D

data preprocessing

GEOP4TH (pronounced /ʤiɒpɑːθ/ jee-uh-pa-th for GEOspatial Python Pre-Processing Platform for Trajectories in Hydro-socio-ecosystems) is a collection of generic, format-agnostic, python tools (geobricks) designed to easily standardize, manipulate and visualize space-time data. These geobricks are made to be assembled into complete pre-processing workflows for specific data or to specific models. Such workflows can be collaboratively developped and shared within GEOP4TH. In the end, this toolbox aims at facilitating working on hydro-socio-ecosystems trajectories and diagnostics. Have fun! :)

The project entailed building an image classification model using PyTorch, integrated with Azure ML for data handling, model training, and deployment. Key steps included initializing Azure ML Workspace, processing the dataset, developing the model in PyTorch, and deploying it on Azure for real-world inference. This project showcases the synergy between advanced machine learning frameworks and cloud computing platforms in solving complex image classification tasks.

This script contains a collection of functions designed to put EthoVision tracks into a more usable format for further data analysis. This includes interpolating, smoothing, extracting data and metadata and writing them out as separate csv files.

website: https://simula-srl.gitlab.io/damast/README.html

Machine Learning - Binary Classification

Machine Learning - Binary Classification (NLP)

Machine Learning - Multiclass Classification