Satellite Images of Hurricane Damage Dataset
A realistic and reliable training environment for the development of automated disaster response and recovery systems by providing satellite images of hurricane damage in Texas after Hurricane Harvey in 2017.
- Labeling Type: damage
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 5,000
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About Dataset
1) Data Introduction
• The Satellite Images of Hurricane Damage Dataset is The Satellite Images of Hurricane Damage Dataset is a binary image classification computer vision dataset based on satellite images taken in Texas, USA, after Hurricane Harvey in 2017. Each image is labeled as either ‘damage’ (indicating structural damage) or ‘no_damage’ (indicating no damage), allowing for automatic identification of building damage in disaster scenarios.2) Data Utilization
(1) Characteristics of the Satellite Images of Hurricane Damage Dataset: • The dataset is composed of real satellite images taken immediately after a natural disaster, providing a realistic and reliable training environment for the development of automated disaster response and recovery systems. (2) Applications of the Satellite Images of Hurricane Damage Dataset: • Development of disaster damage recognition models: This dataset can be used to train deep learning-based AI models that automatically classify whether buildings have been damaged based on satellite imagery. These models can contribute to decision-making in rescue prioritization and damage extent analysis. • Geospatial risk prediction systems: By integrating with GIS systems, the dataset can help visualize damage-prone areas on maps, supporting real-time decisions and resource allocation optimization during future disasters.Meta Data
Normal
Outstanding
Data Samples 5
Utility
| Downstream Classification (▲) | KID (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
KID (Kernel Inception Distance) is a metric used to evaluate the similarity between generated images and real images. It compares the differences between the two sample distributions using Kernel Mean Embedding, without assuming a normal distribution. Interpretation: A lower KID score suggests that generated images are more similar to real images, with a score close to 0 being ideal. Specifically, a score below 0.01 indicates very high similarity.
Privacy
| LPIPS (▲) | SSIM (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The higher the value, the better (▲)
Perceptual Similarity
Learned Perceptual Image Patch Similarity (LPIPS) is a metric used to measure the visual similarity between two images by utilizing neural networks to extract key features and calculate the distance between them. High LPIPS value: Indicates high similarity between images, raising the risk of information leakage. Low LPIPS value: Suggests that synthetic images are perceptually different from real images, indicating a lower risk of sensitive information leakage.
The closer to zero or the lower the value, the better (▼)
Structural Similarity
The Structural Similarity Index Measure (SSIM) is a metric used to assess the similarity between two images. It is primarily used to compare the quality of a restored or compressed image with the original image. SSIM measures visual similarity by considering brightness, contrast, and structure.
High SSIM value (0.9 or above): Indicates that the synthetic image is very similar to the real image, which may increase the risk of information leakage.
Low SSIM value (0.6 or below): Indicates low similarity and reduced risk of leakage.
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