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1. Brief communication: Rapid assessment of damaged residential buildings in the Florida Keys after Hurricane Irma

   https://doi.org/10.5194/nhess-18-2041-2018  

   Xian, Siyuan; Feng, Kairui; Lin, Ning; Marsooli, Reza; Chavas, Daniel; Chen, Jie; Hatzikyriakou, Adam (January 2018, Natural Hazards and Earth System Sciences)

   Abstract. On 10 September 2017, Hurricane Irma made landfall in the Florida Keys and caused significant damage. Informed by hydrodynamic storm surge and wave modeling and post-storm satellite imagery, a rapid damage survey was soon conducted for 1600+ residential buildings in Big Pine Key and Marathon. Damage categorizations and statistical analysis reveal distinct factors governing damage at these two locations. The distance from the coast is significant for the damage in Big Pine Key, as severely damaged buildings were located near narrow waterways connected to the ocean. Building type and size are critical in Marathon, highlighted by the near-complete destruction of trailer communities there. These observations raise issues of affordability and equity that need consideration in damage recovery and rebuilding for resilience. 

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2. Black Marble Nighttime Light Data for Disaster Damage Assessment

   https://doi.org/10.3390/rs15174257  

   Zhang, Danrong; Huang, Huili; Roy, Nimisha; Roozbahani, M Mahdi; Frost, J David (September 2023, Remote Sensing)

   Ambinakudige_Shrinidhi; Dash_Padmanava (Ed.)

   This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation. 

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3. Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

   https://doi.org/10.3390/rs11212492  

   Peng, Bo; Meng, Zonglin; Huang, Qunying; Wang, Caixia (November 2019, Remote Sensing)

   null (Ed.)

   Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes). 

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4. Scalable approach to create annotated disaster image database supporting AI-driven damage assessment

   https://doi.org/10.1007/s11069-024-06641-x  

   Ro, Sun_Ho; Gong, Jie (May 2024, Natural Hazards)

   Abstract As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization. 

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5. Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

   https://doi.org/10.1111/mice.13197  

   Braik, Abdullah M; Koliou, Maria (March 2024, Computer-Aided Civil and Infrastructure Engineering)

   Abstract Efficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports. 

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