Multi-Modal Deep Learning Approach for Post-Hurricane Building Damage Assessment

Основные понятия

A novel multi-modal deep learning model, Multi-Modal Swin Transformer (MMST), can accurately classify building damage levels after hurricanes by leveraging both street-view imagery and structured data such as building characteristics and wind speed.

Аннотация

The article proposes a novel multi-modal deep learning model called Multi-Modal Swin Transformer (MMST) for post-hurricane building damage assessment. The key highlights are:

1. MMST utilizes Swin Transformer as the backbone of the image feature extractor, which can efficiently capture global relationships and detailed damage patterns in street-view imagery.

2. MMST incorporates structured data such as building age, value, wind speed, and proximity to hurricane track, in addition to imagery data. The structured data feature extractor uses multi-head self-attention to capture complex relationships between the structured features.

3. The multi-modal fusion module in MMST integrates the image and structured data representations using an adjustable fusion ratio for the final building damage classification.

4. Evaluated on a dataset from Hurricane Ian in Florida, MMST outperforms state-of-the-art benchmark models, achieving a Matthews correlation coefficient of 0.7404, sample-weighted F1 score of 0.9386, and accuracy of 92.67%. This represents improvements of 56.70%, 5.03%, and 7.71% respectively over the VGG-16 model.

5. The ablation analysis shows that building age is the most important structured feature, while the evacuation zone contributes the least. Incorporating the multi-head self-attention module for structured data processing also significantly enhances the model's performance.

6. The authors conclude that the Transformer-based architecture and the integration of multi-modal data are key factors in MMST's superior performance compared to traditional CNN-based models for post-hurricane building damage assessment.

Статистика

Building value is an important predictor for damage level classification.
Building age is the most important structured feature for MMST's performance.
Wind speed is also a critical predictor for damage level classification.

Цитаты

"Building age is the most important structured feature that contributes to MMST's performance."
"Incorporating the multi-head self-attention module for structured data processing significantly enhances the model's performance."
"MMST outperforms state-of-the-art benchmark models, achieving a Matthews correlation coefficient of 0.7404, sample-weighted F1 score of 0.9386, and accuracy of 92.67%."

Ключевые выводы из

Post-hurricane building damage assessment using street-view imagery and structured data

by Zhuoqun Xue,... в arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07399.pdf

Дополнительные вопросы

How can MMST be extended to assess building damage in other types of natural disasters beyond hurricanes

To extend MMST for assessing building damage in other types of natural disasters beyond hurricanes, the model can be adapted to incorporate relevant data sources and features specific to those disasters. For example:

Earthquakes: Include data on seismic activity, building materials, and structural integrity to assess damage caused by earthquakes.
Floods: Integrate information on flood levels, proximity to water bodies, and building elevation to evaluate flood-induced damage.
Wildfires: Utilize data on vegetation density, fire risk zones, and building materials to predict damage from wildfires.
Tornadoes: Incorporate wind speed data, building age, and construction materials to assess damage caused by tornadoes.

By customizing the structured data inputs and training the model on relevant datasets for each disaster type, MMST can be effectively adapted to assess building damage across various natural disasters.

What other structured data sources could be integrated into MMST to further improve its performance and generalizability

To further enhance MMST's performance and generalizability, additional structured data sources can be integrated, such as:

Building Materials: Including information on the type of materials used in construction can help assess vulnerability to different types of damage.
Building Codes Compliance: Data on adherence to building codes and regulations can indicate the structural resilience of buildings.
Historical Damage Records: Incorporating past damage data can provide insights into recurring vulnerabilities and patterns.
Population Density: Considering the density of inhabitants in an area can help prioritize response efforts and assess potential impact on human life.

By incorporating these additional structured data sources, MMST can gain a more comprehensive understanding of building damage factors and improve its predictive capabilities.

How can the insights from MMST's performance be used to inform building design and construction practices to enhance hurricane resilience

The insights from MMST's performance can be valuable in informing building design and construction practices to enhance hurricane resilience in the following ways:

Improved Building Codes: By analyzing the impact of building age on damage levels, regulators can update building codes to enforce stronger standards for older structures.
Material Selection: Understanding the influence of building materials on damage can guide architects and engineers in choosing more resilient materials for construction.
Location-Based Risk Assessment: By considering factors like proximity to the hurricane track, evacuation zones, and wind speed, urban planners can better assess risk and plan for resilient infrastructure.
Emergency Response Planning: Using MMST insights, emergency management agencies can prioritize resources and response efforts based on predicted damage levels, improving overall disaster response effectiveness.

By leveraging MMST's findings, stakeholders in the construction and disaster management sectors can make informed decisions to enhance building resilience and mitigate the impact of hurricanes.