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Hi there 👋

An enthusiastic and results-oriented team player with an extensive academic and professional background in Earth Observation and Geoscience and Environmental engineering. Recently I graduated with MSc in Applied Earth Observation and Geoanalysis (EAGLE) from Julius-Maximilians-Universität of Würzburg (JMU). My main activities on Github involve in the practical applications of remote sensing using geospatial analysis and Machine learning. I started my coding journey using R and over time I gravitated toward python. I make sure to provide valuable contribution and technical insights to the organizations I am part of. I am excited about leveraging data-driven insights to solve complex problems and drive impactful decision-making.

Walid Ghariani's Projects

agriroof icon agriroof

Potential Flat Rooftop Detection using LiDAR and GIS for Rooftop Farming

aus_bushfires_app icon aus_bushfires_app

Tracking and Analyzing the Spatial Temporal Variation of Australian Bushfire 2019-2020 Using MODIS and VIIRS Data

better-python-59-ways icon better-python-59-ways

Code Sample of Book "Effective Python: 59 Specific Ways to Write Better Pyton" by Brett Slatkin

getweather icon getweather

Get weather data for any location in the world using Bash and OpenWeatherMap API

mb2-final-project icon mb2-final-project

Final Project for the Programming and Geostatistical Analysis course (MSc. Earth Observation and Geoanalysis)

mb3_lidar icon mb3_lidar

Retrieving LAI, LAD and Forest Structure using LiDAR data

open-geo-tutorial icon open-geo-tutorial

Tutorial of fundamental remote sensing and GIS methodologies using open source software in python

py-cdr icon py-cdr

Python API for accessing climate data records from the NOAA National Centers for Environmental Information (NCEI)

rgedi icon rgedi

rGEDI: An R Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing.

sen2r icon sen2r

Find, Download and Process Sentinel-2 Data

spatial-kfold icon spatial-kfold

spatial resampling for more robust cross validation in spatial studies

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