Simone Parmeggiani

Developed Google Cloud-based functions using Google Earth Engine environment to replace the following procedures:

• Calculate the vegetative indexes, NDVI, NDMI, NDWI, and more in the future.
• Use GeoTIFF indexes for calculating prescription maps in GeoJSON format.
• Retrieve a time series of vegetation index values.

In addition, Google Earth Engine is used as a platform to run GIS common algorithms, such as overlay analysis algorithm, based on GeoTIFF files uploaded, calculated, or available on the platform.

Also, Google Earth Engine is being used to visualize GeoTIFF data in the company's GIS platform. This tool has been previously developed using Google Maps and JavaScript SDK under Angular 2 framework.

The project shows a condition-based maintenance (CBM) system layout and code examples to demonstrate how paying agencies can process and use Sentinel data to check aid applications for common agricultural policies. The European Commission Joint Research Center's CBM is based on a cloud infrastructure solution that is modular and built exclusively on open-source components.

This CAP monitoring service I've developed is used by the EU member states, including Denmark, Portugal, Spain, Belgium, Germany, and France.

A Python-based tool that starts from a CSV containing string addresses and retrieves coordinates in WGS84 (latitude and longitude) from Open Street Maps using Nominatim and GeoPy library.
Possible improvements are:
-check for duplicates in the list
-implement unknown addresses approximation

The tool creates a multi-band GeoTIFF from all the spectral bands with a resolution of 20m of a Sentinel-2 level 2A tile from any practical TOI (2020 - 2022), clipped to the extent of the given AOI in .geojson format.
Also, some example images are provided as post-processing products:
as requested, a multi-band .tif containing all the bands from Sentinel2 data

• Natural Color .tif with RGB bands
• False Color .tif with near-infrared band
• Scl .tif with classification based on sentinel color scale (https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm)

GeoPandas is a project to add support for geographic data to pandas objects. It currently implements GeoSeries and GeoDataFrame types, which are subclasses of pandas.
GeoPandas objects can act on shapely geometry objects and perform geometric operations.

GeoPandas geometry operations are cartesian. The coordinate reference system (CRS) can be stored as an attribute on an object and is automatically set when loading from a file. Objects may be transformed into new coordinate systems with the to_crs() method. There is currently no enforcement of coordinates for operations, but that may change in the future.

Documentation is available at geopandas.org (current release) and Read the Docs (release and development versions).

In the scope of this open-source project, I contributed by adding some methods from shapely to GeoSeries and GeoDataframe