DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery (2405.04800v1)

Abstract: Satellite imagery has played an increasingly important role in post-disaster building damage assessment. Unfortunately, current methods still rely on manual visual interpretation, which is often time-consuming and can cause very low accuracy. To address the limitations of manual interpretation, there has been a significant increase in efforts to automate the process. We present a solution that performs the two most important tasks in building damage assessment, segmentation and classification, through deep-learning models. We show our results submitted as part of the xView2 Challenge, a competition to design better models for identifying buildings and their damage level after exposure to multiple kinds of natural disasters. Our best model couples a building identification semantic segmentation convolutional neural network (CNN) to a building damage classification CNN, with a combined F1 score of 0.66, surpassing the xView2 challenge baseline F1 score of 0.28. We find that though our model was able to identify buildings with relatively high accuracy, building damage classification across various disaster types is a difficult task due to the visual similarity between different damage levels and different damage distribution between disaster types, highlighting the fact that it may be important to have a probabilistic prior estimate regarding disaster damage in order to obtain accurate predictions.

• The paper introduces a two-step model that decouples building segmentation and damage classification, achieving a mean IoU of 0.85.
• It demonstrates that integrating disaster-specific features boosts classification accuracy, achieving an F1 score of 0.66 compared to benchmark scores.
• The study underscores the model’s potential for rapid post-disaster assessments by adapting to diverse geographic and disaster conditions.

DeepDamageNet: A Two-step Deep-learning Model for Multi-disaster Building Damage Segmentation and Classification Using Satellite Imagery

Introduction

The paper "DeepDamageNet: A Two-step Deep-learning Model for Multi-disaster Building Damage Segmentation and Classification Using Satellite Imagery" proposes a novel approach for automating building damage assessments post-disaster using satellite imagery. Traditional methods largely depend on manual visual interpretation and field surveys, which are time-consuming and error-prone. The authors address these limitations by developing a two-step deep-learning model to perform semantic segmentation and damage classification of buildings affected by natural disasters.

Dataset Overview

The xBD dataset is utilized, containing over 2799 pairs of pre- and post-disaster satellite images sourced from DigitalGlobe with 0.5m resolution. Each image pair is annotated with ground truth building polygons and damage levels ranging from no damage (0) to destroyed (3). The dataset encompasses various types of disasters such as hurricanes, earthquakes, and wildfires, collected from diverse geographic locations.

Figure 1: Pre-disaster (left) and its corresponding post-disaster satellite image.

Model Architecture

The paper introduces two primary model architectures: a two-step model and an end-to-end model.

1. Two-step Model: This model decouples building segmentation and damage classification into sequential tasks. Initially, a semantic segmentation model identifies building footprints, followed by a CNN-based classification model evaluating damage extent. The segmentation model utilizes a ResNet-50 FPN, while the classification model employs a twin-tower ResNet-50 architecture.

   Figure 2: Architecture of two-step model. The pre-disaster image is fed into a segmentation model to obtain building polygon coordinates. This result in addition to pre- and post- disaster cropped building image is then fed into a damage classification model to obtain the final image.

2. End-to-End Model: This architecture employs a pre-trained U-Net with ResNet-34 encoder to simultaneously segment and classify damage. It inputs the difference between pre- and post-disaster images to directly predict damage levels.

   Figure 3: Architecture of end-to-end model. The pre- and disaster image is subtracted before feeding into a U-Net to perform simultaneous segmentation and classification.

Experiments and Results

The results demonstrate that the two-step model outperforms the end-to-end model in both segmentation and classification tasks. For instance, the semantic segmentation model achieved a mean IoU of 0.85, surpassing the instance segmentation alternative.

Figure 4: Instance segmentation results on validation set.

In damage classification, incorporating disaster-specific features into the model improved accuracy significantly. The best-performing model yielded a combined F1 score of 0.66, which exceeded the xView2 challenge baseline F1 score of 0.28.

Figure 5: Validation results of correct damage classification predictions.

Challenges noted include significant visual similarity across damage types and discrepancies in building densities affecting model accuracy.

Conclusion

The paper provides substantial evidence supporting the efficacy of using deep learning for automated building damage assessment. It suggests future work may involve integrating probabilistic prior estimates for disaster severity to refine damage predictions. The implications for real-world applications underline the need for deploying adaptable, generalized models capable of accommodating diverse disaster types and geographic variations.

In conclusion, "DeepDamageNet" offers a compelling solution to the challenges of rapid and accurate post-disaster assessment, paving the way for improved response strategies and resource management in the aftermath of natural catastrophes.