Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data
Abstract
:1. Introduction
1.1. Introduction on Climate Change
1.2. Current Methods on Impact of Climate Change
1.3. Related AI Research Studies
1.4. AI in Aerial Imagery
1.5. AI Research Utilizing Our Data Source
1.6. Purpose of Our Research Study
2. Materials and Methods
2.1. Compiling the Climate Change Dataset
- The Louisiana Flood 2016 [44] dataset:
- ○
- contained aerial images from the historic flooding that occurred in Southern Louisiana in 2016. For each image taken during the flood, there was a corresponding image before/after the flood.
- ○
- image size: 512 × 360 pixels.
- The FDL_UAV_flood areas [45] dataset:
- ○
- contained aerial images of Houston, TX from Hurricane Harvey. The dataset contains both flooded and unflooded images.
- ○
- the image dimensions were approximately 3 K × 4 K pixels.
- The Cyclone Wildfire Flood Earthquake Database [46]:
- ○
- contained videos and images from various natural disasters. We selected images from the Flood folder. These images were obtained from a Google search on each natural disaster included in the dataset.
- ○
- the images were of variable sizes.
- The Satellite Image Classification [47] dataset:
- ○
- was created from sensors and Google Map snapshots.
- ○
- images size: 256 × 256 pixels.
- Disaster Dataset [48]:
- ○
- contains images from numerous natural disasters.
- ○
- the images were resized to 224 × 224 pixels.
- Aerial Landscape Images [49]:
- Aerial Images of Cities [50]:
- Forest Aerial Images for Segmentation [51]:
- ○
- satellite images of forest land cover. Dataset was obtained from Land Cover Classification Track in DeepGlobe Challenge [51].
- ○
- images resized to 256 × 256 pixels.
2.2. Preprocessing and Model Initiation
2.3. VGG16 Network Model
2.4. DenseNet201 Network Model
2.4.1. Data Augmentation Layer
2.4.2. Rescaling Layer
2.4.3. Global Average Pooling Layer
2.4.4. Dropout Layer
2.4.5. Fully Connected Layer and Classifier
2.5. ResNet50 Network Model
2.6. Transfer Learning Framework
2.7. Convolutional Neural Network (CNN) Model
2.7.1. CNN Layers
- 1 rescaling layer;
- 1 data augmentation layer;
- 3 convolutional layers;
- 3 pooling layers;
- 1 drop-out layer;
- 3 fully connected (FC) layers.
2.7.2. Pooling Layers
2.8. Experimental Setup
- Collaborate online with code/feedback;
- Accelerate our ML workload with Google GPUs/TPUs;
- Utilize Google’s cloud computing resources.
2.9. Evaluation Metrics
2.10. Cross-Validation Methods
3. Results
3.1. Individual Model Performance
3.1.1. VGG16 Performance
3.1.2. DenseNet Performance
3.1.3. CNN Performance
3.1.4. ResNet Performance
3.2. ML Model Comparison
3.3. Optimization of DenseNet
3.4. ResNet vs. DenseNet Optimized
3.5. Cross-Validation
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Name of Dataset | Total Image Count | Flooded | Desert | Neither |
---|---|---|---|---|
Louisiana Flood 2016 [44] | 263 | 102 | 0 | 161 |
FDL_UAV_flood areas [45] | 297 | 130 | 0 | 167 |
Cyclone, Wildfire, Flood, Earthquake Database [46] | 613 | 613 | 0 | 0 |
Satellite Image Classification Disaster Dataset [47] | 1131 | 0 | 1131 | 0 |
Disasters Dataset [48] | 1630 | 1493 | 0 | 137 |
Aerial Landscape Images [49] | 800 | 0 | 800 | 0 |
Aerial Images of Cities [50] | 600 | 0 | 0 | 600 |
Forest Aerial Images for Segmentation [51] | 1000 | 0 | 0 | 1000 |
Totals | 6334 | 2338 | 1931 | 2065 |
Category | Precision | Recall | F1-Score |
---|---|---|---|
Desert | 0.99 | 0.99 | 0.99 |
Flooded | 0.95 | 0.96 | 0.95 |
Neither | 0.95 | 0.94 | 0.94 |
Category | Precision | Recall | F1-Score |
---|---|---|---|
Desert | 0.99 | 1.0 | 1.0 |
Flooded | 1.0 | 0.99 | 0.99 |
Neither | 0.99 | 0.99 | 0.99 |
Category | Precision | Recall | F1-Score |
---|---|---|---|
Desert | 0.99 | 0.99 | 0.99 |
Flooded | 0.88 | 0.96 | 0.92 |
Neither | 0.96 | 0.88 | 0.92 |
Category | Precision | Recall | F1-Score |
---|---|---|---|
Desert | 1.00 | 1.00 | 1.00 |
Flooded | 0.98 | 0.99 | 0.98 |
Neither | 0.98 | 0.98 | 0.98 |
ML Model | Validation Accuracy |
---|---|
CNN | 0.9368 |
VGG16 [54] | 0.9581 |
DenseNet201 [57] Optimized | 0.9889 |
ResNet50 [63] | 0.9921 |
ML Model | Validation Accuracy | Validation Loss |
---|---|---|
DenseNet201 [57] | 0.9755 | 1.5234 |
DenseNet201 [57] Optimized | 0.9913 | 0.0196 |
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VanExel, K.; Sherchan, S.; Liu, S. Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data. J. Imaging 2025, 11, 32. https://doi.org/10.3390/jimaging11020032
VanExel K, Sherchan S, Liu S. Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data. Journal of Imaging. 2025; 11(2):32. https://doi.org/10.3390/jimaging11020032
Chicago/Turabian StyleVanExel, Kim, Samendra Sherchan, and Siyan Liu. 2025. "Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data" Journal of Imaging 11, no. 2: 32. https://doi.org/10.3390/jimaging11020032
APA StyleVanExel, K., Sherchan, S., & Liu, S. (2025). Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data. Journal of Imaging, 11(2), 32. https://doi.org/10.3390/jimaging11020032