1. Roelof Pieters
CTO & Co-founder
roelof@overstory.ai
@graphific
Tackling Forest Fires and Deforestation with Satellite
Data and AI
May 14, 2021

2. • Computer Science/Deep Learning
• Anthropology and Development
Sociology
• Previously founded Creative.ai,
Stockholm.ai, GitXiv and other open
source initiatives/networks/startups
Introduction
Roelof Pieters
CTO & Co-founder, Overstory
This session
• Advancements in satellite data & AI
• Example: deforestation monitoring
• Example: preventing wildfires and power
outages

3. Indra den Bakker
CEO & Co-founder
Anniek Schouten
COO & Co-founder
David de Meij
Data Scientist
Roelof Pieters
CTO & Co-founder
Rochelle Silva
Radar &
GIS Researcher
Lorenzo Riches
Data Scientist
We’re Overstory
Arsha Yuditha
Amiranti
GIS Specialist
Elvira Garkava
Business Developer
Fiona Spruill
Chief Product Officer
Andrea Giardini
DevOps Engineer
Killian Tobin
Head of Business
Development

4. Customers
Investors
Partners

5. The problem
Insect outbreaks
Forest fires
Deforestation
Forest fire (Source: Nasa)
Bark beetle infestation (Source: Sodra)
Deforestation (Source: ESA, Google)

6. Remote Sensing: an overview

7. Satellite market
Today 2029
# of satellites
2010
1000
800
600
400
200
1,470 satellites launched
between 2010 and 2019
8,500 satellites expected to launch
between 2019 and 2028*
Hyperspectral
VHR SAR Video Real-time
Source:Euroconsult: https://spacenews.com/analysis-are-smallsats-entering-the-maturity-stage/

8. Input data High-resolution satellite imagery
up to 30 cm resolution

9. Input data Invisible light like near-IR
Multispectral data

10. Input data Weather independent radar images
SAR data

11. July 2, 2017 July 4, 2017 July 5, 2017
Input data Frequent revisits
up to daily on a global scale

12. Input data Frequent revisits
up to daily on a global scale
2.5m
source:
https://platform.digitalglobe.com/earth-imaging-basics-spatial-resolution/
30cm

13. Tri-stereo and video monitoring
for 3D-mapping
SAR data to look through
clouds
Multi- and hyperspectral
data
Up to 30 cm resolution
Up to 20 VHR daily revisits
& geostationary satellites
Very high-resolution satellite data

14. ● Mega Large Imagery
● Constant change
● Noisy satellite data
Data Science Challenges
● Noisy or lack of labels
● Generalize from training
● “ground truth”
● Classical machine learning still the norm

15. Unsupervised Machine Learning
???
A? B? C?
Allows Overstory to get insights
anywhere in the world with 1-2 factors
less customer/labeled data (1000 instead
of 10K-100K data points)
"Everything is related to everything else,
but near things are more related than
distant things"

16. Stereo Imaging
For creating height maps and 3D maps we use Stereo Imaging
techniques to create Digital Surface Maps (DSM) and Digital Terrain
Maps (DTM), as well as 3D point clouds as in this example

17. Stereo Imaging Learning Feature Matching with Graph Neural Networks
Magic Leap/ETH Zurich (2020)

18. A process by which freely accessible low resolution imagery can be upscaled to commercial-grade high
resolution, allowing for more accurate insights, easier labelling by our annotators, and increased
accuracy for our machine learning models, at a cheaper cost
(Upscaling Landsat-8 to
Digital Globe Worldview-3-like level)
LS8 TIRS
LS8
CIRRUS
LS8
Panchromatic
LS8 SWIR
LS8 NIR LS8 RGB
DG-WV3 RGB
DG-WV3
Panchromatic
DG-WV3 SWIR
31cm
1.24m
3.7m
15m
100m
30m
Generative Upscaling
Li et al (2019) Feedback Network for Image
Super-Resolution"
See also Super-Resolution Generative
Adversarial Network(s) (many papers)

19. Active Learning
(re)train
candidate selection
oracle / human annotator
● eg BAyesian Active Learning library
(BaaL) by ElementAI:
https://github.com/ElementAI/baal/
○ MCDropout (Gal et al. 2015)
○ BALD (Houlsby et al. 2011)
(ElementAI)

20. Living data repository
training data for scalable and
accurate deep learning
algorithms

21. case #1: Machine Learning for
deforestation monitoring

22. ● high resolution forest and landcover map
● up to date with 2014 to now
● high forest and crop type
accuracy
Deforestation monitoring
what how
● deep learning for segmentation
● multiple satellite sources: sensor
fusion / multimodality
● noisy data: generative gap filling
● dynamic data regime: open data, active
labelling (ground and satellite), noisy
labels

23. Climate &
weather
Height data
Advancements in
deep learning
SAR (radar)
data
Multi resolution
data
Sensor fusion
Convolutional Neural
Networks
Unsupervised
learning and active
(bayesian) learning
Multispectral
data
Output
Segmentation
Layers of satellite
imagery
Land Cover Segmentation on
pixel level

