An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)
An elevation-guided annotation tool for flood extent mapping on earth imagery (demo paper)
复制标题
用于在地球图像上绘制洪水范围的高程引导注释工具（演示论文）
DOI：
10.1145/3557915.3560962
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Sainju, Arpan Man
中科院分区：
文献类型：
--
作者：
Adhikari, Saugat;Yan, Da;Sami, Mirza Tanzim;Khalil, Jalal;Yuan, Lyuheng;Joy, Bhadhan Roy;Jiang, Zhe;Sainju, Arpan Man
准确和及时地绘制洪水范围图在灾害评估和救灾活动等灾害管理中起着至关重要的作用。近年来，随着卫星和无人机的广泛部署，高分辨率的光学图像变得越来越容易获得。然而，由于障碍物等噪声(例如树冠、云层)，分析此类图像数据以提取洪水范围带来了独特的挑战。在本文中，我们提出了一种高程引导的洪泛范围制图注记工具，该工具允许注记人员只提供几个像素的淹没/干燥标签，以覆盖大范围的区域，其中大多数其他像素的标签被自动推断。我们在这里用来指导自动标签推理的物理规则是，如果一个位置被泛洪(Resp.干燥)，然后其邻近位置具有较低的(分别更高的)高程也必须被淹没(分别干燥)。这样，注释者只需要标注他们确信的像素，就可以自动推断出许多模糊像素的真实标签，例如树冠像素。我们使用美国国家海洋和大气管理局(NOAA)的高分辨率航空图像、国家大地测量(NGS)以及相应的数字高程模型(DEM)数据来演示我们的注释工具的使用。利用标注后的数据训练机器学习模型绘制洪水范围图，并训练U网模型对未知区域的洪水图进行推理，取得了较高的精度。我们的注释工具在https://github.com/SaugatAdhikari/Flood-Annotation-Tool.上是开源的
Accurate and timely mapping of flood extent plays a crucial role in disaster management such as damage assessment and relief activities. In recent years, high-resolution optical imagery becomes increasingly available with the wide deployment of satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challenges due to noises such as obstacles (e.g., tree canopies, clouds). In this paper, we propose an elevation-guided annotation tool for flood extent mapping, which allows annotators to provide the flooded/dry labels for just a few pixels to cover a large area where the labels of most other pixels are automatically inferred. The physical rule we use here to guide the automatic label inference is that if a location is flooded (resp. dry), then its adjacent locations with a lower (resp. higher) elevation must also be flooded (resp. dry). In this way, annotators just need to label the pixels that they are confident with, and the true labels of many ambiguous pixels such as tree-canopy ones can be automatically inferred. We demonstrate the usage of our annotation tool using high-resolution aerial imagery from National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey (NGS) together with the corresponding Digital Elevation Model (DEM) data. The annotated data can be used to train machine learning models for flood extent mapping, and we train U-Net models to infer the flood map for an unseen region and achieve a high accuracy. Our annotation tool is open-sourced at https://github.com/SaugatAdhikari/Flood-Annotation-Tool.
DOI：
10.1145/3219819.3220053
发表时间：
2018-05
期刊：
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Miao Xie;Zhe Jiang;Arpan Man Sainju
通讯作者：
Miao Xie;Zhe Jiang;Arpan Man Sainju
基于高分辨率航空图像和 DEM 的植被茂密地区洪水范围测绘的隐马尔可夫树模型：飓风马修洪水案例研究
影响因子：
3.4
作者：
Jiang, Zhe;Sainju, Arpan Man
通讯作者：
Sainju, Arpan Man