Semantic Segmentation

This repository contains example code to get 3 working segmentation models up and running quickly using DroneDeploy Segmentation Dataset. The use of both supervised and unsupervised learning allows to determine which model performs better in which scenarios.

Testing:

Follow these steps to run inference using the vote from unsupervised GMM model and saved Supervised Model(Resnet18, VGG16)

• Unzip the segmentation code.
• Python 3 test.py 

Inference will be performed on scenes and a prediction images stored in the directory:

‘predictions_cluster’ : GMM 
‘predictions_resnet18’ : Resnet18 
‘predictions_vgg16’ : VGG16 

Training

Follow these steps to train the supervised models and run inference end-to-end:

git clone https://github.com/tanyajoon/dd-ml-segmentation-benchmark.git
cd dd-ml-segmentation-benchmark
pip3 install -r requirements.txt

# optional: log in to W&B to track your experiements
wandb login

# train and save a Keras model(you can change the model to be used in main_keras)
python3 main_keras.py

This will download the sample dataset and begin training a model. You can monitor training performance on Weights & Biases. Once training is complete, inference will be performed on all test scenes and a number of prediction images with names like 123123_ABCABC-prediction.png will be created in the wandb directory. After the images are created they will be scored, and those scores stored in the predictions directory.

Dataset Details

The dataset comprises a number of aerial scenes captured from drones. Each scene has a ground resolution of 10 cm per pixel. For each scene there is a corresponding "image", "elevation" and "label". These are located in the images, elevation and labels directories.

The images are RGB TIFFs, the elevations are single channel floating point TIFFs (where each pixel value represents elevation in meters), and finally the labels are PNGs with 7 colors representing the 7 classes (documented below).

In addition please see index.csv - inside the downloaded dataset folder - for a description of the quality of each labelled image and the distribution of the labels.

To use a dataset for training, it must first be converted to chips (see images2chips.py). This will create two directories, images-chips and label-chips, which will contain a number of 300x300 (by default) RGB images. The label-chips are also RGB but will be very low pixel intensities [0 .. 7] so will appear black as first glance. You can use the color2class and category2mask function to switch between the two label representations.

Here is an example of one of the labelled scenes:

Each color represents a different class.

Color (Blue, Green, Red) to Class Name:

(075, 025, 230) : BUILDING
(180, 030, 145) : CLUTTER
(075, 180, 060) : VEGETATION
(048, 130, 245) : WATER
(255, 255, 255) : GROUND
(200, 130, 000) : CAR
(255, 000, 255) : IGNORE

• IGNORE - These magenta pixels mask areas of missing labels or image boundaries. They can be ignored.

Possible Improvements

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The sample implementation is very basic and there is immediate opportunity to experiment with:

• Data augmentation (datasets_keras.py)
• Hyperparameters and Model architecture(train_keras.py)
• Post-processing (inference_keras.py)
• Chip size (images2chips.py)
• Elevation tiles are not currently used at all (images2chips.py)