Automated Flood Impact Detection from Remote Sensing Data using Transfer Learning: A New Zealand Case Study

Abstract

Proper flood damage assessment is required for rapid emergency response and recovery planning. Highresolution drone imagery provides greater visual detail compared to satellite imagery and can support more accurate damage assessment after flood events. This work-in-progress study investigates the use of transfer learning with a pre-trained ResNet50 convolutional neural network to classify post-flood drone images. A manually annotated dataset of drone images representing damaged and non-damaged areas was used to finetune the pre-trained model and evaluate its performance using several metrics. The training process employed hyperparameter tuning to select the best model which achieved an accuracy of approximately 87%. These preliminary results demonstrate the potential of transfer learning for flood damage classification using a limited drone image dataset. Future work will focus on comparing the performance of additional deep learning models, incorporating satellite imagery and extending the approach from image-level classification to object-level damage detection.