Multi-Scale Remote Sensing of Tornado Effects

Abstract

To achieve risk-based engineered structural designs that provide safety for life and property from tornadoes, sufficient knowledge of tornado wind speeds and wind flow characteristics is needed. Currently, sufficient understanding of the magnitude, frequency, and velocity structure of tornado winds remain elusive. Direct measurements of tornado winds are rare and nearly impossible to acquire, and the pursuit of in situ wind measurements can be precarious, dangerous, and even necessitating the development of safer and more reliable means to understand tornado actions. Remote-sensing technologies including satellite, aerial, lidar, and photogrammetric platforms, have demonstrated an ever-increasing efficiency for collecting, storing, organizing, and communicating tornado hazards information at a multitude of geospatial scales. Current remote-sensing technologies enable wind-engineering researchers to examine tornado effects on the built environment at various spatial scales ranging from the overall path to the neighborhood, building, and ultimately member and/or connection level. Each spatial resolution contains a unique set of challenges for efficiency, ease, and cost of data acquisition and dissemination, as well as contributions to the body of knowledge that help engineers and atmospheric scientists better understand tornado wind speeds. This paper examines the use of remote sensing technologies at four scales in recent tornado investigations, demonstrating the challenges of data collection and processing at each level as well as the utility of the information gleaned from each level in advancing the understanding of tornado effects.

Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

“…With the rapid development of technologies to collect remotely sensed 3D point clouds and the growing application of these data in various fields of civil engineering, many researchers have proposed various methods to analyze 3D point clouds, in particular for routine inspections or post-event data collection and analyses [16,17]. The datasets here are considered to be non-temporal, which is a single post-event only dataset that does not utilize change detection from a baseline (or pre-event) dataset.…”

“…Afterward, each segment was downsampled based on the selected occupancy grid dimensions. Within this study, the occupancy grid model of 64 3 was used as it results in a sampling of 10 to 16 cm for 10 m × 10 m segments, which was a sufficient resolution to perform per building damage assessment in the aftermath of wind storm events [17]. Lastly, an extra-label corresponding to the empty cells within the 3D arrays was assigned to each instance and denoted as neutral.…”

Morphometry as a Key to Investigate the Stability to a Wind-Induced Rollover of Agricultural Equipment for Irrigation

“…Even if pivot and lateral move systems are theoretically "movable" (according to the definitions given at the beginning of the previous paragraph), they will be difficultly moved away from the original place of installation, due to their important dimensions and masses. Hence, even when not in operation, these systems are exposed to the inclemency of the weather, with serious risks to be overturned by wind (as documented in Pampa, State of Texas, USA, in 2015 and in other occasions using remote-sensing imagery [12][13][14]) and, hence, to be damaged in the rollover [11] or to hurt people (e.g. typically the persons that are trying to move their irrigation system below a shelter during a windstorm).…”

Feature-Based Point Cloud-Based Assessment of Heritage Structures for Nondestructive and Noncontact Surface Damage Detection

“…In parallel, the corresponding image of 3D point cloud instances was analyzed by a support vector machine classifier to identify the damaged areas. This proposed workflow was developed and tested at the building level [18]. More recently, Nasrollahi et al utilized a well-established deep learning network, PointNet [19], to detect cracks in a flat concrete block [20].…”