Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment

11/20/2016
by   N. Attari, et al.
0

We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two components. The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification. In the second component, a hidden layer of a Convolutional Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments generated from the first component in order to help discriminate between different levels of damage. To showcase our approach we use data from UAVs that were deployed to assess the level of damage in the aftermath of a devastating cyclone that hit the island of Vanuatu in 2015. The collected images were labeled by a crowdsourcing effort and the labeling categories consisted of fine-grained levels of damage to built structures. Since our data set is relatively small, a pre- trained network for pixel-level classification and FV encoding was used. Nazr-CNN attains promising results both for object detection and damage assessment suggesting that the integrated pipeline is robust in the face of small data sets and labeling errors by annotators. While the focus of Nazr-CNN is on assessment of UAV images in a post-disaster scenario, our solution is general and can be applied in many diverse settings. We show one such case of transfer learning to assess the level of damage in aerial images collected after a typhoon in Philippines.

READ FULL TEXT

page 1

page 5

page 6

page 7

page 8

research
08/11/2019

Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Object detection from images captured by Unmanned Aerial Vehicles (UAVs)...
research
02/24/2022

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

Due to climate change, we can observe a recent surge of natural disaster...
research
10/10/2021

Fast and Robust Structural Damage Analysis of Civil Infrastructure Using UAV Imagery

The usage of Unmanned Aerial Vehicles (UAVs) in the context of structura...
research
12/31/2019

Comparison of object detection methods for crop damage assessment using deep learning

Severe weather events can cause large financial losses to farmers. Detai...
research
12/23/2022

xFBD: Focused Building Damage Dataset and Analysis

The xView2 competition and xBD dataset spurred significant advancements ...
research
10/04/2019

Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector

Automatic post-disaster damage detection using aerial imagery is crucial...

Please sign up or login with your details

Forgot password? Click here to reset