{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T05:31:04Z","timestamp":1770787864037,"version":"3.50.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Urban Info"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The spatiotemporal nature of urban environments \u2014 encompassing both, interactions among stationary features of the built and natural surroundings, and dynamic elements such as road users \u2014 pose significant challenges to the adoption of active mobility. Previously, influences of the urban environment on active mobility users have been typically investigated in unimodal approaches, thus greatly neglecting the diverse range of urban stressors that adversely affect (sustainable) mobility experiences, which can be inferred from multimodal data. For this reason, we propose a multimodal approach to investigate urban stress factors. Our methodology integrates data from wearable sensors and visual urban media to gain a more comprehensive understanding of spatiotemporal stressors in urban environments and active mobility. Spatially clustered stress measurements, i.e., hotspots and coldspots, derived from physiological reactions of the body, are used as labels for the classification of high-stress and low-stress urban areas. Semantic segmentation-based visual features, describing the immediate surroundings, are derived from real-time videos and snapshots of street scenes, captured through Street View Imagery (SVI). By comparing isovist features, i.e., visual impressions of dynamically changing urban scenes from a cyclists\u2019 point of view (POV), with scenes captured through SVI, we show that SVI provides a valuable data source for urban visual intelligence and relating high-stress cycling experiences to the surrounding environmental characteristics. While our Random Forest (RF) model trained on SVI-based features outperformed a POV video-based model by 3.9 percentage points in accuracy and 5.3 percentage points in recall (accuracy: 72.6% vs. 68.7%, recall: 72.2% vs. 66.9%), we encourage future studies to validate our findings in regions with higher environmental diversity, lower coverage of SVI data, and with additional data sources to account for confounding factors.<\/jats:p>","DOI":"10.1007\/s44212-025-00096-6","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T01:02:27Z","timestamp":1770771747000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying environmental stress factors in urban cycling using multimodal human sensing and machine learning"],"prefix":"10.1007","volume":"5","author":[{"given":"Martin Karl","family":"Moser","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David Ruben Max","family":"Graf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaily","family":"Gandhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernd","family":"Resch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"96_CR1","doi-asserted-by":"publisher","unstructured":"Abbiasov, T., et al. (2024). The 15-minute city quantified using human mobility data. In: Nature Human Behaviour. Publisher: Nature Publishing Group, pp. 1\u201311. issn: 2397-3374. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1038\/s41562-023-01770-y. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.nature.com\/articles\/s41562-023-01770-y. (visited on 02\/07\/2024).","DOI":"10.1038\/s41562-023-01770-y"},{"key":"96_CR2","doi-asserted-by":"publisher","unstructured":"Ai, D., et al. (2024). Measuring pedestrians\u2019 movement and building a visual-based attractiveness map of public spaces using smartphones. In: Computers, Environment and Urban Systems, 108, 102070. issn: 0198-9715.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compenvurbsys.2023.102070. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S0198971523001333. (visited on 03\/17\/2025).","DOI":"10.1016\/j.compenvurbsys.2023.102070"},{"key":"96_CR3","doi-asserted-by":"publisher","unstructured":"Anselin, L., & Getis, A. (1992). Spatial statistical analysis and geographic information systems. In: The Annals of Regional Science, 26(1), 19\u201333. issn: 1432-0592. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/BF01581478. (visited on 03\/18\/2025).","DOI":"10.1007\/BF01581478"},{"key":"96_CR4","doi-asserted-by":"publisher","unstructured":"B\u02d8adit,\u02d8a, A., & Popescu, L. (2012). Urban image analysis through visual surveys. Craiova town (Romania) as a case study. In: Forum Geografic, XI(2), 223\u2013228. issn: 1583-1523, 2067-4635. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.5775\/fg.2067-4635.2012.085.d. url: https:\/\/2.zoppoz.workers.dev:443\/http\/forumgeografic.ro\/2013\/1452\/. (visited on 03\/13\/2025).","DOI":"10.5775\/fg.2067-4635.2012.085.d"},{"key":"96_CR5","doi-asserted-by":"publisher","unstructured":"Becker, A. M., et al. (2024). Environmental tracking for healthy mobility. In: Volunteered geographic information: interpretation, visualization and social context. Ed. by Dirk Burghardt, Elena Demidova, and Daniel A. Keim. Cham: Springer Nature Switzerland, pp. 221\u2013239. isbn: 978\u20133\u2013031\u201335374\u20131. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-031-35374-1.11. (visited on 02\/23\/2024).","DOI":"10.1007\/978-3-031-35374-1.11"},{"key":"96_CR6","doi-asserted-by":"publisher","unstructured":"Biljecki, F., & Ito, K. (2021). Street view imagery in urban analytics and GIS: A review. In: Landscape and Urban Planning, 215, 104217. issn: 01692046. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.landurbplan.2021.104217\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S0169204621001808. (visited on 03\/05\/2025).","DOI":"10.1016\/j.landurbplan.2021.104217"},{"key":"96_CR7","doi-asserted-by":"publisher","unstructured":"Boeing, G. (2020). Urban street network analysis in a computational notebook. In: REGION, 6(3), 39\u201351. issn: 2409-5370.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.18335\/region.v6i3.278. arXiv: 2001.06505[physics]. url: https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/2001.06505. (visited on 01\/31\/2025).","DOI":"10.18335\/region.v6i3.278"},{"key":"96_CR8","doi-asserted-by":"crossref","unstructured":"Boucsein, W., (2012). Electrodermal activity. Second edition. New York: Springer Science. 618 pp. isbn: 978\u20131\u20134614\u20131125\u20133.","DOI":"10.1007\/978-1-4614-1126-0"},{"key":"96_CR9","doi-asserted-by":"publisher","unstructured":"Chen, C., et al. (2022). Predicting the effect of street environment on residents\u2019 mood states in large urban areas using machine learning and street view images. In: Science of the Total Environment, 816, 151605. issn: 00489697.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.scitotenv.2021.151605. url: https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S004896972106681X.\u00a0(visited on 06\/12\/2024).","DOI":"10.1016\/j.scitotenv.2021.151605"},{"key":"96_CR10","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler M., Benenson, R., Franke, U., Roth, S., Schiele,\u00a0B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding, in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/www.cityscapes-dataset.com\/citation\/","DOI":"10.1109\/CVPR.2016.350"},{"key":"96_CR11","doi-asserted-by":"publisher","unstructured":"Dritsa, D., & Biloria, N. (2021). Mapping the urban environment using real-time physiological monitoring. In: Archnet-IJAR: International Journal of Architectural Research, 15(3), 467\u2013486. issn: 2631-6862, 1938-7806. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1108\/ARCH-02-2021-0041\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.emerald.com\/insight\/content\/doi\/10.1108\/ARCH-02-2021-0041\/full\/html. (visited on 01\/31\/2025).","DOI":"10.1108\/ARCH-02-2021-0041"},{"key":"96_CR12","unstructured":"Empatica (2024). E4 wristband \u2014 Real-time physiological signals \u2014 Wearable PPG, EDA, Temperature, Motion sensors. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.empatica.com\/research\/e4\/. (visited on 12\/16\/2024)."},{"key":"96_CR13","doi-asserted-by":"publisher","unstructured":"Everly, G.S. and J.M. Lating (2019). A clinical guide to the treatment of the human stress response. New York, NY: Springer. isbn: 978-1-4939-9097-9.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-1-4939-9098-6. url: https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/978-14939-9098-6 (visited on 12\/16\/2024).","DOI":"10.1007\/978-1-4939-9098-6"},{"key":"96_CR14","doi-asserted-by":"publisher","unstructured":"Fan, Z., et al. (2023).