Talk delivered at Free and Open Source Software for Geo North America 2019 (FOSS4GNA) Large scale solar arrays or farms have been installed globally faster than can be reliably tracked by interested stakeholders. We have built a deep learning model with Sentinel 2 satellite imagery that allows us to create accurate, timely global maps of solar farms.

1. Detecting Solar Farms
with Deep Learning
Jason Brown, Data Scientist
FOSS4GNA 2019, April 16
astraea.earth/careers

2. Project Motivation
• Fast growth in renewable energy industry
including photovoltaic (PV)
• More investment needed for 2°C Paris
target: $130B by 2030
• Solar farm location, capacity, and date
are not reliably recorded
• EO data archives contain verifiable,
spatially-correct, timestamped images of
PV
• Can we extract desired info from imagery
with deep learning?
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Source: BloombergNEF

3. Take Home Message
3
August 2018
Yes we can! Our deep learning
solution can extract verifiable,
spatially-correct, timestamped
info about large PV arrays
from archives of Earth-
observation data.

4. Remote Sensing Imagery
Sentinel 2
– Free, publicly available
• Archive begins 2015
– Global coverage
– 5 day revisit
– Multispectral imagery
• 13 channels in visible, near
infrared (IR) & short wave IR
– Spatial resolution: 10m & 20m
– Top of atmosphere (L1C)
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Sentinel-2 (10m)

5. Computer Vision Tasks
Image credit ataspinar.com
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6. Annotating Imagery
2600 polygons
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7. Creating Training Examples
• Lots of effort to create
images and masks from
annotated imagery
• Many small decisions can
impact model
performance
• 5,000 positive examples,
15,000 or more negative
Positive: Negative:
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8. Deep Learning Ensemble
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• Classification
– Inception v3 architecture
– Tune for high recall, with
minimum precision
• Segmentation
– U-net architecture
– Tune for F1 score (balance of
precision and recall)
positive predictions
𝐹1 =
2 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
2 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒

9. Model Performance
Array ≤ 0.1 km2 Array > 0.1 m2
Recall 0.799 0.892
F1 score 0.822 0.908
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0.1 km2 equivalent to 316 m square or 100 pixels
Model Performance
Array ≤ 0.1 km2 Array > 0.1 m2
Recall 0.799 0.892
F1 score 0.822 0.908
9
0.1 km2 equivalent to 316 m square or 100 pixels

10. Model Inference
• We conducted model
inference on conterminous
USA & China
• Approx 4GB per 120 km2
• We processed approx 13TB
– Access and prepare imagery
– Infer on model pipeline
– Process and write outputs
• Fast enough to do as scenes
are published
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11. Demo
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12. Roll Credits
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Collaborators
Dr. Kimberly Scott, VP of Data Science
Courtney Whalen, Data Scientist
Eric Culbertson, Data Scientist
Earth on AWS
QGIS
SentinelHub
Folium
Dash
Keras
Scikit-learn
GeoPandas
rasterio
Open Source & Open Data Used
astraea.earth/careers