24. Active Learning
Encoder Neural Planet Embedding
1.
2.
3.
4a.
Data
repositor
y
5.
4b.
(Overstory training data
pipeline)
Annotator
Field worker
Public Data

27. High-Resolution Global Maps of 21st-
Century Forest Cover Change
(Hansen et al., Science 2013).

28. Overstory 10m and less
Hansen et al. 2013/2019 1.7 (2000)
GlobCover 2010 2.3 (2009)
CCI Landcover Africa 20m (2016)

29. case # 2: Scaling ML for very high
resolution earth observation & risk
monitoring

30. Vegetation &
powerlines
of global annual
CO2 emissions
~1%
direct
economic losses
$21B
people without
power
40M

33. ● very high resolution tree species map:
37 different tree species, shrub species and
grass classes
● over 16,000 km of power lines
with a corridor width of 150
meter
● total area of over 505,000 km2
● hard constraints on minimum
level of accuracy
fire risk monitoring
what how
● deep learning, naturally :)
● open source labels, customer labels,
external party ground validation
● very high resolution satellite imagery
(50cm)
● massively parallel distributed
processing through dask, kubernetes,
and distributed data parallel training

34. Jupyterhub
Jupyterhub allows users to create
dedicated computational environments
Our Infrastructure

35. Getting a new notebook
Our Infrastructure - Jupyterhub
👥
I need a new notebook! Processing...
We need a new machine
for this...
New node
Notebook available
1
2
3
4
5
6

36. Dask
Dask provides advanced parallelism for
analytics, enabling performance at scale
for the tools you love
Our Infrastructure

37. I need some heavy compute
Our Infrastructure - Dask
👥
I need a new
Dask cluster! Dask-gateway
Dask-scheduler
Dask-worker
Dask-worker
Cluster
available
1
2
3
4
5
6

38. Dask cluster - Structure
Our Infrastructure - Dask
Dask-gateway
Dask-scheduler
Dask-scheduler
Dask-worker Dask-worker
Dask-worker Dask-worker
Dask-worker Dask-worker
Dask-worker Dask-worker
Dask-worker Dask-worker
Dask-worker Dask-worker

39. Papermill
Papermill provides queues and ETL
capabilities for running and logging jupyter
notebook workloads
Our Infrastructure

40. Our Infrastructure - Papermill
Image by Matthew Seal,
Netflix

41. Our Infrastructure - Papermill
Image by Matthew
Seal, Netflix

42. All together now
Our Infrastructure
Abernathey et al. (2021):
Pangeo architecture
diagram.

43. Jupyterhub

44. Dask Dashboard

45. Monitoring Kubernetes/Dask with Grafana

46. Real-time
Vegetation
Intelligence
Platform
Tree height
Canopy segmentation
Fire risks
Tree species
Verification of trimming
Storm damage
Risk identification
Predictive prioritization

47. Overstory is on a mission to monitor all
natural resources on Earth in real-time.

48. We’re in this together!
Overstory is on a mission to monitor all
natural resources on Earth in real-time.
Come join us!
https://www.overstory.com/careers

49. Learning Resources / Research
● List of videos about Geospatial data science from FOSS4G (Free
and Open Source Software for Geospatial / OSGeo)
https://www.youtube.com/channel/UC_2Lyc9VUX-jC-E1prJitHw/vi
deos
● ICLR 2020 proceedings now available: https://iclr.cc/virtual_2020/
& video recordings of climate change AI workshop:
https://www.youtube.com/channel/UCyjDr_aoMlzhSvCTdT7eZ9g/
videos
● CVPR pre-papers for EarthVision: Large Scale Computer Vision
for Remote Sensing Imagery Workshop
http://openaccess.thecvf.com/CVPR2020_workshops/CVPR2020
_w11.py
Awesome lists of resources:
● https://github.com/sshuair/awesome-gis
● https://github.com/robmarkcole/satellite-image-deep-learning
● https://github.com/chrieke/awesome-satellite-imagery-datasets
● https://github.com/acgeospatial/awesome-earthobservation-code
● https://github.com/wenhwu/awesome-remote-sensing-change-
detection
Amazing projects
● https://www.globalforestwatch.org/
● https://trase.earth/
● https://www.half-earthproject.org/
● https://www.climatewatchdata.org/
Geospatial Toolkit/UI:
● QGIS: https://www.qgis.org
Satellite Data
● sentinel-2 10m resolution satellite imagery:
https://scihub.copernicus.eu/dhus/#/home
● landsat 30m resolution satellite imagery:
https://landsat.gsfc.nasa.gov/
ML for geospatial/satellite data libraries:
● https://github.com/sentinel-hub/eo-learn
● https://rastervision.io/
● https://github.com/fastai/fastai2/
Resources

50. Rolnick, et al. Tackling Climate Change with Machine Learning, arXiv:1906.05433 & https://www.climatechange.ai/

52. www.overstory.ai
linkedin.com/company/overstoryai
twitter.com/overstoryai
medium.com/overstoryai
Roelof Pieters
CTO & Co-founder
roelof@overstory.ai
@graphific