\u00a0Urban visual intelligence: Uncovering hidden city profiles with street view images. In: Proceedings of the National Academy of Sciences of the United States of America, 120(27), e2220417120. issn: 0027-8424.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1073\/pnas.2220417120. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10319000\/. (visited on 03\/13\/2025).","DOI":"10.1073\/pnas.2220417120"},{"key":"96_CR15","doi-asserted-by":"publisher","unstructured":"Giannakakis, G., et al. (2022). Review on psychological stress detection using biosignals. In: IEEE Transactions on Affective Computing 13.1. Conference name: IEEE transactions on affective computing, pp. 440\u2013460. issn: 1949-3045.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/TAFFC.2019.2927337. url: https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/8758154. (visited on 04\/04\/2024).","DOI":"10.1109\/TAFFC.2019.2927337"},{"key":"96_CR16","doi-asserted-by":"publisher","unstructured":"Grassi, G. et al. (2017). Parkmaster: an in-vehicle, edge-based video analytics service for detecting open parking spaces in urban environments. In: Proceedings of the second ACM\/IEEE symposium on edge computing. SEC \u201917. New York, NY, USA: Association for Computing Machinery, pp. 1\u201314. isbn: 978-1-4503-50877.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3132211.3134452. url: https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3132211.3134452. (visited on 03\/17\/2025).","DOI":"10.1145\/3132211.3134452"},{"key":"96_CR17","doi-asserted-by":"publisher","unstructured":"Han, X., et al. (2022). Measuring perceived psychological stress in urban built environments using google street view and deep learning. In: Frontiers in Public Health, 10, 891736. issn: 2296-2565. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3389\/fpubh.2022.891736","DOI":"10.3389\/fpubh.2022.891736"},{"key":"96_CR18","doi-asserted-by":"publisher","unstructured":"Helbich, M., et al. (2016).\u00a0Natural and built environmental exposures on children\u2019s active school travel: A Dutch global positioning system-based crosssectional study. In: Health & Place, 39, 101\u2013109. issn: 1353-8292.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.healthplace.2016.03.003. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S1353829216300120. (visited on 06\/16\/2024).","DOI":"10.1016\/j.healthplace.2016.03.003"},{"key":"96_CR19","doi-asserted-by":"publisher","unstructured":"Helbich, M. (2018). Toward dynamic urban environmental exposure assessments in mental health research. In: Environmental Research, 161, 129\u2013135. issn: 00139351. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.envres.2017.11.006\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0013935117312550. (visited on 01\/18\/2024).","DOI":"10.1016\/j.envres.2017.11.006"},{"key":"96_CR20","doi-asserted-by":"publisher","unstructured":"Helbig, C., et al. (2021).\u00a0Wearable sensors for human environmental exposure in urban settings. In: Current Pollution Reports, 7(3), 417\u2013433. issn: 2198-6592. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s40726-021-00186-4. (visited on 05\/31\/2023).","DOI":"10.1007\/s40726-021-00186-4"},{"key":"96_CR21","doi-asserted-by":"publisher","unstructured":"Jalilian, E., and A. Uhl (2018). Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: The impact of training data. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). ISSN: 2157-4774, pp. 1\u20138.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/WIFS.2018.8630794. url: https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/8630794. (visited on 11\/19\/2025).","DOI":"10.1109\/WIFS.2018.8630794"},{"key":"96_CR22","doi-asserted-by":"publisher","unstructured":"Jeong, S. I., Jeong, M. S., & Park, K. R. (2025). Estimation of fractal dimension and semantic segmentation of motion-blurred images by knowledge distillation in autonomous vehicle. In: Fractal and Fractional, 9(7), 460. issn: 2504-3110. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/fractalfract9070460\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2504-3110\/9\/7\/460. (visited on 11\/20\/2025).","DOI":"10.3390\/fractalfract9070460"},{"key":"96_CR23","doi-asserted-by":"publisher","unstructured":"Juh\u00b4asz, L., & Hochmair, H. H. (2016). User contribution patterns and completeness evaluation of Mapillary, a crowdsourced street level photo service. In: Transactions in GIS, 20(6), 925\u2013947. issn: 1361-1682, 1467-9671. