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.

本研究开发了一种自动化框架，利用不同时间间隔获取的高分辨率图像中提取的数据，检测佛罗里达州的橙县、奥斯塞奥拉县和塞米诺尔县的人行横道变化，结果显示橙县有 2,094 个人行横道变化，其中 312 个发生在州内道路上，塞米诺尔县和奥斯塞奥拉县分别有 1,040 个和 1,402 个人行横道变化发生在本地和州内道路上，其中塞米诺尔县和奥斯塞奥拉县的州内道路上分别有 340 个和 344 个人行横道变化。人行横道的时空变化可以定期更新现有人行横道清单，这对从事交通和安全研究的机构非常重要，可以将从这些人行横道变化中提取的数据与交通和事故数据相结合，为决策者提供有价值的见解。

基于自动化 GIS 的双时相高分辨率航空影像检测人行横道变化的框架

Automated GIS-Based Framework for Detecting Crosswalk Changes from  Bi-Temporal High-Resolution Aerial Images

A defining characteristic of natural vision is its ability to withstand a
variety of input alterations, resulting in the creation of an invariant
representation of the surroundings. While convolutional neural networks exhibit
resilience to certain forms of spatial input variation, modifications in the
spatial and temporal aspects can significantly affect the representations of
video content in deep neural networks. Inspired by the resilience of natural
vision to input variations, we employ a simple multi-stream model to explore
its potential to address spatiotemporal changes by including temporal features.
Our primary goal is to introduce a video-trained model and evaluate its
robustness to diverse image and video inputs, with a particular focus on
exploring the role of temporal features in invariant recognition. Results show
that including videos and the temporal stream during training mitigates the
decline in accuracy and mAP in image and video understanding tasks by 1.36% and
3.14%, respectively.

通过多流模型，我们引入一个经过视频训练的模型，并评估其在各种图像和视频输入下的鲁棒性，特别关注时间特征在不变识别中的作用。结果表明，在训练中包含视频和时间流能够抑制图像和视频理解任务中准确性和平均准确率（mAP）下降分别约 1.36％和 3.14％。