Aerial images provide important situational awareness for responding to natural disasters such as hurricanes. They are well-suited for providing information for damage estimation and localization (DEL); i.e., characterizing the type and spatial extent of damage following a disaster. Despite recent advances in sensing and unmanned aerial systems technology, much of post-disaster aerial imagery is still taken by handheld DSLR cameras from small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU information, and images are taken opportunistically post-event by operators. As such, DEL from such imagery is still a highly manual and time-consuming process. We propose an approach to both detect damage in aerial images and localize it in world coordinates, with specific focus on detecting and localizing flooding. The approach is based on using structure from motion to relate image coordinates to world coordinates via a projective transformation, using class activation mapping to detect the extent of damage in an image, and applying the projective transformation to localize damage in world coordinates. We evaluate the performance of our approach on post-event data from the 2016 Louisiana floods, and find that our approach achieves a precision of 88%. Given this high precision using limited data, we argue that this approach is currently viable for fast and effective DEL from handheld aerial imagery for disaster response.

翻译：尽管遥感和无人驾驶航空系统技术最近有所进步,但许多灾后空中图像仍由小型、有人驾驶和固定翼飞机的DSRR手持摄影机拍摄,但这些手持相机缺乏IMU信息,而且操作者在事后对图像进行随机拍摄,因此,从这些图像中获取的DEL仍是一个高度人工和耗时的过程。我们提议一种方法,既探测航空图像中的损坏,又将其定位于世界坐标,特别侧重于探测和定位洪水。该方法基于从运动结构上将图像坐标与世界坐标联系起来,通过投影转换,利用班级启动绘图以探测图像损坏的程度,并将投影转换用于世界坐标上的破坏。我们评估了我们从2016年路易斯安那洪水中获取的事件后数据的方法的性能,我们发现我们目前采用的方法在空中图像中达到了一种可维持的精确度,我们用这种精确度来进行这种精确度,我们用这种精确度来进行这种精确度,我们用这种精确度来进行空中测量。