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1111\/tgis.12190\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1111\/tgis.12190. (visited on 01\/31\/2025).","DOI":"10.1111\/tgis.12190"},{"key":"96_CR24","doi-asserted-by":"publisher","unstructured":"Keralis, J. M., et al. (2020). Health and the built environment in United States cities: Measuring associations using Google Street View-derived indicators of the built environment. In: BMC Public Health, 20(1), 215. issn: 1471-2458. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1186\/s12889-020-8300-1\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/bmcpublichealth.biomedcentral.com\/articles\/10.1186\/s12889-020-8300-1. (visited on 06\/12\/2024).","DOI":"10.1186\/s12889-020-8300-1"},{"key":"96_CR25","doi-asserted-by":"publisher","unstructured":"Ki, D., Chen, Z., et al. (2023). A novel walkability index using Google Street View and deep learning. In: Sustainable Cities and Society, 99, 104896. issn: 2210-6707. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.scs.2023.104896\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S2210670723005073. (visited on 01\/18\/2024).","DOI":"10.1016\/j.scs.2023.104896"},{"key":"96_CR26","doi-asserted-by":"publisher","unstructured":"Ki, D., & Lee, S. (2021). Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. In: Landscape and Urban Planning, 205, 103920. issn: 01692046. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.landurbplan.2020.103920\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0169204620301018. (visited on 06\/12\/2024).","DOI":"10.1016\/j.landurbplan.2020.103920"},{"key":"96_CR27","doi-asserted-by":"publisher","unstructured":"Kyriakou, K., and B. Resch (2019). Spatial analysis of moments of stress derived from wearable sensor data. In: Advances in cartography and GIScience of the ICA 2. Publisher: Copernicus GmbH, pp. 1\u20138.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.5194\/icaadv-2-9-2019. url: https:\/\/2.zoppoz.workers.dev:443\/https\/ica-adv.copernicus.org\/articles\/2\/9\/2019\/. (visited on 01\/22\/2024).","DOI":"10.5194\/icaadv-2-9-2019"},{"key":"96_CR28","doi-asserted-by":"publisher","unstructured":"Kyriakou, K., Resch, B., et al. (2019). Detecting moments of stress from measurements of wearable physiological sensors. In: Sensors, 19(17). Number: 17 Publisher: Multidisciplinary Digital Publishing Institute, p. 3805. issn: 1424-8220.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s19173805. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/19\/17\/3805. (visited on 01\/22\/2024).","DOI":"10.3390\/s19173805"},{"key":"96_CR29","doi-asserted-by":"publisher","unstructured":"Li, X., et al. (2015). Assessing street-level urban greenery using Google Street View and a modified green view index. In: Urban Forestry & Urban Greening, 14(3), 675\u2013685. issn: 16188667. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ufug.2015.06.006\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S1618866715000874. (visited on 06\/12\/2024).","DOI":"10.1016\/j.ufug.2015.06.006"},{"key":"96_CR30","doi-asserted-by":"publisher","unstructured":"Liu, L., & Sevtsuk, A. (2024). Clarity or confusion: A review of computer vision street attributes in urban studies and planning. In: Cities, 150, 105022. issn: 0264-2751. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.cities.2024.105022\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S0264275124002361. (visited on 04\/23\/2024).","DOI":"10.1016\/j.cities.2024.105022"},{"key":"96_CR31","doi-asserted-by":"publisher","unstructured":"Markkula, G., et al. (2023). Explaining human interactions on the road by large-scale integration of computational psychological theory. In: PNAS Nexus, 2(6), pgad163. issn: 2752-6542. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1093\/pnasnexus\/pgad163","DOI":"10.1093\/pnasnexus\/pgad163"},{"key":"96_CR32","doi-asserted-by":"publisher","unstructured":"Millar, G. C., et al. (2021). Space-time analytics of human physiology for urban planning. In: Computers, Environment and Urban Systems, 85, 101554. issn: 0198-9715. