Deep Learning for Satellite Imagery Feature Detection

Vladimir Iglovikov

Sr. Data Scientist at TrueAccord

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

1. Industry => interpretability, scalability, size, throughput
2. Academia => novelty
3. Competitions => accuracy

Problem description

Output

Classes each pixel belongs to

1. Buildings

2. Misc. man-made structures

3. Roads

4. Track

5. Trees

6. Crops

7. Waterways

8. Standing water

9. Vehicle (Large)

10. Vehicle (Small)

• Train set: 25 images
• Test set: 32 images
  • ​​6 Public
  • 26 Private
• ​​Each image 1km x 1km

Bands

M-band

A-band

Problem description

Evaluation metric

Issues with the data

• Small amount of train data (25 km^2)
• Data is very diverse (towns, jungles, farms)
• Mistakes in labeling
• Big class imbalance
• Different distributions in train / public test / private test

Issues with the data

The same car

Cars + trash cans marked as cars

Water classes: sometimes simple solutions work better

Text

Indices

1. NDWI: Normalized difference water index
2. EVI: Enhanced vegetation index
3. SAVI: Soil-Adjusted Vegetation Index
4. CCCI: Canopy Chlorophyl Content Index

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

Ongoing Deep Learning competitions

1. Intel & MobileODT Cervical Cancer Screening (June 21, $100,000)

2. NOAA Fisheries Steller Sea Lion Population Count (June 27, $25,000)

3. ImageNet 2017. (Jun 30)

4. Planet: Understanding the Amazon from Space (July 20, $60,000)