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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <title>Disabled Parking CV: Scalable Methods to Analyze Disability Parking using Computer Vision and High-Resolution Aerial and Streetscape Images</title>
      <link>https://rip.trb.org/View/2553158</link>
      <description><![CDATA[People with disabilities disproportionately rely on public transportation to access employment, education, and healthcare services; however, public transit is not always available or equally distributed, which excludes social and community participation. Car transit is thus the only viable alternative. Since the Americans with Disability Act (ADA) of 1990, 4-8% of public parking spaces need to be reserved for drivers/passengers with disabilities, providing wide, accessible spaces close to destinations. And yet, there has been no systematic, large-scale study of the allocation and sizes of disability parking spaces across the US. The limited prior work that does exist has employed questionnaire methods to survey disabled drivers or examines the appropriate design of the disabled parking spot itself (e.g., its dimensions).

In this project, the research team proposes building and evaluating state-of-the-art computer vision (CV) methods applied to emerging high resolution aerial photography—such as the open 0.08 meter/pixel orthoimagery of Washington DC (DC Orthophoto, 2021)—to semi-automatically (1) track the allocation of disability parking in public and commercial lots; (2) examine characteristics of said parking (e.g., size, access area, % of allotment to normal parking) as well as public transportation ridership usage; (3) and create new analytic metrics enabled by this approach such as such as the proximity of disabled spaces to POIs (e.g., the distance to an entrance). 

The overarching goal of this work is to create open datasets and analytics for ADA-accessible parking as well as to infuse this information into modern mapping tools (e.g., OpenStreetMaps).
]]></description>
      <pubDate>Tue, 13 May 2025 19:30:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553158</guid>
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      <title>Artificial Intelligence (AI) Frameworks for Detecting Roadway Features Along Arterial Roadways from Planimetric Satellites Imagery Data </title>
      <link>https://rip.trb.org/View/2522024</link>
      <description><![CDATA[Departments of Transportation (DOTs) at the state level play a vital role in collecting and maintaining highway inventory data to support informed decision-making across various operational levels. Traditionally, these efforts have relied on labor-intensive and expensive processes, presenting challenges in updating and expanding inventory coverage. However, advancements in Artificial Intelligence (AI), particularly in computer vision and deep learning, offer a transformative solution to these limitations.

The overall goal of this proposed research is to develop an AI-driven framework that enables automated extraction of roadway geometric features (i.e., pedestrian crosswalks and turn lanes) from aerial imagery, advancing Ohio Department of Transportation's (ODOT's) efficiency in data collection. The associated objectives include: (1) Collect and pre-process georeferenced aerial images for detecting pedestrian crosswalks and turn lanes, annotating them to train AI algorithms effectively. (2) Design and train a deep learning model to detect specific roadway features from satellite images. (3) Develop a Geographic Information System (GIS) database to organize and store the extracted features for easy accessibility and integration with existing ODOT datasets. (4) Build a flexible framework to support future expansion, enabling the detection of additional roadway features as needed.
           ]]></description>
      <pubDate>Fri, 14 Mar 2025 13:41:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2522024</guid>
    </item>
    <item>
      <title>Aerial Infrared Scanning of Bridge Decks for Detecting and Mapping Delamination</title>
      <link>https://rip.trb.org/View/2190082</link>
      <description><![CDATA[This research is to evaluate the condition of Alaska DOT bridge decks located along the Parks Highway. The deck condition evaluations will be carried out using aerial infrared thermography (aerial IF) and corresponding visual imaging data collected from a fixed wing aircraft. Work will include the following deliverables: a final report including a description of the equipment, the data collection and analysis procedures, results of analysis, detailed comparison of aerial IR versus ground-truth data, ROI analysis, and recommendations for future implementation based on the results.]]></description>
      <pubDate>Fri, 02 Jun 2023 19:19:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2190082</guid>
    </item>
    <item>
      <title>Unmanned Aerial Systems Business Model Assessment for DOT&amp;PF</title>
      <link>https://rip.trb.org/View/1738117</link>
      <description><![CDATA[The objective of this project is to compile and produce best practices and recommendations for optimizing small unmanned aerial systems (SUAS) in the design of transportation projects. The study objectives are (1) acquire and process (orthomosaic) survey grade real-time kinematic (RTK) geotagged optical imagery, (2) evaluate the level of survey ground control required to improve the horizontal precision and accuracy of RTK geotagged imagery, (3) test and evaluate methods to increase the viability of using SUAS in arctic and sub-arctic conditions, and (4) provide documented methodologies and field training for both the SUAS acquisition of RTK geotagged optical imagery and the processing of RTK-enabled SUAS collected aerial photography.]]></description>
      <pubDate>Wed, 09 Sep 2020 18:27:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1738117</guid>
    </item>
    <item>
      <title>Automated Traffic Surveillance from an Aerial Camera Array</title>
      <link>https://rip.trb.org/View/1364458</link>
      <description><![CDATA[The overall goal of this O&amp;E Grant is to design and develop an automated aerial network monitoring system concept that can identify and track individual vehicles through a network of 16 square miles in near real-time. The research includes the development of algorithms to map the locations of the vehicles and to extract traffic parameters for data mining purposes. Three (3) experimental vehicle tracking systems have been conceptualized and are being evaluated. They are: (1) Vehicle Tracking using a raw pixel appearance model/OpenStreetMap; (2) Surf feature tracking; and (3) Deep learning using the Caffe library (UC Berkeley). Based on results to date, the research team has selected a machine learning approach to detect and track vehicles in an aerial camera array video. The third system identified above, deep learning, appears more promising than other approaches--especially with challenging video sequences with seams and variation in luminance.]]></description>
      <pubDate>Sat, 08 Aug 2015 01:01:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/1364458</guid>
    </item>
    <item>
      <title>Integrated Remote Sensing and Visualization (IRSV) System for Transportation-Infrastructure Operations and Management Phase II</title>
      <link>https://rip.trb.org/View/1263737</link>
      <description><![CDATA[In response to the U.D. Department of Transportation-Research Innovative Technology Administration (USDOT-RITA CRS-SI Initiative #2: Transportation Infrastructure Construction and Condition Assessment, this Phase 2 project (USDOT designation) is targeted at (1) validation of new Commercial Remote Sensing and Spatial Information (CRS-SI) applications for bridge management systems at the state and local levels, and (2) application of CRS-SI to existing structure condition assessment. Begun in 2007, a research partnership (University of North Carolina at Charlotte, ImageCat Incorporated, Charlotte Department of Transportation and North Carolina Department of Transportation has completed a proof-of-concept project to develop an Integrated Remote Sensing and Visualization (IRSV) System that integrates LiDAR scan and sub-inch-resolution aerial photography which promises to extend the available CRS-SI tools to enhance bridge inspection and data management. The goal of this project is to enhance IRSV performance and develop a commercialization component through extended partnerships with departments of transportation, state highway administrations and public works agencies across the country.]]></description>
      <pubDate>Fri, 27 Sep 2013 01:01:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1263737</guid>
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