Model description
Autoencoder model trained to compress information from sentinel-2 satellite images using Resnet50 V2 as encoder backbone to extract features. The latent space of the model is given by 1024 neurons which can be used to generate embeddings from the sentinel-2 satellite images.
The model was trained using bands RGB (2, 3 and 4) (Red, Green and Blue) of the Sentinel-2 satellites and using 10 municipalities of Colombia with most dengue cases.
The input shape of the model is 224, 224, 3. To extract features you should remove the last layer.
Intended uses & limitations
The model was trained with images of 10 different cities in Colombia with most dengue cases, however it may require fine tuning or retraining to learn from other contexts such as countries and other continents.
Training and evaluation data
The model was trained with satellite images of 10 different cities in Colombia extracted from sentinel-2 using RGB bands using an asymmetric autoencoder. Images with information that could result in noise such as black images were filtered prior to training to avoid noise in the data.
The dataset was split into train and test using 80% for train and 20% to test.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
|---|---|
| name | Adam |
| learning_rate | 0.0010000000474974513 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
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