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.compenvurbsys.2020.101554\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S0198971520302878. (visited on 09\/29\/2025).","DOI":"10.1016\/j.compenvurbsys.2020.101554"},{"key":"96_CR33","doi-asserted-by":"crossref","unstructured":"Miller, Harvey J. (2004). Tobler\u2019s first law and spatial analysis. In: Annals of the Association of American Geographers, 94(2). Publisher: [Association of American Geographers, Taylor & Francis, Ltd.], pp. 284\u2013289. issn: 0004-5608. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.jstor.org\/stable\/3693985. (visited on 05\/09\/2025).","DOI":"10.1111\/j.1467-8306.2004.09402005.x"},{"key":"96_CR34","doi-asserted-by":"publisher","unstructured":"Morra, D., et al. (2024).\u00a0Mapping sidewalk accessibility with smartphone imagery and Visual AI: a participatory approach. In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 382(2285). Publisher: Royal Society, p. 20240106.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1098\/rsta.2024.0106. url: https:\/\/2.zoppoz.workers.dev:443\/https\/royalsocietypublishing.org\/doi\/full\/10.1098\/rsta.2024.0106. (visited on 03\/17\/2025).","DOI":"10.1098\/rsta.2024.0106"},{"key":"96_CR35","doi-asserted-by":"publisher","unstructured":"Moser, M. K., Ehrhart, M., & Resch, B. (2024). An explainable deep learning approach for stress detection in wearable sensor measurements. In: Sensors, 24(16). Number: 16 Publisher: Multidisciplinary Digital Publishing Institute, p. 5085. issn: 1424-8220.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s24165085. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/24\/16\/5085. (visited on 03\/17\/2025).","DOI":"10.3390\/s24165085"},{"key":"96_CR36","doi-asserted-by":"publisher","unstructured":"Moser, M. K., Resch, B., & Ehrhart, M. (2023). An individual-oriented algorithm for stress detection in wearable sensor measurements. In: IEEE Sensors Journal, 23(19), 22845\u201322856. issn: 1558-1748. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/JSEN.2023.3304422\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/10221780. (visited on 04\/04\/2024).","DOI":"10.1109\/JSEN.2023.3304422"},{"key":"96_CR37","doi-asserted-by":"publisher","unstructured":"Nieuwenhuijsen, M. J. (2016). Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities. In: Environmental Health, 15(1), S38. issn: 1476-069X. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1186\/s12940-016-0108-1. (visited on 04\/15\/2025).","DOI":"10.1186\/s12940-016-0108-1"},{"key":"96_CR38","doi-asserted-by":"publisher","unstructured":"Ogawa, Y., et al. (2024). Evaluating the subjective perceptions of streetscapes using street-view images. In: Landscape and Urban Planning, 247, 105073. issn: 0169-2046. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.landurbplan.2024.105073\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S0169204624000720. (visited on 02\/03\/2025).","DOI":"10.1016\/j.landurbplan.2024.105073"},{"key":"96_CR39","doi-asserted-by":"publisher","unstructured":"Panter, J. R., and A. Jones (2010). Attitudes and the environment as determinants of active travel in adults: what do and don\u2019t we know?\u00a0In: Journal of Physical Activity & Health, 7(4), 551\u2013561. issn: 1543\u20133080. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1123\/jpah.7.4.551","DOI":"10.1123\/jpah.7.4.551"},{"key":"96_CR40","doi-asserted-by":"publisher","unstructured":"Panter, J. R., Jones, A. P., & van Sluijs, E. M. F. (2008).\u00a0Environmental determinants of active travel in youth: A review and framework for future research. In: International Journal of Behavioral Nutrition and Physical Activity, 5(1), 34. issn: 1479-5868. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1186\/1479-5868-5-34. (visited on 06\/13\/2024).","DOI":"10.1186\/1479-5868-5-34"},{"key":"96_CR41","doi-asserted-by":"publisher","unstructured":"Petutschnig, A., et al. (2022). An eDiary app approach for collecting physiological sensor data from wearables together with subjective observations and emotions. In:\u00a0Sensors (Basel, Switzerland), 22(16), 6120. issn: 1424-8220. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s22166120","DOI":"10.3390\/s22166120"},{"key":"96_CR42","doi-asserted-by":"publisher","unstructured":"Ravi, N., et al. (2024). SAM 2: Segment anything in images and videos.