SpaceSense Library
Contents
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
- Getting Started
- Installation
- 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
- 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
- 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
- Monthly optical images
- Create custom water or vegetation index
- 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
- Python API Documentation
- Geospatial Fundamentals
- Changelog