Weaver REST API – OGC API – Processes

Finds Sentinel-2 products on Copernicus using filtering parameters and performs band calculation on retrieved Sentinel-2 products to evaluate algae bloom for water quality assessment.

Performs band calculation on Sentinel-2 product bands to evaluate algae bloom for water quality assessment.

Searches for Sentinel-2 L2A products on Earth-Search using filtering parameters and performs band calculation on matched Sentinel-2 products to evaluate algae bloom for water quality assessment. For each match, reference bands are downloaded, processed, and returned as output with raw data, color map and plot visualization. Each output file will be named with the ID of the original product to distinguish them in case of multiple matches.

Performs band calculation on Sentinel-2 L2A product bands to evaluate algae bloom for water quality assessment from a specific STAC Item reference from Earth-Search platform. Reference bands are downloaded, processed, and returned as output with raw data, color map and plot visualization.

Performs a calculation over imagery bands and returns the resulting GeoTiff.

Retrieves relevant data or files resolved from a collection reference using its metadata, queries and desired outputs.

Downloads the Copernicus product from S3 using the Sentinel-2 SAFE manifest.

Downloads the GeoTiff band from the Sentinel-2 product STAC Item using the requested band selection criteria. It is assumed that the referenced STAC Item contains the desired GeoTiff Asset with the 'common band name' associated to the band (see https://github.com/stac-extensions/eo#common-band-names).

Selects the single file at the provided index within an array of files.

Transforms a file input into JSON file containing an array of file references as value.

Extracts and fetches NetCDF files from a JSON file containing an URL string array, and provides them on the output directory.

Extracts and fetches NetCDF files from a Metalink file containing an URL, and outputs the NetCDF file at a given. Index of the list.

Pixel-wise classification and probability of recognized objects on a Sentinel-2 satellite image using pre-trained deep learning model. The model was trained from results and discoveries from CS-CANADA/MUSES01 experiments. The package was improved and extended to provide additional functionalities during OGC-TB16 developments.

Applies the specified color mapping with scaled bins to a geospatial image.

Performs a reprojection over image bands and returns the resulting GeoTiff.

Searches a catalog for Sentinel-2 products using filtering parameters and returns all matched locations. Returned matches will be either S3 or direct HTTPS references depending on the catalog.