Utilized the Google Earth Engine environment to develop Google Cloud-based functions for replacing the aforementioned procedures: - Conducting calculations of vegetative indexes including NDVI, NDMI, NDWI, and potential future additions. - Utilizing GeoTIFF indexes to generate prescription maps in GeoJSON format. - Retrieving a time series of vegetation index values. Furthermore, Google Earth Engine serves as a valuable platform for executing GIS common algorithms, such as overlay analysis, based on GeoTIFF files uploaded, computed, or accessible on the platform. Additionally, Google Earth Engine is employed for the visualization of GeoTIFF data within the company's GIS platform. This tool, previously created using Google Maps and JavaScript SDK under the Angular 2 framework, has been successfully integrated.

The project showcases a comprehensive layout of a condition-based maintenance (CBM) system, along with illustrative code examples. It serves to exemplify how paying agencies can effectively process and utilize Sentinel data for the purpose of scrutinizing aid applications pertaining to common agricultural policies. The European Commission Joint Research Center has developed a cloud infrastructure solution that underlies its CBM, devised exclusively from open-source components. Notably, the CAP monitoring service I have developed is employed by various EU member states, including Denmark, Portugal, Spain, Belgium, Germany, and France.

I specialize in collaborative profile writing for developers seeking assistance in optimizing their profiles. Here is the revamped sentence: "The tool, built on Python, functions by extracting WGS84 coordinates (latitude and longitude) from Open Street Maps using Nominatim and GeoPy libraries, using a CSV file that contains string addresses. Potential enhancements include implementing duplicate checks within the list and incorporating an approximation for handling unknown addresses.

The tool enables the generation of a multi-band GeoTIFF with a 20m resolution for a Sentinel-2 level 2A tile, derived from any practical TOI (2020 - 2022). The resulting output is clipped to the given AOI extent in .geojson format. Moreover, a set of example images will be provided as post-processing products, including: - A multi-band .tif file encompassing all the bands from Sentinel2 data - A Natural Color .tif file comprising RGB bands - A False Color .tif file incorporating the near-infrared band - An Scl .tif file representing the classification based on the sentinel color scale (https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm)

Project objectives: - Successfully execute a project to enhance the portfolio. - Implement Git version control for effective project management. - Utilize Jupyter Notebook as a tool for presenting discoveries. - Conduct an end-to-end data analytics project. - Acquire proficiency in data analytics workflows.

GeoPandas is a project that aims to enhance pandas objects with geographic data support. Presently, it incorporates GeoSeries and GeoDataFrame types as subclasses of pandas. GeoPandas objects effectively operate on shapely geometry objects, performing geometric operations. The geometry operations in GeoPandas follow a cartesian approach. The object's coordinate reference system (CRS) can be stored as an attribute and is automatically established during file loading. Coordinate systems can be transformed using the to_crs() method. Currently, coordinates for operations are not enforced, but this may change in the future. Documentation is available at geopandas.org (current release) and Read the Docs (release and development versions). Within the scope of this open-source project, my contributions involved integrating several methods from shapely into GeoSeries and GeoDataFrame.

I have successfully developed and deployed a Django web application with pre-built templates and a SQLite DB. The application includes custom-made models, views, forms, authentication, and logout functionalities, all created by me. The website has been deployed to https://simoparmeg.pythonanywhere.com/admin/.

The Python/Django application enables restaurants to effectively manage their inventory tracking system. Users can log in as either "front-of-house" or admin. Front-of-house users have the ability to view the menu, access customer orders, and create or modify them. On the other hand, admins possess additional functionalities such as viewing the menu, creating, modifying, and removing dishes, adjusting dish recipes, monitoring ingredient inventory, managing ingredient stock, purchasing ingredients based on a shopping list, editing stock quantities directly, as well as analyzing profit and loss and determining the most popular dishes.