The document discusses the use of the Python scientific stack, particularly for analyzing satellite images from Landsat 8. It covers Landsat 8's specifications, data acquisition processes, and the available imaging capabilities, highlighting both advantages and disadvantages of the Python libraries used. Additionally, it mentions that Landsat 8 data is freely available through the US Geological Survey after registration.

1. Analyzing Satellite
Images With Python
Scientific Stack
Milos Miljkovic

2. About Me
• Name is hard to spell and pronounce
• Miloš Miljković
• @Hyperion_HQ
• Boston, MA

3. Python Scientific Stack
• python, numpy, scipy, scikit-image, scikit-learn
• Pros:
• Free
• Not bindings to C-libraries
• Easy to read algorithms
• Cons:
• Single-threaded
• Memory hungry
• uint16 not fully supported

4. Landsat 8
• Launched in 2013
• Life span of 5 years
• Heliosynchronous orbit
• Altitude 710 km (440 mi)
• Satellite orbits Earth every 99 minutes

5. Landsat 8 Data Acquisition
• 400 GB of data, 400 scenes every day
• Each scene is 185 x 170 km (115 x 105 mi)
• Data recorded in midmorning hours
• Images captured between 82° 40’ N/S latitudes
• Same scene captured every 16 days

6. Landsat 8 Imagers
• Operational Land Imager - OLI:
• 9 bands in violet to short infrared range
• Resolution 15 - 30 meters
• Thermal Infrared Imaging Sensor - TIRS:
• Two bands in long infrared range
• Resolution 100 meters, interpolated to 30 meters

7. Landsat 8 Data Store
• Data curated by US Geological Survey
• Freely available for download upon registration
• Level 1 data is terrain corrected
• Data is available as 16-bit grayscale geotifs

8. IPython Notebook
time!