A Hybrid Transfer Learning Approach to Migratable Disaster Assessment in Social Media Sensing

Zhang, Yang; Zong, Ruohan; Wang, Dong

doi:10.1109/asonam49781.2020.9381433

Cited by 15 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of ITS, integrating DL techniques into hybrid models and the strategic implementation of transfer learning strategies have heralded a new era of innovation and efficiency. Hybrid models, which harmoniously combine the capabilities of DNNs with traditional algorithms, have proven to be remarkably versatile in tackling the intricate challenges within ITS [247]. By leveraging DL's feature extraction and pattern recognition prowess with classical algorithms, these hybrid models optimize critical tasks, including traffic prediction, congestion management, and route optimization.…”

Section: E Hybrid Models and Transfer Learningmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

show abstract

Section: E Hybrid Models and Transfer Learningmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…In addition, there are unsupervised domain adaptation approaches [6], [7] that examine the damage severity by applying a domain adaption framework. These works determine two different disaster events as the source and target data and aim to accurately identify the damaged areas for a target disaster, while only the source feature representation is considered for the classification task.…”

Section: A Identifying Disaster Damagementioning

confidence: 99%

“…In addition, realtime labeling data is pricey and even impractical in some situation. Therefore, these problems make classification models impractical for timely response [6], [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data

Shekarizadeh¹,

Rastgoo²,

Al-Kuwari³

et al. 2022

Preprint

View full text Add to dashboard Cite

Social media plays a significant role in sharing essential information, which helps humanitarian organizations in rescue operations during and after disaster incidents. However, developing an efficient method that can provide rapid analysis of social media images in the early hours of disasters is still largely an open problem, mainly due to the lack of suitable datasets and the sheer complexity of this task. In addition, supervised methods can not generalize well to novel disaster incidents. In this paper, inspired by the success of Knowledge Distillation (KD) methods, we propose an unsupervised deep neural network to detect and localize damages in social media images. Our proposed KD architecture is a feature-based distillation approach that comprises a pre-trained teacher and a smaller student network, with both networks having similar GAN architecture containing a generator and a discriminator. The student network is trained to emulate the teacher's behavior on training input samples, which, in turn, contain images that do not include any damaged regions. Therefore, the student network only learns the distribution of no damage data and would have different behavior from the teacher network facing damages. To detect damage, we utilize the difference between features generated by two networks using a defined score function that demonstrates the probability of damages occurring. Our experimental results on the benchmark dataset confirm that our approach outperforms state-of-the-art methods in detecting and localizing the damaged areas, especially for novel disaster types 1 .

show abstract

“…Social media has been explored as a primary source of data for hurricane damage assessment because of the swift integrability these platforms provide to automated damage assessments (e.g., [13][14][15]). Hao and Wang [16] used five machine learning classifiers that take social networking platform images and output the damage types and severity levels presented in images.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning

Calton

Wei

2022

Applied Sciences

View full text Add to dashboard Cite

Coastal hazard events such as hurricanes pose a significant threat to coastal communities. Disaster relief is essential to mitigating damage from these catastrophes; therefore, accurate and efficient damage assessment is key to evaluating the extent of damage inflicted on coastal cities and structures. Historically, this process has been carried out by human task forces that manually take post-disaster images and identify the damaged areas. While this method has been well established, current digital tools used for computer vision tasks such as artificial intelligence and machine learning put forth a more efficient and reliable method for assessing post-disaster damage. Using transfer learning on three advanced neural networks, ResNet, MobileNet, and EfficientNet, we applied techniques for damage classification and damaged object detection to our post-hurricane image dataset comprised of damaged buildings from the coastal region of the southeastern United States. Our dataset included 1000 images for the classification model with a binary classification structure containing classes of floods and non-floods and 800 images for the object detection model with four damaged object classes damaged roof, damaged wall, flood damage, and structural damage. Our damage classification model achieved 76% overall accuracy for ResNet and 87% overall accuracy for MobileNet. The F1 score for MobileNet was also 9% higher than the F1 score of ResNet at 0.88. Our damaged object detection model achieved predominant predictions of the four damaged object classes, with MobileNet attaining the highest overall confidence score of 97.58% in its predictions. The object detection results highlight the model’s ability to successfully identify damaged areas of buildings and structures from images in a time span of seconds, which is necessary for more efficient damage assessment. Thus, we show that this level of accuracy for our damage assessment using artificial intelligence is akin to the accuracy of manual damage assessments while also completing the assessment in a drastically shorter time span.

show abstract

A Hybrid Transfer Learning Approach to Migratable Disaster Assessment in Social Media Sensing

Cited by 15 publications

References 26 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data

Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning

Contact Info

Product

Resources

About