Geospatial Interpolation

Overview

A Python-based interpolation CLI that utilizes real-world point-cloud data and spatial interpolation techniques to create a smooth heightmap of a given area.

$$ \begin{align*} p_{1} &= (0.25, 0.25, 5.00) & p_{2} &= (0.50, 0.75, 2.00) \\ p_{3} &= (0.75, 0.50, 3.00) & p_{4} &= (1.00, 0.75, 6.00) \end{align*} $$

Inverse Distance Weighting	Shape Function

Features

• Command-Line Interface: Easily interpolate and save data directly from the command line.
• Multiple Interpolation Methods: Choose from two different methods of interpolation to suit your needs.
• Python Virtual Environment: Utilizes a Python virtual environment to manage dependencies and ensure a consistent runtime environment.

Notes

The application currently only supports the Inverse Distance Weighting and Shape Function methods of interpolation.

Prerequisites

Before using this application, ensure you have Python installed on your machine. You will also need to set up a virtual environment. After setting up your virtual environment, run

python -m pip install -r requirements.txt

to install all necessary dependencies.

Usage

python main.py [-h] [-p POWER] [-r RESOLUTION] [-s] {inverse_distance_weighting,shape_function} points

positional arguments:

• {inverse_distance_weighting,shape_function}: The interpolation method used.
• points: The relative path to the CSV file containing the 3-dimensional point data.

options:

• -h, --help: Show the help message and exit
• -p POWER, --power POWER: The power parameter, $p$, used in the Inverse Distance Weighting equation (default: 2).

$$ \displaystyle z_{p} = \frac{\sum_{i=1}^{n}{\frac{z_{i}}{d_{i}^{p}}}}{\sum_{i=1}^{n}{\frac{1}{d_{i}^{p}}}} $$

• -r RESOLUTION, --resolution RESOLUTION: The number of subdivisions along each axis (default: 50).
• -s, --save: Save the data as a CSV file to the same directory where the 3-dimensional point data is stored.