用卫星图+深度学习自动评估路面状况，准确率超90%。
Deep Learning for Pavement Condition Evaluation Using Satellite Imagery
- 基于深度学习分析卫星图像，实现路面状况自动评估
- 3000+张图像测试，准确率超过90%
- 适合交通管理部门快速普查路面网络
公路基础设施覆盖面积广，需频繁检测以维持公共服务能力。传统人工巡检或车载自动化检测方法成本高、耗时长。近年来，卫星系统与图像处理算法的进步为高效监测提供了新可能。得益于地面采样距离（GSD）的提升，卫星影像可捕捉更精细细节。本研究利用深度学习模型分析卫星图像，评估路面状况。收集了超过3000张路面区域的卫星图像，并结合德克萨斯州公路部PMIS数据库中的路面评价评分。实验结果显示，模型准确率超过90%。该研究为未来快速、低成本评估路面网络提供了可行路径。
原文摘要 · Abstract (English)
Civil infrastructure systems covers large land areas and needs frequent inspections to maintain their public service capabilities. The conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technology advancement, this research investigated to evaluate pavement conditions using deep learning models for analyzing satellite images. We gathered over 3,000 satellite images of pavement sections, together with pavement evaluation ratings from TxDOT's PMIS database. The results of our study show an accuracy rate is exceeding 90%. This research paves the way for a rapid and cost-effective approach to evaluating the pavement network in the future.
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