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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.22092 |
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| _version_ | 1866914996933361664 |
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| author | Rulff, Joao Pereira, Giancarlo Hosseini, Maryam Lage, Marcos Silva, Claudio |
| author_facet | Rulff, Joao Pereira, Giancarlo Hosseini, Maryam Lage, Marcos Silva, Claudio |
| contents | Over the past decades, improvements in data collection hardware coupled with novel artificial intelligence algorithms have made it possible for researchers to understand urban environments at an unprecedented scale. From local interactions between actors to city-wide infrastructural problems, this new data-driven approach enables a more informed and trustworthy decision-making process aiming at transforming cities into safer and more equitable places for living. This new moment unfolded new opportunities to understand various phenomena that directly impact how accessible cities are to heterogeneous populations. Specifically, sensing localized physical interactions among actors under different scenarios can drive substantial interventions in urban environments to make them safer for all. In this manuscript, we list opportunities and associated challenges to leverage street-level multimodal sensing data to empower domain experts in making more informed decisions and, ultimately, supporting a data-informed policymaking framework. The challenges presented here can motivate research in different areas, such as computer vision and human-computer interaction, to support cities in growing more sustainably. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Data-Informed Interventions: Opportunities and Challenges of Street-level Multimodal Sensing Rulff, Joao Pereira, Giancarlo Hosseini, Maryam Lage, Marcos Silva, Claudio Human-Computer Interaction Over the past decades, improvements in data collection hardware coupled with novel artificial intelligence algorithms have made it possible for researchers to understand urban environments at an unprecedented scale. From local interactions between actors to city-wide infrastructural problems, this new data-driven approach enables a more informed and trustworthy decision-making process aiming at transforming cities into safer and more equitable places for living. This new moment unfolded new opportunities to understand various phenomena that directly impact how accessible cities are to heterogeneous populations. Specifically, sensing localized physical interactions among actors under different scenarios can drive substantial interventions in urban environments to make them safer for all. In this manuscript, we list opportunities and associated challenges to leverage street-level multimodal sensing data to empower domain experts in making more informed decisions and, ultimately, supporting a data-informed policymaking framework. The challenges presented here can motivate research in different areas, such as computer vision and human-computer interaction, to support cities in growing more sustainably. |
| title | Towards Data-Informed Interventions: Opportunities and Challenges of Street-level Multimodal Sensing |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.22092 |