We propose Nazr-CNN 1 , 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 pretrained 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. 1 Nazr means "sight" in Arabic. 2
Despite recent attempts in the field of explainable AI to go beyond black box prediction models, typically already the training data for supervised machine learning is collected in a manner that treats the annotator as a "black box", the internal workings of which remains unobserved. We present an annotation method where a task is given to a pair of annotators who collaborate on finding the best response. With this we want to shed light on the questions if the collaboration increases the quality of the responses and if this "thinking together" provides useful information in itself, as it at least partially reveals their reasoning steps. Furthermore, we expect that this setting puts the focus on explanation as a linguistic act, vs. explainability as a property of models. In a crowd-sourcing experiment, we investigated three different annotation tasks, each in a collaborative dialogical (two annotators) and monological (one annotator) setting. Our results indicate that our experiment elicits collaboration and that this collaboration increases the response accuracy. We see large differences in the annotators' behavior depending on the task. Similarly, we also observe that the dialog patterns emerging from the collaboration vary significantly with the task.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.