derived data field delineation on-demand sentinel hub sentinel-2
An algorithm for automatic delineation of agricultural field boundaries from Sentinel-2 imagery.The main part of the algorithm is an advanced pre-trained machine learning model. It was trained on multiple locations throughout Europe for a time interval from March to August.The algorithm is an improved version of the one developed for NIVA project. More information about the process is available in a blog post and in a recording of a webinar.To run the field delineation process, navigate to EDC Browser, select the required input parameters and follow the check-out wizard to complete your order. Produced data will be uploaded directly into your JupyterLab environment on EDC.
Global coverage but intended for areas with agricultural fields
January 2016 - ongoing
On-demand
| Name | Description | Type |
|---|---|---|
| aoi | Area of interest | Polyon or bounding box |
| time_interval | A time range of Sentinel-2 data to process | String |
| maxcc | Maximum cloud coverage of Sentinel-2 data. | Float from interval `[0, 1]` |
| Name | Description |
|---|---|
| results.gpkg | A Geopackage containing delineated polygons of fields from selected AOI. Polygons are projected to WGS84 coordinate reference system. |
Prices are defined based on amount of data required to process. Check here for more info about pricing and restrictions.
Creative Commons Attribution 4.0 International License. Attribution: Contains modified Copernicus Sentinel data (year) processed by Sentinel Hub
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