In disaster-prone regions, particularly in developing countries, the availability of maps and accessibility information is paramount for efficient crisis response. The DeepGlobe Road Extraction dataset addresses this need by presenting the task of automatically extracting road and street networks from satellite imagery.
- Training Data: The dataset for the Road Challenge comprises 6,226 satellite images in RGB format, each with a size of 1024x1024 pixels.
- Pixel Resolution: These images feature a pixel resolution of 50cm, collected using DigitalGlobe's high-resolution satellite technology.
This dataset serves as a critical resource for training and evaluating road segmentation models, enabling the development of solutions that can aid disaster response efforts.
Sample satellite image from the DeepGlobe Road Extraction Challenge dataset.
The U-Net model architecture is designed for semantic segmentation tasks and is employed in this project to extract roads from satellite imagery. Below is a summary of the U-Net architecture:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 128, 128, 3)] 0 []
conv2d (Conv2D) (None, 128, 128, 16) 448 ['input_1[0][0]']
dropout (Dropout) (None, 128, 128, 16) 0 ['conv2d[0][0]']
conv2d_1 (Conv2D) (None, 128, 128, 16) 2320 ['dropout[0][0]']
max_pooling2d (MaxPooling2 (None, 64, 64, 16) 0 ['conv2d_1[0][0]']
D)
conv2d_2 (Conv2D) (None, 64, 64, 32) 4640 ['max_pooling2d[0][0]']
dropout_1 (Dropout) (None, 64, 64, 32) 0 ['conv2d_2[0][0]']
conv2d_3 (Conv2D) (None, 64, 64, 32) 9248 ['dropout_1[0][0]']
max_pooling2d_1 (MaxPoolin (None, 32, 32, 32) 0 ['conv2d_3[0][0]']
g2D)
conv2d_4 (Conv2D) (None, 32, 32, 64) 18496 ['max_pooling2d_1[0][0]']
dropout_2 (Dropout) (None, 32, 32, 64) 0 ['conv2d_4[0][0]']
conv2d_5 (Conv2D) (None, 32, 32, 64) 36928 ['dropout_2[0][0]']
max_pooling2d_2 (MaxPoolin (None, 16, 16, 64) 0 ['conv2d_5[0][0]']
g2D)
conv2d_6 (Conv2D) (None, 16, 16, 128) 73856 ['max_pooling2d_2[0][0]']
dropout_3 (Dropout) (None, 16, 16, 128) 0 ['conv2d_6[0][0]']
conv2d_7 (Conv2D) (None, 16, 16, 128) 147584 ['dropout_3[0][0]']
max_pooling2d_3 (MaxPoolin (None, 8, 8, 128) 0 ['conv2d_7[0][0]']
g2D)
conv2d_8 (Conv2D) (None, 8, 8, 256) 295168 ['max_pooling2d_3[0][0]']
dropout_4 (Dropout) (None, 8, 8, 256) 0 ['conv2d_8[0][0]']
conv2d_9 (Conv2D) (None, 8, 8, 256) 590080 ['dropout_4[0][0]']
conv2d_transpose (Conv2DTr (None, 16, 16, 128) 131200 ['conv2d_9[0][0]']
anspose)
concatenate (Concatenate) (None, 16, 16, 256) 0 ['conv2d_transpose[0][0]',
'conv2d_7[0][0]']
conv2d_10 (Conv2D) (None, 16, 16, 128) 295040 ['concatenate[0][0]']
dropout_5 (Dropout) (None, 16, 16, 128) 0 ['conv2d_10[0][0]']
conv2d_11 (Conv2D) (None, 16, 16, 128) 147584 ['dropout_5[0][0]']
conv2d_transpose_1 (Conv2D (None, 32, 32, 64) 32832 ['conv2d_11[0][0]']
Transpose)
concatenate_1 (Concatenate (None, 32, 32, 128) 0 ['conv2d_transpose_1[0][0]',
) 'conv2d_5[0][0]']
conv2d_12 (Conv2D) (None, 32, 32, 64) 73792 ['concatenate_1[0][0]']
dropout_6 (Dropout) (None, 32, 32, 64) 0 ['conv2d_12[0][0]']
conv2d_13 (Conv2D) (None, 32, 32, 64) 36928 ['dropout_6[0][0]']
conv2d_transpose_2 (Conv2D (None, 64, 64, 32) 8224 ['conv2d_13[0][0]']
Transpose)
concatenate_2 (Concatenate (None, 64, 64, 64) 0 ['conv2d_transpose_2[0][0]',
) 'conv2d_3[0][0]']
conv2d_14 (Conv2D) (None, 64, 64, 32) 18464 ['concatenate_2[0][0]']
dropout_7 (Dropout) (None, 64, 64, 32) 0 ['conv2d_14[0][0]']
conv2d_15 (Conv2D) (None, 64, 64, 32) 9248 ['dropout_7[0][0]']
conv2d_transpose_3 (Conv2D (None, 128, 128, 16) 2064 ['conv2d_15[0][0]']
Transpose)
concatenate_3 (Concatenate (None, 128, 128, 32) 0 ['conv2d_transpose_3[0][0]',
) 'conv2d_1[0][0]']
conv2d_16 (Conv2D) (None, 128, 128, 16) 4624 ['concatenate_3[0][0]']
dropout_8 (Dropout) (None, 128, 128, 16) 0 ['conv2d_16[0][0]']
conv2d_17 (Conv2D) (None, 128, 128, 16) 2320 ['dropout_8[0][0]']
conv2d_18 (Conv2D) (None, 128, 128, 1) 17 ['conv2d_17[0][0]']
==================================================================================================
Total params: 1941105 (7.40 MB)
Trainable params: 1941105 (7.40 MB)
Non-trainable params: 0 (0.00 Byte)
After training the U-Net model, the following metrics were obtained:
- Loss: 0.0905
- Dice Coefficient: 0.4025
- Intersection over Union (IoU): 0.2531
- Recall: 0.2685
- Precision: 0.8949
Here are some sample predictions made by the U-Net model on the satellite imagery:
