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Semelhante a Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Satellite Data and AI
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Roelof Pieters (Overstory) – Tackling Forest Fires and Deforestation with Satellite Data and AI
- 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
- 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