PhD Showcase: Initial validation of non-authoritative data for road assessment PhD Student: E.Schnebele George Mason University 4400 University Drive Fairfax, VA 22030, USA eschnebe@gmu.edu PhD Supervisor: G.Cervone The Pennsylvania State University 302 Walker Building University Park, PA 16802, USA cervone@ucar.edu PhD Supervisor: N.Waters Center for Excellence in GIS George Mason University 4400 Unversity Drive Fairfax, VA 22030, USA nwaters@gmu.edu ABSTRACT This research proposes a methodology that leverages non- authoritative data to augment flood extent mapping and the evaluation of transportation infrastructure. The novelty of this approach is the application of freely available, non-authoritative data and its integration with established data and methods. Crowdsourced photos and volunteered geographic data are fused together using a geostatistical interpolation to create an estimation of flood damage in New York City following Hurricane Sandy. This damage assessment is utilized to augment an authoritative storm surge map as well as to create a road damage map for the affected region. Categories and Subject Descriptors J.2 [Physical Sciences and Engineering]: Earth and atmospheric sciences General Terms Management Keywords Road Assessment, Remote Sensing, Natural Hazards 1. INTRODUCTION Accurate and timely flood assessments are critical during all phases of a flood disaster. Knowledge of road conditions and accessibility is especially important for emergency managers, first responders, and residents. Over the past two decades, satellite remote sensing has become the standard technique to determine flood extent. Satellite remote sensing data provide high spatial resolution even for areas of poor accessibility or lacking in ground measurements The SIGSPATIAL Special, Volume 5, Number 3, November 2013. Copyright is held by the author/owner(s). [18]. However, in some events, particularly hurricanes, high resolution satellite data might be unavailable for days because of cloud cover or orbit revisit time. Satellite data are often supplemented with additional data, such as digital elevation models (DEM) and river gauge data, to provide a more comprehensive flood assessment [27, 1]. RADAR data, in particular, are a good resource for flood identification because of the capability to distinguish water bodies from other land cover while penetrating through vegetative canopy and cloud cover [12, 24]. Because the application of RADAR data can be difficult due to limited swaths and long revisit times, there are many recent efforts for increasing RADAR’s availability and accessibility. For example, [10] illustrate how a RADAR instrument on an unmanned aerial vehicle (UAV) can be used for flood assessment of targeted areas. [19] propose a multi-sensor approach by combining satellite, aerial, and ground data for a more accurate flood assessment. They test how a RADAR sensor onboard a UAV can provide useful data. Aerial platforms, both manned and unmanned, are particularly suited for coastal monitoring after major catastrophic events because they can fly below the clouds, and thus acquire data in a targeted and timely fashion. Remote sensing data are also used to catalog damages to the built environment. For the evaluation of transportation infrastructure following Hurricane Katrina, a variety of assessment techniques were utilized including visual, non- destructive, and remote sensing. However, the assessment of transportation infrastructure over such a large area could have been accelerated through the use of high resolution imagery and geospatial analysis [25]. Recent studies have focused on the application of remote sensing data after earthquakes or flooding specifically to assess transportation networks. [2] used multi-sensor, multi- temporal imagery to identify flooded roads. [4] identified infrastructure and road damages after the 2008 Wenchuan earthquake, using pre- and post-disaster very high resolution (VHR) optical imagery (1m or better). The combination of optical satellite imagery with a DEM to assess roads for accessibility after flooding was used to create a model for application in near-real time for emergency managers [6]. The integration of new data sources and methods with traditional approaches can provide additional information regarding on-the-ground conditions. For example, non- authoritative data are data not collected or distributed by