Few-Shot Learning for Post-Earthquake Urban Damage Detection

Koukouraki, Eftychia; Vanneschi, Leonardo; Paìnho, Marco

doi:10.3390/rs14010040

Cited by 9 publications

(1 citation statement)

References 32 publications

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“…In [9] a few-shot learning technique from [50] was used in order detect complex morphologies representing poor areas within the urban environment, the authors found out that the technique works very well when only a hand-full of samples are available. Other approaches have been using selfsupervised embedding optimization for adaptive generalization in urban settings [51] or using Prototypical Networks for urban damage detection after natural hazards [52].…”

Section: Transfer-learning From Few Samplesmentioning

confidence: 99%

Quantifying Uncertainty in Slum Detection: Advancing Transfer Learning With Limited Data in Noisy Urban Environments

Stark,

Wurm,

Zhu

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In the intricate landscape of mapping urban slum dynamics, the significance of robust and efficient techniques is often underestimated and remains absent in many studies. This not only hampers the comprehensiveness of research but also undermines potential solutions that could be pivotal for addressing the complex challenges faced by these settlements. With this ethos in mind, we prioritize efficient methods to detect the complex urban morphologies of slum settlements. Leveraging transfer-learning with minimal samples and estimating the probability of predictions for slum settlements, we uncover previously obscured patterns in urban structures. By using Monte Carlo Dropout, we not only enhance classification performance in noisy datasets and ambiguous feature spaces but also gauge the uncertainty of our predictions. This offers deeper insights into the model's confidence in distinguishing slums, especially in scenarios where slums share characteristics with formal areas. Despite the inherent complexities, our custom CNN STnet stands out, delivering performance on par with renowned models like ResNet50 and Xception but with notably superior efficiency -faster training and inference, particularly with limited training samples. Combining Monte Carlo Dropout, class-weighted loss function, and class-balanced transfer-learning, we offer an efficient method to tackle the challenging task of classifying intricate urban patterns amidst noisy datasets. Our approach not only enhances AI model training in noisy datasets but also advances our comprehension of slum dynamics, especially as these uncertainties shed light on the intricate intraurban variabilities of slum settlements.

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Section: Transfer-learning From Few Samplesmentioning

confidence: 99%

Quantifying Uncertainty in Slum Detection: Advancing Transfer Learning With Limited Data in Noisy Urban Environments

Stark,

Wurm,

Zhu

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A Survey on Few-Shot Techniques in the Context of Computer Vision Applications Based on Deep Learning

San-Emeterio

2022

Lecture Notes in Computer Science

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Unlocking the capabilities of explainable few-shot learning in remote sensing

Lee,

Dam,

Ferdaus

et al. 2024

Artif Intell Rev

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Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for image-based remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, few-shot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of few-shot learning methods on satellite-based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from Unmanned Aerial Vehicles (UAVs), which are increasingly used in remote sensing studies. In this review, we provide an up-to-date overview of both existing and newly proposed few-shot classification techniques, along with appropriate datasets that are used for both satellite-based and UAV-based data. We demonstrate few-shot learning can effectively handle the diverse perspectives in remote sensing data. As an example application, we evaluate state-of-the-art approaches on a UAV disaster scene dataset, yielding promising results. Furthermore, we highlight the significance of incorporating explainable AI (XAI) techniques into few-shot models. In remote sensing, where decisions based on model predictions can have significant consequences, such as in natural disaster response or environmental monitoring, the transparency provided by XAI is crucial. Techniques like attention maps and prototype analysis can help clarify the decision-making processes of these complex models, enhancing their reliability. We identify key challenges including developing flexible few-shot methods to handle diverse remote sensing data effectively. This review aims to equip researchers with an improved understanding of few-shot learning’s capabilities and limitations in remote sensing, while pointing out open issues to guide progress in efficient, reliable and interpretable data-efficient techniques.

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Few-Shot Learning for Post-Earthquake Urban Damage Detection

Cited by 9 publications

References 32 publications

Quantifying Uncertainty in Slum Detection: Advancing Transfer Learning With Limited Data in Noisy Urban Environments

Quantifying Uncertainty in Slum Detection: Advancing Transfer Learning With Limited Data in Noisy Urban Environments

A Survey on Few-Shot Techniques in the Context of Computer Vision Applications Based on Deep Learning

Unlocking the capabilities of explainable few-shot learning in remote sensing

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