AI FOR HUMANITARIAN ASSISTANCE AND DISASTER RELIEF

INSTANCE SEGMENTATION AND CLASSIFICATION OF BUILDING DAMAGE USING PAIR OF PRE AND POST DISASTER IMAGES

IMPLICIT CLASSIFICATION OF NON LABELLED AREAS (VEGETATION)

OPENSOURCE XVIEW2 SUBMISSION USING MODIFIED UNET (MORE STAGES AND PAIRED IMAGE INPUTS)

TESTED ON UBUNTU 18.04, PYTHON 3.7.5, USING RTX GPU WITH HIGH VRAM

Get XView2 Data

Untar train.tar and tier3.tar and test.tar from https://xview2.org/dataset

Arrange data as follows:

data
├── test
│   └── images
└── train
    ├── images1024
    ├── labels1024
    └── targets1024

INSTRUCTIONS

For fp16 training using Volta or Turing GPU install Nvidia apex python only version from https://github.com/NVIDIA/apex . (Note the automatically applied dynamic loss scaling feature may help with stability of training)

You may need to modify batch sizes in trainlocunet.py and traindamgeunet.py

pip install -r requirements.txt 
python preprocess.py
python trainlocunet.py
python traindamageunet.py
tree workspace # to see your checkpoints and tensorboard logs 
python testdamage.py

Results will be in

results
├── predictions          # pixel values in range (0-1) or (0-4) valid for submission (zip this folder for submission) 
└── vizpredictions       # pixel values in range (0-255) for easy viewing

Score

Just missed out on top 50 leaderboard despite joining the competition very late and entering submissions on last day only

(weighted overall, loc, dmg) .68 / .78 / .63

Feel free to experiment with the code and post issues.