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.48550\/arXiv.2408.00714. arXiv: 2408.00714[cs]. url: https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/2408.00714. (visited on 03\/17\/2025).","DOI":"10.48550\/arXiv.2408.00714"},{"key":"96_CR43","doi-asserted-by":"publisher","unstructured":"Resch, B., et al. (2020). An interdisciplinary mixed-methods approach to analyzing urban spaces: The case of urban walkability and bikeability. International Journal of Environmental Research and Public Health, 17(19), 6994. issn: 1660-4601. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/ijerph17196994\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/16604601\/17\/19\/6994. (visited on 01\/22\/2024).","DOI":"10.3390\/ijerph17196994"},{"key":"96_CR44","doi-asserted-by":"publisher","unstructured":"Rita, Lu. \u00b4\u0131s, et al. (2023). Using deep learning and Google Street View imagery to assess and improve cyclist safety in London.\u00a0In:\u00a0Sustainability, 15(13). Publisher: Multidisciplinary Digital Publishing Institute, p. 10270. issn: 2071-1050.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/su151310270. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2071-1050\/15\/13\/10270. (visited on 09\/29\/2025).","DOI":"10.3390\/su151310270"},{"key":"96_CR45","doi-asserted-by":"publisher","unstructured":"Schwonberg, M. and H. Gottschalk (2025). Domain generalization for semantic segmentation: A survey.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.48550\/arXiv.2510.03540. arXiv: 2510.03540[cs]. url: https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/2510.03540. (visited on 11\/10\/2025).","DOI":"10.48550\/arXiv.2510.03540"},{"key":"96_CR46","doi-asserted-by":"publisher","unstructured":"Sharif, M. S., et al. (2023). An innovative random-forest-based model to assess the health impacts of regular commuting using non-invasive wearable sensors. In: Sensors, 23(6), 3274. issn: 1424-8220. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/s23063274\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/23\/6\/3274. (visited on 09\/30\/2025).","DOI":"10.3390\/s23063274"},{"key":"96_CR47","doi-asserted-by":"publisher","unstructured":"Stjernborg, V. (2024). Triggers for feelings of insecurity and perceptions of safety in relation to public transport; The experiences of young and active travellers. In: Applied Mobilities, 0(0), 1\u201321. issn: 2380-0127. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1080\/23800127.2024.2318095. (visited on 08\/20\/2025).","DOI":"10.1080\/23800127.2024.2318095"},{"key":"96_CR48","doi-asserted-by":"publisher","unstructured":"Stu\u00a8lpnagel, R. V. (2020). Gaze behavior during urban cycling: Effects of subjective risk perception and vista space properties. In:\u00a0Transportation Research Part F: Traffic Psychology and Behaviour, 75, 222\u2013238. issn: 1369-8478.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.trf.2020.10.007. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.sciencedirect.com\/science\/article\/pii\/S1369847820305556. (visited on 02\/28\/2024).","DOI":"10.1016\/j.trf.2020.10.007"},{"key":"96_CR49","doi-asserted-by":"publisher","unstructured":"Titze, S., et al. (2008). Association of built-environment, social-environment and personal factors with bicycling as a mode of transportation among Austrian city dwellers. In: Preventive Medicine, 47(3), 252\u2013259. issn: 1096-0260. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.ypmed.2008.02.019","DOI":"10.1016\/j.ypmed.2008.02.019"},{"key":"96_CR50","doi-asserted-by":"publisher","unstructured":"Tost, H., Champagne, F. A., & Meyer-Lindenberg, A. (2015). Environmental influence in the brain, human welfare and mental health. In: Nature Neuroscience, 18(10), 1421\u20131431. issn: 1546-1726. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1038\/nn.4108","DOI":"10.1038\/nn.4108"},{"key":"96_CR51","doi-asserted-by":"publisher","unstructured":"Werner, C., Resch, B., & Loidl, M. (2019). Evaluating urban bicycle infrastructures through intersubjectivity of stress sensations derived from physiological measurements. ISPRS International Journal of Geo-Information, 8(6), 265. issn: 2220-9964. