Improving access to satellite imagery with Cloud computing
- 1. University of Minnesota-Twin Cities Rahul Bhojwani(bhojw005@umn.edu) Kate Kuehl(kuehl088@umn.edu) Improving Access to Satellite Imagery with Cloud Computing
- 2. Challenges in Remote Sensing: ● Data size - Expensive to download and process satellite data ● Data availability ● Timeliness of available data ● Data management and handling ● Computation ● Machine Learning friendly datasets
- 3. Challenges in Remote Sensing: ● Data size - Expensive to download and process satellite data 1 petabyte = 1,000,000,000,000,000 bytes OR 1,000 terabytes
- 4. What is “Earth on AWS”? https://aws.amazon.com/earth/ ● Run on Amazon Web Services’ servers ● Accessible through command line when logged into AWS account ● Used by MatLab, Mapbox, Esri, PlanetLabs, DigitalGlobe, NVIDIA, and more
- 5. What is “Google Earth Engine”? https://earthengine.google.com/ ● We used this in class ● Used by WRI, FAO, U of M, and other non-profits/universities ● Free for research, education, and nonprofit use Python and JavaScript API’s available here: https://developers.google.com/earth- engine/#api
- 6. What is “NEX”? https://nex.nasa.gov/nex/ NASA Earth Exchange ● Accessible only by NASA supported researchers ● Runs on a private cloud at NASA Ames Research Center
- 7. Landsat MODISNAIP OpenStreetMap Sentinel-1 Sentinel-2 Elevation GDELT NOAA SpaceNet IARPA MOGREPS LOCA GlobCover SRTMPRISM BCCA NARR FLUXNET FIA AVHRR GIMM TRIMM WorldPop Oxford MAP NASS WWF PSDI GSMaP CHIRPS NCEP/NCAR WorldClim WHRC Geoscience Australia
- 8. Datasets available at AWS Earth ● SpaceNet Machine Learning Imagery ● National Agriculture Imagery Program ● GDELT - A Global Database of Society ● NASA Earth Exchange (NEX) ● DigitalGlobe Open Data Program ● UK Met Office Weather Forecasts ● Functional Map of the World ● Landsat ● NEXRAD ● Terrain Tiles ● GOES ● Sentinel-2 ● OpenStreetMap ● MODIS
- 9. SpaceNet Machine Learning Imagery ● Commercial satellite imagery and labeled training data ● Intended for advancing innovation in Computer Vision ● To extract geometric features from remote sensing data such as: ○ Roads ○ Building footprints ○ Points of interest
- 10. Current Available Areas: ● Rio De Janeiro ● Paris ● Las Vegas ● Shanghai ● Khartoum
- 11. Object Detection
- 12. Semantic segmentation Source Image Inference visualization
- 13. Road detection
- 14. Quiz What challenges of remote sensing that’s addressed by cloud geospatial data? A. Petabyte size of datasets B. Timeliness of available data C. Data management and handling D. Computation E. Machine Learning friendly datasets
- 15. Additional Sources Information and Images ● Earth on AWS https://aws.amazon.com/earth/ ● NDIVIA https://devblogs.nvidia.com/parallelforall/exploring-spacenet-dataset-using-digits/ ● Google Earth Engine https://earthengine.google.com/ ● NASA https://www.nasa.gov ● DigitalGlobe http://ow.ly/rIhV307X3u6 Images ● Wikipedia Commons https://commons.wikimedia.org/
Editor's Notes
- #4: “Big Geospatial Data – an OGC White Paper” Open Geospatial Consortium September 25, 2017
- #5: Ask the class if anyone has heard of it It has existed in some form or another for 2 years
- #6: Ask the class if anyone has heard of it
- #7: Ask the class if anyone has heard of it
- #12: https://devblogs.nvidia.com/parallelforall/exploring-spacenet-dataset-using-digits/
- #14: https://medium.com/the-downlinq/spacenet-road-detection-and-routing-challenge-part-i-d4f59d55bfce