Polar TEP make satellite data (Sentinels, Landsat, and other providers) easily accessible for browsing or analysis within the cloud or within the user’s own environment.
Users can work with the data without worrying about synchronization issues, storage, processing, de-compression algorithms, meta-data, or sensor bands.
A service-oriented satellite imagery infrastructure takes care of all the complexity of handling satellite imagery archives and makes it available via easy-to-integrate web services. The main features of the system are:
- Full resolution preview over the web
- Time-lapse functionality
- Time-series statistical info service
- Analysis tools for an area or a point of choice
- Script-based on-the-fly definition of new products
- Reprojected WMS services for integration into 3rd party tools
- APIs for advanced feature integration
A cloud-based data API removes the complexity of processing large volumes of satellite data. Users can instantly access Sentinel, Landsat, and other Earth observation imagery. Polar TEP features include:
Global Archive of Earth Observation Data
Sentinels (1 SAR, 2 MSI, 3 OLCI and SLSTR, 5P), Landsat (8, ESA archive of 5 and 7), ENVISAT, MODIS.
Serverless Processing with JavaScript
Polar TEP offers numerous predefined visualization options and allows EO products to be created with simple JavaScript configurations.
Bring your own
Users can access their own data stored in S3 buckets. The data stays fully under user control and no replication is needed.
REST APIs for Advanced Feature Integration
Users can build new Earth observation services using Polar TEP REST interfaces and open-source libraries. The Polar TEP Python Package allows users to make WMS and WCS web requests to download and process satellite images from various data sources within Python scripts and Jupyter Notebooks.
Use in Desktop and Web Applications
Easy integration with desktop and web GIS software such as ArcGIS, QGIS, MapBox, Carto, Google Maps, Leaflet, OpenLayers and others. Standard web services – WMS, WMTS, WCS and WFS – are also available, configurable, and customizable with various output formats, projections, and processing algorithms.
EO-learn for Easy Extraction of Valuable Information
The EO-learn library acts as a bridge between the Earth observation/remote sensing field and the Python ecosystem for data science and machine learning.