## 2 Related Works  
The collection and availability of high-quality datasets play a pivotal role in advancing research and development in fields like computer vision, autonomous navigation, and urban planning. Although many datasets have been developed for urban research, there is a clear lack of data on important but often ignored parts of the city like sidewalks and pedestrian routes. This section provides an overview of existing datasets and highlights the unique contribution of our work in addressing this gap. **Road-Centric Datasets:** Many existing datasets predominantly focus on data collected from vehicular perspectives and road networks, reflecting the significant emphasis on mobile vehicles in the realm of autonomous navigation and traffic management. Notable examples include the KITTI vision benchmark suite [7], KITTI-360 [8], Cityscapes [9], Oxford [10], ApolloScape [11], and Waymo [12], while these resources have been instrumental in advancing research in autonomous driving and urban traffic systems, they are primarily oriented towards vehicular environments. Similarly, while datasets like UrbanLoco [13] and Hong Kong UrbanNav [14] attempt to fill the gap by including raw GNSS measurements for detailed navigation research, they mainly focus on highly urbanized, vehicle-centric environments. These datasets, with their comprehensive coverage, have set the standard for autonomous vehicle technologies and traffic analysis methodologies. However, their predominant focus on vehicular viewpoints often leads to the neglect of the intricacies and unique challenges present in pedestrian areas. This oversight in existing datasets underscores a critical need in the field for resources that provide detailed insights into pedestrian spaces, essential for a more holistic understanding of urban mobility and safety. Such comprehensive data would enable the development of autonomous systems capable of navigating and operating safely in the full spectrum of urban environments, addressing both vehicular and pedestrian dynamics. **Sidewalk-Centric Datasets:** While a segment of existing datasets provides insights into pedestrian dynamics, they primarily capture limited scenarios such as crowd behavior in public spaces or indoor tracking. These specialized datasets, although valuable, do not reflect the full spectrum of pedestrian interactions within the diverse urban landscapes encountered daily. Recent advances in deep learning have notably enhanced computer vision techniques like semantic segmentation, offering more granular analysis of urban scenes. Despite this, the field still grapples with a significant shortage of quality training  
Figure 1: An overview of the platform, showcasing our multisensor datasets, segmentation techniques, sound event classifications, and the study area in Tartu, Estonia. data for pedestrian infrastructure analysis. In recent research endeavors within urban data collection for pedestrian infrastructure, a variety of innovative methodologies have been utilized to develop specialized datasets. Among these, Tile2 Net [15] employs semantic segmentation of aerial imagery to map pedestrian infrastructures, showcasing an aerial perspective in data collection. SideGuide [16] takes a unique approach by integrating feedback from interviews with impaired individuals, focusing on sidewalk-specific objects and combining crowd-sourced and stereo data to form its dataset. The study by [17] represents a distinct method, using Google Street View (GSV) panoramas for assessing sidewalk accessibility. This approach utilizes existing online map imagery as a primary data source. In contrast, the PESID [18] project concentrates on pedestrian safety, developing its dataset from a collection of labeled images depicting various sidewalk environments. Further diversifying the data collection techniques, ref. [19] combined results from both aerial and street view imagery to create a comprehensive sidewalk network dataset, this integrative method synthesizes data from multiple sources.