Patrick-Samson-Udama-Eneche/landscape-character-nigeria

This repository contains a collection of dataset and Python codes implemented in Jupyter Notebook environment for the landscape character assessment of Nigeria. We are open for collaboration and possibilities of scaling-out to other countries. # Landscape Characterization of Nigeria This repository contains a collection of Python scripts used for the landscape characterization of Nigeria, leveraging machine learning-based algorithms and spatial analysis techniques. The characterization focuses on the composition and configuration of Nigeria's biophysical landscape types/areas at a 1km resolution. All input datasets required for this assessment are provided within the repository, while the output results and other resources (e.g. Dashboard, datasets, etc.) are available in our mirrored project on Open Science Framework (OSF). ## Table of contents * Requirements * Getting Started * Notebooks * Data and Methods * Results * Usage * Contribution/Collaboration ## Requirements The results were produced using Python 3.12.3 and Jupyter Notebook in the following test environment: ``` Operating system: Windows x86_64 CPU: 13th Gen Intel(R) Core(TM) i7-13700H (14 Cores | 20 Log. Processors) memory (RAM): 16GB disk storage: 1TB GPU: NVIDIA RTX A500 ``` ## Getting Started To get started with this project, follow the steps below to set up your environment and install the necessary dependencies. ### Prerequisites #### Python Make sure Python 3.12 or later is installed on your system. You can download it from python.org. #### Setting Up a Virtual Environment It is recommended that a virtual environment be created to isolate project dependencies. See the link ### Install Libraries/Dependencies Run the following command to install all the required libraries: ``` pip install -r requirements.txt ``` ## Notebooks This repository is organized into four Jupyter notebooks; each handling a specific aspect of the research and in the order: ### 1. Google Earth Engine Dataset Acquisition * Notebook: /GEE_Dataset/GEE_data_acq.ipynb * Purpose: Acquires elevation, land cover, and Land Surface Temperature (LST) data. * Requirements: Google authentication is necessary. ### 2. Cluster Determination * Notebook: /c …