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In the aftermath of large-scale disasters, such as hurricanes, floods, or earthquakes, preliminarily damage assessment (PDA) is carried out to determine the impact and magnitude of damage and meet the needs of affected individuals, businesses, and communities. Traditionally, site evaluation and consensus-based assessment techniques are used to estimate the extent of the damage. More recently, given their low-cost, ease of operation, and ability to be deployed ondemand, unmanned aerial vehicles (UAVs) are increasingly used for disaster response and mitigation. However, the resulting large volume of visual data collected by and shared among first responders and volunteer groups is not used effectively because current practices of processing such data are heavily human-dependent, extremely resource-intensive, and significantly slow compared to the fast-evolving nature and progression of disaster impact. This paper contributes to the core body of knowledge by presenting a fully annotated dataset (with the object classes people, flooded area, and damaged and undamaged building roof, car, debris, vegetation, road, and boat) and a host of convolutional neural network (CNN) models for detecting and segmenting critical objects in the aerial footage of disaster sites. For best results, two CNN-based image segmentation architectures, namely, Mask-RCNN and Pyramid Scene Parsing Network (PSPNet), are adopted (through transfer learning), trained, validated, and tested on annotated videos to detect countable and bulk objects. The paper further introduces a targeted data augmentation technique to preserve data balance, as well as a data-driven approach to splitting highly mismatched classes for better model performance. Through these improvements, the best performing Mask-RCNN model generates pixel-level segmentations of countable objects with a 51.54% mean average precision (mAP). Additionally, the best performing PSPNet models can achieve mean intersection over union (mIoU) as high as 32.17% and accuracy as high as 77.01% on bulk objects. [ABSTRACT FROM AUTHOR]

• Successful disaster management requires data describing disaster impact and extent of damage. • Aerial data collection is resource-intensive and requires offline post-processing. • We introduce convolutional neural network (CNN) models for ground object detection. • The models recognize building roofs, cars, vegetation, debris, and flooded areas. • We achieve 80.69% and 74.48% mAP for high and low altitude footage, respectively. Accurate and timely access to data describing disaster impact and extent of damage is key to successful disaster management (a process that includes prevention, mitigation, preparedness, response, and recovery). Airborne data acquisition using helicopter and unmanned aerial vehicle (UAV) helps obtain a bird's-eye view of disaster-affected areas. However, a major challenge to this approach is robustly processing a large amount of data to identify and map objects of interest on the ground in real-time. The current process is resource-intensive (must be carried out manually) and requires offline computing (through post-processing of aerial videos). This research introduces and evaluates a series of convolutional neural network (CNN) models for ground object detection from aerial views of disaster's aftermath. These models are capable of recognizing critical ground assets including building roofs (both damaged and undamaged), vehicles, vegetation, debris, and flooded areas. The CNN models are trained on an in-house aerial video dataset (named Volan2018) that is created using web mining techniques. Volan2018 contains eight annotated aerial videos (65,580 frames) collected by drone or helicopter from eight different locations in various hurricanes that struck the United States in 2017–2018. Eight CNN models based on You-Only-Look-Once (YOLO) algorithm are trained by transfer learning, i.e., pre-trained on the COCO/VOC dataset and re-trained on Volan2018 dataset, and achieve 80.69% mAP for high altitude (helicopter footage) and 74.48% for low altitude (drone footage), respectively. This paper also presents a thorough investigation of the effect of camera altitude, data balance, and pre-trained weights on model performance, and finds that models trained and tested on videos taken from similar altitude outperform those trained and tested on videos taken from different altitudes. Moreover, the CNN model pre-trained on the VOC dataset and re-trained on balanced drone video yields the best result in significantly shorter training time. [ABSTRACT FROM AUTHOR]