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1. Validating Pedestrian Infrastructure Data: How Well Do Street-View Imagery Audits Compare to Government Field Data?

   https://doi.org/10.3390/urbansci9040130  

   Askari, Sajad; Snyder, Devon; Li, Chu; Saugstad, Michael; Froehlich, Jon E; Eisenberg, Yochai (April 2025, Urban Science)

   Data on pedestrian infrastructure is essential for improving the mobility environment and for planning efficiency. Although governmental agencies are responsible for capturing data on pedestrian infrastructure mostly by field audits, most have not completed such audits. In recent years, virtual auditing based on street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to pedestrian movement planning, but concerns about the quality and reliability of opensource geospatial data pose barriers to use by governments. Limited research has compared opensource data in relation to traditional government approaches. In this study, we compare pedestrian infrastructure data from an opensource virtual sidewalk audit platform (Project Sidewalk) with government data. We focus on neighborhoods with diverse walkability and income levels in the city of Seattle, Washington and in DuPage County, Illinois. Our analysis shows that Project Sidewalk data can be a reliable alternative to government data for most pedestrian infrastructure features. The agreement for different features ranges from 75% for pedestrian signals to complete agreement (100%) for missing sidewalks. However, variations in measuring the severity of barriers challenges dataset comparisons. 

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2. Virtual Guide Dog: Next-generation pedestrian signal for the visually impaired

   https://doi.org/10.1177/1687814019883096  

   Zhong, Zijia; Lee, Joyoung (March 2020, Advances in Mechanical Engineering)

   Accessible pedestrian signal was proposed as a mean to achieve the same level of service that is set forth by the Americans with Disabilities Act for the visually impaired. One of the major issues of existing accessible pedestrian signals is the failure to deliver adequate crossing information for the visually impaired. This article presents a mobile-based accessible pedestrian signal application, namely, Virtual Guide Dog. Integrating intersection information and onboard sensors (e.g. GPS, compass, accelerometer, and gyroscope sensor) of modern smartphones, the Virtual Guide Dog application can notify the visually impaired: (1) the close proximity of an intersection and (2) the street information for crossing. By employing a screen tapping interface, Virtual Guide Dog can remotely place a pedestrian crossing call to the controller, without the need of using a pushbutton. In addition, Virtual Guide Dog informs VIs the start of a crossing phase using text-to-speech technology. The proof-of-concept test shows that Virtual Guide Dog keeps the users informed about the remaining distance as they are approaching the intersection. It was also found that the GPS-only mode is accompanied by greater distance deviation compared to the mode jointly operating with both GPS and cellular positioning. 

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3. Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

   Ruining Deng*, Can Cui* (July 2023, Medical Imaging with Deep Learning (MIDL))

   The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various seg- mentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we eval- uate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmen- tation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological seg- mentation tasks might help the model to achieve better performance in dense object segmentation. 

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4. Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

   https://doi.org/10.1016/j.compenvurbsys.2023.101950  

   Hosseini, Maryam; Sevtsuk, Andres; Miranda, Fabio; Cesar, Roberto M.; Silva, Claudio T. (April 2023, Computers, Environment and Urban Systems)

   While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. 

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5. Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures

   https://doi.org/10.3390/futuretransp1020022  

   Shourov, Chowdhury Erfan; Sarkar, Mahasweta; Jahangiri, Arash; Paolini, Christopher (September 2021, Future Transportation)

   Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, due to differences in their architectures and based on the advantages and disadvantages of each model, both models were individually used to perform two different set of tasks. Due to improved accuracy, the Faster R-CNN model was used to automate the calculation of post encroachment time, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, the Single Shot Multibox MobileNet V1 model was used. An outcome of this work is a model that can be deployed on low-cost, small-footprint mobile and IoT devices at traffic intersections with existing cameras to perform on-device inferencing for in situ Surrogate Safety Measurement (SSM), such as Time-To-Collision (TTC) and Post Encroachment Time (PET). SSM values that exceed a hazard threshold can be published to an Message Queuing Telemetry Transport (MQTT) broker, where messages are received by an intersection traffic signal controller for real-time signal adjustment, thus contributing to state-of-the-art vehicle and pedestrian safety at hazard-prone intersections. 

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