SpaceSense Library

SpaceSense is a library which facilitates downloading, processing, and fusing of satellite and other sources of geospatial data. In this documentation, you can find guides on installation and usage, as well as examples.

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SpaceSense

• Overview
  • What is the SpaceSense python library?
  • Why we built the SpaceSense library?
  • How does the SpaceSense library work?
• Getting Started
  • Installation
    • Pre-requisites
    • Installing with Venv
    • Fix gdal dependencies
    • Fix netCDF dependencies
    • Installing with Anaconda
  • Authentication
  • Initial Example
    • 0 - Install Spacesense Client Library and dependencies
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Configure the API Key by setting the SS_API_KEY environment variable
    • 3 - Define the area of interest (AOI), time of interest (TOI), output options
    • 4 - Search S2
    • Select just one S2 result
    • 5 - Select only certain bands of S2
    • 6 - Obtain S2 image (Fuse)
    • 7 - Visualize
  • Library Structure
• Examples
• SpaceSense Basics
  • Search S1 and S2
    • 0 - Install Spacesense Client Library and dependencies
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Configure the API Key by setting the SS_API_KEY environment variable
    • 3 - Define AOI and output options
    • 4 - Search S1
    • 5 - Search S2
    • 6 - S2 search results can be viewed using a calendar
  • Search and filter S1 and S2 data
    • 0 - Install Spacesense Client Library and dependencies
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Configure the API Key by setting the SS_API_KEY environment variable
    • 3 - Define AOI and output options
    • 4 - Search S1
    • 5 - Search S2
    • 6 - S2 search results can be viewed using a calendar
    • 7 - Filter and select only the S1 and S2 images you require
  • Weather Data Access in Search
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Define AOI and TOI
    • 3 - Search Weather
    • 4 - Plot time series of LAI
    • 5 - Search S2
    • 6 - Filter weather dates matching S2 dates
    • 7 - Filter S2 dates with U-wind<1
    • 8 - Merge dataframes
  • Search and fuse S1, S2 and weather data with custom raster or vector
    • 0 - Install Spacesense Client Library and dependencies
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Configure the API Key by setting the SS_API_KEY environment variable
    • 3 - Define AOI and output options
    • 4 - Search S1
    • 5 - Search S2
    • 6 - Search Weather
    • 7 - Filter search results
    • 8 - Select S2 bands
    • 9 - Provide custom data
    • 10 - Obtain all S1 and S2 images, and Fuse with custom files
    • 11 - See the fused dataset
    • 12 - Plot the fused dataset with matplotlib
• Use Cases
  • Time series of vegetation indices
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Define AOI and output options
    • 3 - Search S2
    • 4 - Specify bands and pre-calculated indices
    • 5 - Filter search results
    • 6 - Obtain S2 data through Fuse function
    • 7 - See the fused dataset
    • 8 - Plot timeseries of vegetation indices with matplotlib
  • Monthly optical images
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Define AOI and output options
    • 3 - Search S2
    • 4 - Specify bands
    • 5 - Filter search results
    • 6 - Select one image per month
    • 7 - Obtain S2 data through Fuse function
    • 8 - Plot using matplotlib
  • Create custom water or vegetation index
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Define AOI and output options
    • 3 - Search S2
    • 4 - Specify bands
    • 5 - Obtain S2 data through Fuse function
    • 6 - Postprocessing: Calculating indices
    • 7 - Visualization
  • Time series of custom index and masking
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Define AOI and output options
    • 3 - Search S2
    • 4 - Specify bands
    • 5 - Obtain S2 data through Fuse function
    • 6 - Postprocessing: Calculating indices
    • 7 - Postprocessing: Masking
    • 8 - Visualization mask
    • 9 - Create time series of Chl_a and SPM with and without the mask applied
    • 10 - Plot time series
  • Clustering to find homogenous areas
    • 1 - Import spacesense object(s) and other dependencies
    • 2 - Define AOI and output options
    • 3 - Search S2
    • 4 - Specify bands
    • 5 - Obtain S2 data through Fuse function
    • 6 - Look at the RGB image
    • 7 - Cluster S2 data with the cluster function
    • 8 - Plot the clustered function using the util function
• Python API Documentation
  • Client/Fusion
  • Fusion Result
  • Search Results
    • Sentinel-1 Search Result
    • Sentinel-2 Search Result
    • ERA5 Weather Search Results
  • File Loaders
    • Raster
    • Vector
  • Job
    • Example
  • Utilities
• Geospatial Fundamentals
  • Synthetic Apature Radar (SAR) Basics
    • What is Synthetic Apature Radar?
    • Sentinel-1 SAR in SpaceSense’s library
    • SpaceSense SAR Processing Pipeline
  • Optical Basics
    • What is Optical Remote Sensing?
    • Example: Vegetation Indices
    • Sentinel-2 Optial imagery in SpaceSense’s Library
  • Vegetation Indices
    • What is a Vegetation Index?
    • Sentinel-2 Spectral Mapping
    • Vegetation Indices provided by SpaceSense
  • External Resources
• Changelog
  • 0.8.0
  • 0.7.0
  • 0.6.0
  • 0.5.0
  • 0.4.1
  • 0.4.0

Index

• Index