Remote Sensing Building Damage Assessment Based on Machine Learning
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30. Sept. 2024
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Online veröffentlicht: 30. Sept. 2024
Seitenbereich: 1 - 12
DOI: https://doi.org/10.2478/ijanmc-2024-0021
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© 2024 Jiawei Tang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Confusion matrix formal table
| Prediction category | True category | Positive sample | Negative sample |
|---|---|---|---|
| Positive sample | TP | FP | |
| Negative sample | FN | TN | |
Based on the building damage level table defined in this article
| Class | Description |
|---|---|
| 0 | Undamaged |
| 1 | Minor damage |
| 2 | Major damage |
| 3 | Destroyed |
Training results on validation dataset
| Name | Explanation | Color |
|---|---|---|
| F1 | The overall F1 value of the building damage assessment on the xBD validation set | Yellow |
| F1_Loc | F1 values for segmentation of building localization on the xBD validation set | Purple |
| F1_Dam | F1 value for building damage classification on the xBD validation set | Green |
| F1_Undam | F1 value for classification of undamaged buildings on the xBD validation set | Grey |
| F1_Min | F1 value for classification of minor damage buildings on the xBD validation set | Blue |
| F1_Maj | F1 value for classification of major damage buildings on the xBD validation set | Orange |
| F1_Des | F1 value for classification of destroyed buildings on the xBD validation set | Red |
European disaster committee table for building damage assessment
| Masonry Construction | Fortified Buildings | Damage Level |
| Undamaged | ||
| Minor Damaged | ||
| Medium Damaged | ||
| Major Damage | ||
| Destroyed |
Training environment configuration table
| Configuration information | Detail |
|---|---|
| Hardware Configuration | Nivdia RTX 3080 12G |
| Language | Python 3.8 |
| Main Frame | Pytorch 2.1.0 Cuda11.8 |
| Image information | 1024×1024 20248 photos |
| Optimization Function | Adam |
| Loss Function | cross entropy loss |
| Epoch | 30 |
| Training time | 12h |