Vladimir & Sergey. 3rd out of 419 teams.

$20,000 prize

• Sr. Data Scientist at TrueAccord
• PhD in Physics at UC Davis
• San Francisco, USA
• Kaggle Master

• Deep Learning Engineer
• BS in Computer Science
• Angarsk. Siberia. Russia

Problem description

Evaluation metric

Water classes: sometimes simple solutions work better

Neural network pipeline

• Buildings

• Misc. man-made structures

• Roads

• Track

• Trees

• Crops

Network ​architecture

Local boundary effects

Local boundary effects

Problem

Prediction quality decreases at the edges

Solution

Added Cropping2D layer

Global boundary effects

Problem

We need integer number of tiles    => 

Problem

Zero Padding creates artifacts    => 

Solution

Zero Padding

Solution

Reflection Padding

Test time augmentation

Results

1. Water classes => unsupervised
2. Car classes => did not predict
3. Other classes => U-net per class
4. Input 4 indices + M-band + P-band + RGB
5. Test time augmentation

Summary

Hardware

Vladimir

Core i7

RAM 32 Gb

Titan X (Pascal)

Sergey

2x Xeon E5-2670

RAM 128 GB

GTX 1080