Enabling Quick, Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping

In order to assess damage and properly allocate relief efforts, mapping the extent of flood events is a necessary and important aspect of disaster management. In recent years, deep learning methods have evolved as an effective tool to quickly label high-resolution imagery and provide necessary flood extent mappings. These methods, though, require large amounts of annotated training data to create models that are accurate and robust to new flooded imagery. In this work, we provide FloodTrace, an application that enables effective crowdsourcing for flooded region annotation for machine learning training data, removing the requirement for annotation to be done solely by researchers. We accomplish this through two orthogonal methods within our application, informed by requirements from domain experts. First, we utilize elevation-guided annotation tools and 3D rendering to inform user annotation decisions with digital elevation model data, improving annotation accuracy. For this purpose, we provide a unique annotation method that uses topological data analysis to outperform the state-of-the-art elevation-guided annotation tool in efficiency. Second, we provide a framework for researchers to review aggregated crowdsourced annotations and correct inaccuracies using methods inspired by uncertainty visualization. We conducted a user study to confirm the application effectiveness in which 266 graduate students annotated high-resolution aerial imagery from Hurricane Matthew in North Carolina. Experimental results show the accuracy and efficiency benefits of our application apply even for untrained users. In addition, using our aggregation and correction framework, flood detection models trained on crowdsourced annotations were able to achieve performance equal to models trained on expert-labeled annotations, while requiring a fraction of the time on the part of the researcher.

Topological Data Analysis Made Easy with the Topology ToolKit

This tutorial presents topological methods for the analysis and visualization of scientific data from a user's perspective, with the Topology ToolKit (TTK), a recently released open-source library for topological data analysis. Topological methods have gained considerably in popularity and maturity over the last twenty years and success stories of established methods have been documented in a wide range of applications (combustion, chemistry, astrophysics, material sciences, etc.) with both acquired and simulated data, in both post-hoc and in-situ contexts. While reference textbooks have been published on the topic, no tutorial at IEEE VIS has covered this area in recent years, and never at a software level and from a user's point-of-view. This tutorial fills this gap by providing a beginner's introduction to topological methods for practitioners, researchers, students, and lecturers. In particular, instead of focusing on theoretical aspects and algorithmic details, this tutorial focuses on how topological methods can be useful in practice for concrete data analysis tasks such as segmentation, feature extraction or tracking. The tutorial describes in detail how to achieve these tasks with TTK. First, after an introduction to topological methods and their application in data analysis, a brief overview of TTK's main entry point for end users, namely ParaView, will be presented. Second, an overview of TTK's main features will be given. A running example will be described in detail, showcasing how to access TTK's features via ParaView, Python, VTK/C++, and C++. Third, hands-on sessions will concretely show how to use TTK in ParaView for multiple, representative data analysis tasks. Fourth, the usage of TTK will be presented for developers, in particular by describing several examples of visualization and data analysis projects that were built on top of TTK. Finally, some feedback regarding the usage of TTK as a teaching platform for topological analysis will be given. Presenters of this tutorial include experts in topological methods, core authors of TTK as well as active users, coming from academia, labs, or industry. A large part of the tutorial will be dedicated to hands-on exercises and a rich material package (including TTK pre-installs in virtual machines, code, data, demos, video tutorials, etc.) will be provided to the participants. This tutorial mostly targets students, practitioners and researchers who are not experts in topological methods but who are interested in using them in their daily tasks. We also target researchers already familiar to topological methods and who are interested in using or contributing to TTK.