGRS.2023.3268647.
  • Rzotkiewicz A, Pearson AL, Dougherty BV, Shortridge A, Wilson N. 2018. Systematic review of the use of google street view in health research: major themes, strengths, weaknesses and possibilities for future research. Health Place. 52:240–246. doi: 10.1016/j.healthplace.2018.07.001.
  • Shao Z, Zhou Z, Huang X, Zhang Y. 2021. MRENet: simultaneous extraction of road surface and road centerline in complex urban scenes from very high-resolution images. Remote Sens. 13(2):239. doi: 10.3390/rs13020239.
  • Shi J, Li G, Zhou L, Lu G. 2022. Lane-level road network construction based on street-view images. IEEE J Sel Top Appl Earth Observ Remote Sens. 15:4744–4754. doi: 10.1109/JSTARS.2022.3181464.
  • Sun JB. 2013. Principles and applications of remote sensing. Wuhan, China: Wuhan University Press.
  • Tao L, Zhang P, Yan L, Zhu D. 2020. Automatically building linking relations between lane-level map and commercial navigation map using topological networks matching. J Navigation. 73(5):1159–1178. doi: 10.1017/S0373463320000259.
  • Tao Y, Wang C, Xu Y, Zhang Z, Song S, Yang W. 2020. Classification and expression of urban road from the perspective of DEM modeling. J Geo-Inf Sci. 22(8):1589–1596. doi: 10.12082/dqxxkx.2020.200004.
  • Tian Y, Gelernter J, Wang X, Chen W, Gao J, Zhang Y, Li X. 2017. Lane marking detection via deep convolutional neural network. Neurocomputing (Amst). 280:46–55. doi: 10.1016/j.neucom.2017.09.098.
  • Verma D, Mumm O, Carlow VM. 2021. Identifying streetscape features using VHR imagery and deep learning applications. Remote Sens. 13(17):3363. doi: 10.3390/rs13173363.
  • Wu S, Du C, Chen H, Xu Y, Guo N, Jing N. 2019. Road extraction from very high resolution images using weakly labeled openstreetmap centerline. IJGI. 8(11):478. doi: 10.3390/ijgi8110478.
  • Xiao X, Zhao Y, Zhang F, Luo B, Yu L, Chen B, Yang C. 2023. BASeg: Boundary aware semantic segmentation for autonomous driving. Neural Networks, 157,460–470. doi: 10.1016/j.neunet.2022.10.034
  • Xu S, Wang R, Zheng H. 2017. Road curb extraction from mobile LiDAR point clouds. IEEE Trans Geosci Remote Sens. 55(2):996–1009. doi: 10.1109/TGRS.2016.2617819.
  • Xu Q, Long C, Yu L, Zhang C. 2023. Road extraction with satellite images and partial road maps. IEEE Trans Geosci Remote Sens. 61:1–14. doi: 10.1109/TGRS.2023.3261332.
  • Yap W, Chang JH, Biljecki F. 2023. Incorporating networks in semantic understanding of streetscapes: contextualising active mobility decisions. Environ Plan B Urban Anal City Sci. 50(6):1416–1437. doi: 10.1177/23998083221138832.
  • Yang B, Fang L, Li Q, Li J. 2012. Automated extraction of road markings from mobile lidar point clouds. Photogramm Eng Remote Sens. 78(4):331–338. doi: 10.14358/PERS.78.4.331.
  • Yang C, Jiang L, Chen X, Wang C, Zhao M. 2017. Classification and expression of urban topographic features for DEM construction. J Geo-Info Sci. 19(3):317–325. doi: 10.3724/SP.J.1047.2017.00317.
  • Yang C, Zhao M, Wang C, Deng K, Jiang L, Xu Y. 2020. Urban road DEM construction based on geometric and semantic characteristics. Earth Sci Inform. 13(4):1369–1382. doi: 10.1007/s12145-020-00510-4.
  • Yang C, Xu F, Jiang L, Wang R, Yin L, Zhao M, Zhang X. 2021. Approach to quantify spatial comfort of urban roads based on street view images. J Geo-Inf Sci. 23(5):785–801. doi: 10.12082/dqxxkx.2021.200353.
  • Yang C, Ling J, Dai W, Peng D, Deng K, Zhao M, Huang X, Chen X. 2023. An operational framework for reconstructing lane-level road maps using open access data. IEEE J Sel Top Appl Earth Observ Remote Sens. 16:6671–6681. doi: 10.1109/JSTARS.2023.3296957.
  • Yang L, Zhai J, Wang X. 2022. A graph-cut-based method for road labels making with OSM data. IEEE Geosci Remote Sens Lett. 19:1–5. doi: 10.1109/LGRS.2021.3135960.
  • Yang X, Li X, Ye Y, Lau RYK, Zhang X, Huang X. 2019. Road detection and centerline extraction via deep recurrent convolutional neural network u-net. IEEE Trans Geosci Remote Sens. 57(9):7209–7220. doi: 10.1109/TGRS.2019.2912301.
  • Zhang F, Salazar-Miranda A, Duarte F, Vale L, Hack G, Chen M, Liu Y, Batty M, Ratti C. 2024. Urban visual intelligence: studying cities with artificial intelligence and street-level imagery. Ann Am Assoc. 114(5):876–897. doi: 10.1080/24694452.2024.2313515.
  • Zhang Y, Lu Z, Zhang X, Xue JH, Liao Q. 2022. Deep learning in lane marking detection: a survey. IEEE Trans Intell Transport Syst. 23(7):5976–5992. doi: 10.1109/TITS.2021.3070111.
  • Zhang Z, Liu Q, Wang Y. 2018. Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett. 15:1–5. doi: 10.1109/LGRS.2018.2802944.
  • Zhou G, Chen W, Gui Q, Li X, Wang L. 2022. Split depth-wise separable graph-convolution network for road extraction in complex environments from high-resolution remote-sensing images. IEEE Trans Geosci Remote Sens. 60:5614115. doi: 10.1109/TGRS.2021.3128033.