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

Aerial imaging from UAVs (Unmanned Aerial Vehicles) permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest.

However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures.

This study aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D CNN (2D Convolutional Neural Networks) are developed based on transfer learning from two well-known networks: AlexNet and VGGNet.

In contrast, a 3DFCN (3D Fully Convolutional Network) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. 

This demonstrates the value and importance of 3D Datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.

Read more: https://www.mdpi.com/2504-446X/4/2/24/htm

UAVs for 3D mapping applications

Unmanned Aerial Vehicle (UAV) platforms are nowadays a valuable source of data for inspection, surveillance, mapping and 3D modeling issues.

As UAVs can be considered as a low cost alternative to the classical manned aerial photogrammetry, new applications in the short- and close-range domain are introduced.

Rotary or fixed wing UAVs, capable of performing the photogrammetric data acquisition with amateur or SLR digital cameras, can fly in manual, semi automated and autonomous modes.

Following a typical photogrammetric workflow, 3D results like Digital Surface or Terrain Models (DTM/DSM), contours, textured 3D models, vector information, etc. can be produced, even on large areas.

This paper explore the use of UAV for Geomatics applications, giving an interesting overview of different UAV platforms and case studies.

https://www.researchgate.net/profile/Fabio_Remondino/publication/260529522_UAV_for_3D_mapping_applications_A_review/links/00b7d532f0e4da131e000000/UAV-for-3D-mapping-applications-A-review.pdf