This deep learning project is my graduated thesis which is scored 9.5/10. It is also the first time i do end-to-end an applied research project by myself.

This project aim to automated extraction of urban objects from data acquired by airborne sensors which very high-resolution data (leads to high intra-class variance while the inter-class variance is low), focus is on detailed 2D semantic segmentation that assigns labels to multiple object categories. At the end, my accuraccy reachs 90% (the top world leader at 92%)

• 2D Semantic Labeling Contest - Potsdam
• Short description at here

1. Visualise and analyse data
2. Preprocessing data
3. Build model
4. Choose hyper-parameters and loss function
5. Experiment and validate
6. Testing phase
7. Report and submit

1. Visualise and analyse data

See the detail. Conclusion:

• Unbalance dataset: building, road and low vegetable class have highest acreage, while car class is negligible.
• Large file size, high resolution.
• The tree class looks like low veg. class on the RGB images. So we use additional DSM channel to distinguish.
• The clutter class 's (disregard class, not the main class) percentage is quite big.

2. Preprocessing data

• Image size 6000x6000 5 channel (IR-R-G-DSM-nDSM), split to patchs 256x256
• Train/Valid/Test: 18/6/14

3 & 4. Build model and Experiment

I tried lots of models, but there are 3 main:

4. Choose hyper-parameters and loss function

5. Experiment and validate

6. Testing phase

7. Report and submit

• Use cyclical learning rate
• Use Resnet101 pretrain
• Split to maxable size patch
• Choose maxable batch size (6->32)
• Adam optimizer
• Small weight decay lr
• Use less pooling
• Use more skip connection
• Normalize image before training

Install package and run notebook on your computer (with anaconda, or cloud such as kaggle, colab)