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/ijgi8060265\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/22209964\/8\/6\/265. (visited on 01\/22\/2024).","DOI":"10.3390\/ijgi8060265"},{"key":"96_CR52","doi-asserted-by":"publisher","unstructured":"Xie, E. et al. (2021). SegFormer: Simple and efficient design for semantic segmentation with transformers.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.48550\/arXiv.2105.15203. arXiv: 2105. 15203. url: https:\/\/2.zoppoz.workers.dev:443\/http\/arxiv.org\/abs\/2105.15203. (visited on 10\/18\/2024).","DOI":"10.48550\/arXiv.2105.15203"},{"key":"96_CR53","doi-asserted-by":"publisher","unstructured":"Yap, W., Stouffs, R., & Biljecki, F. (2023). Urbanity: Automated modelling and analysis of multidimensional networks in cities. In: Npj Urban Sustainability, 3(1). Publisher: Nature Publishing Group, pp. 1\u201311. issn: 2661-8001. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1038\/s42949-023-00125-w\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.nature.com\/articles\/s42949-02300125-w. (visited on 04\/18\/2024).","DOI":"10.1038\/s42949-023-00125-w"},{"key":"96_CR54","doi-asserted-by":"publisher","unstructured":"Yu, B., et al. (2018). Biofeedback for everyday stress management: A systematic review. In: Frontiers in ICT. issn: 2297-198X. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3389\/fict.2018.00023\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.frontiersin.org\/journals\/ict\/articles\/10.3389\/fict.2018.00023\/full. (visited on 01\/07\/2025).","DOI":"10.3389\/fict.2018.00023"},{"key":"96_CR55","doi-asserted-by":"publisher","unstructured":"Yue, Z., et al. (2024). Urban aquatic scene expansion for semantic segmentation in Cityscapes. In: Urban Science, 8(2), 23. Publisher: Multidisciplinary Digital Publishing Institute, p. 23. issn: 2413-8851. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/urbansci8020023\n. url: https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2413-8851\/8\/2\/23. (visited on 09\/24\/2025).","DOI":"10.3390\/urbansci8020023"},{"key":"96_CR56","doi-asserted-by":"publisher","unstructured":"Zeile, P., et al. (2016). Urban emotions and cycling experience \u2013 enriching traffic planning for cyclists with human sensor data. In:\u00a0GI Forum 2016,\u00a04.\u00a0Publisher: Verlag der Osterreichischen Akademie der Wissenschaften,\u00a8 pp. 204\u2013216. issn: 2308\u20131708.\u00a0https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1553\/giscience201601s204. url: https:\/\/2.zoppoz.workers.dev:443\/https\/austriaca.at?arp=0x0033ffa1. (visited on 01\/08\/2025).","DOI":"10.1553\/giscience201601s204"},{"key":"96_CR57","doi-asserted-by":"publisher","unstructured":"Zhang, F., et al. (2024). Urban visual intelligence: Studying cities with artificial intelligence and street-level imagery. In: Annals of the American Association of Geographers, 114(5), 876\u2013897. issn: 2469-4452. url: https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1080\/24694452.2024.2313515. (visited on 07\/01\/2024).","DOI":"10.1080\/24694452.2024.2313515"},{"key":"96_CR58","doi-asserted-by":"publisher","unstructured":"Zhou, B., et al. (2017). Scene parsing through ADE20K dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). ISSN: 1063\u20136919, pp. 5122\u20135130. https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2017.544. url: https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/8100027. (visited on 03\/13\/2025).","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Urban Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s44212-025-00096-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s44212-025-00096-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s44212-025-00096-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T01:02:29Z","timestamp":1770771749000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/s44212-025-00096-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["96"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s44212-025-00096-6","relation":{},"ISSN":["2731-6963"],"issn-type":[{"value":"2731-6963","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,11]]},"assertion":[{"value":"23 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}