The service aims at splitting the input shapefile for each FAO-LCCS taxo code. Within the Ailanthus workflow of the Internal Joint Intiative, it represents the step 6 and 7 of the second stage (where a 2 classes problem is considered). The algorithm is run by considering training and test data allowing to map the FAO-LCCS classes into the corresponding numerical classes.

The service, starting from a multi-class shapefile containing the land cover classes in the scene, expressed in FAO-LCCS taxonomy, aims to generate a series of shapefiles, one for each different class. A numeric code is also associated to each class. The algorithm uses as input training and test data used to obtain the multi-class land cover mapping of the scene by a supervised, pixel-based classification. Within the Ailanthus workflow of the Internal Joint Intiative, it represents the step 1 and 2 of the first stage (where a multi-class problem is considered).

This service aims at creating a stack of coregistered multi-spectral raster images. It groups four multi-seasons images and stack them to obtain a unique image at native resolution (30 meters) by using the Landsat 5 sensor. It represents the Step 3 of the Ailanthus Workflow within the Internal Joint Initiative.

This service allows to filter the list of samples in the MasterARMS csv file on date range (start and end date in ISO format) and geographic location (lat and lon in decimal degrees). Moreover, also the selection of specific samples is permitted. It represents the Step 3 of the ARMS Workflow within the Internal Joint Initiative.

A service that aims at verifying the species names (performing the taxonomy check) by using the input file SIA.csv. It represents the Step 1 of the Crustaceans Workflow within the Internal Joint Initiative.

This service aims to create a dataset of environmental predictors linked to SIA’s coordinates by exploiting oceanographic and bioclimatic raster layers. It extracts Enviromental Data and represents the Step 4 of the Crustaceans Workflow within the Internal Joint Initiative.

This service aims at modeling trophic positions as a function of environmental drivers (e.g., through GAMs, etc.) by using the "Bioclim_predic.csv" file. It represents the final step (Step 5) of the Crustaceans Workflow within the Internal Joint Initiative.

This service intersects the map(s) (geographical spatial polygons) from GBIF occurrence(s) with the locations of a list of taxa detected from eDNA metabarcoding and which were not included in the NIS checklist to verify if such eDNA detection(s) is/are likely to be new NIS detected in that location from eDNA sequences. It represents the Step 5 of the Metabarcoding Workflow within the Internal Joint Initiative.

This service aims at extracting the Deciduous Vegetation Layer from multiclass land cover map at 30 meters, resampling it at 2 meters and masking all the pixels, of the 2 meters stack, not overlaid by the deciduous vegetation layer. It represents the Step 8 of the Ailanthus Workflow within the Internal Joint Initiative.

It is a Support Vector Machine, pixel-based, classifier trained for a multi-class problem. It represents the Step 4 of the Ailanthus Workflow within the Internal Joint Initiative.