Alan Turing Institute – SENSE Hackathon : Team 9 presentation

1. SENSE CDT - ATI Hackathon
Alex Merrington
University of Edinburgh
Jacob Connolly
University of Leeds
Leo Pauly
University of Leeds

2. Intended workflow
Split into 200 x
200 patches
Run classifier
Stitch classified
patches together

3. Data preparation: Sub-sampling
Reading in data
Surface type
shapefiles
Shapefile
dataframe
(geopandas)
Iterate through
shapefiles/tiffs
Sentinel 1
tiffs
Find associated S1
image
Select random x and y
locations within the S1
image
Quota
Max. tries
Coordinates
Are those coordinates
in a polygon?
Yes
no
Add image size, K, to
coordinates (top left
corner)
Metadata file
(.csv)
Append image index,
x,y coords, label to
metadata
Input
S1 image
(rasterio object)
extract the label from
polygon
Image sub-
sample (.png)
K= 100
K= 100
Add K/2 to x and y to
find center
Output
Samples per label

4. Patch samples
LABELS = {"L": 0,"W": 1,"I": 2,}

5. Class distribution: train
Land: 304 (12.1 %)
Water: 1035 (41.19 %)
Ice: 1174 (46.72 %)
Dataset info
Total no: of patches: 2513
Train/Test split: 2261/252 (10%)
Class distribution: test
Land: 25 (9.92 %)
Water: 92 (37.7 %)
Ice: 135 (52.32 %)

6. Model: PolarNet_V1
Batch size Iterations Learning rate Validation
accuracy (%)
32 78 .1 54.76
32 78 .001 52.78
64 39 .1 52.38
64 39 .001 70.24
Architecture & training details:
Results:
Library:
Optimizer: Adam
Loss: Categorical cross entropy loss
Conv1
(filters=32,
kernel=3x3
activation-ReLU)
Input patch
(200x200x3)
Class
{Land,Water,Ice}
fc1
(neurons-10,
activation=Softmax)

7. Confusion matrix

8. Deep networks : PolarNet_V2
Train/Test
set
Batch size Iterations Learning
rate
Validation
accuracy
(%)
PloarNet_V1 2261/252 64 39 .001 70.24
PloarNet_V2 2261/252 64 39 .001 66.27
PloarNet_V1 5762/641 64 100 .001 68.33
PloarNet_V2 5762/641 64 100 .001 68.64
Architecture & training details:
Results:
Conv1
(filters=32,
kernel=3x3
activation-ReLU)
Input patch
(200x200x3)
Class
{Land,Water,Ice}
fc2
(neurons-10,
activation=Softmax)
Conv2
(filters=64,
kernel=3x3
activation-ReLU)
fc1
(neurons-100,
activation=Relu)

9. Confusion matrix: (PloarNet_V2)
train/test: 2261/252 train/test: 5762/641

10. Q!