A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image

TL;DR

This paper introduces a deep-learning framework combining data augmentation, EfficientNet extsubscript{Large}, and SVM classification for improved landslide detection from remote sensing images, achieving high accuracy on a public dataset.

Contribution

It presents a novel combination of data augmentation, EfficientNet extsubscript{Large}, and SVM for landslide classification, addressing data imbalance and overfitting issues.

Findings

Achieved an F1-score of 0.8938 on the Zindi challenge test set.

Effective handling of imbalanced data through combined online and offline augmentation.

Demonstrated robustness of the EfficientNet extsubscript{Large} backbone in remote sensing applications.

Abstract

The use of satellite imagery combined with deep learning to support automatic landslide detection is becoming increasingly widespread. However, selecting an appropriate deep learning architecture to optimize performance while avoiding overfitting remains a critical challenge. To address these issues, we propose a deep-learning based framework for landslide detection from remote sensing image in this paper. The proposed framework presents an effective combination of the online an offline data augmentation to tackle the imbalanced data, a backbone EfficientNet\_Large deep learning model for extracting robust embedding features, and a post-processing SVM classifier to balance and enhance the classification performance. The proposed model achieved an F1-score of 0.8938 on the public test set of the Zindi challenge.