Automated GIS-Based Framework for Detecting Crosswalk Changes from Bi-Temporal High-Resolution Aerial Images
arxiv(2024)
摘要
Identification of changes in pavement markings has become crucial for
infrastructure monitoring, maintenance, development, traffic management, and
safety. Automated extraction of roadway geometry is critical in helping with
this, given the increasing availability of high-resolution images and
advancements in computer vision and object detection. Specifically, due to the
substantial volume of satellite and high-resolution aerial images captured at
different time instances, change detection has become a viable solution. In
this study, an automated framework is developed to detect changes in crosswalks
of Orange, Osceola, and Seminole counties in Florida, utilizing data extracted
from high-resolution images obtained at various time intervals. Specifically,
for Orange County, crosswalk changes between 2019 and 2021 were manually
extracted, verified, and categorized as either new or modified crosswalks. For
Seminole County, the developed model was used to automatically extract
crosswalk changes between 2018 and 2021, while for Osceola County, changes
between 2019 and 2020 were extracted. Findings indicate that Orange County
witnessed approximately 2,094 crosswalk changes, with 312 occurring on state
roads. In Seminole and Osceola counties, on the other hand, 1,040 and 1,402
crosswalk changes were observed on both local and state roads, respectively.
Among these, 340 and 344 were identified on state roads in Seminole and
Osceola, respectively. Spatiotemporal changes observed in crosswalks can be
utilized to regularly update the existing crosswalk inventories, which is
essential for agencies engaged in traffic and safety studies. Data extracted
from these crosswalk changes can be combined with traffic and crash data to
provide valuable insights to policymakers.
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