ABOUT EVENT

Description

This workshop will be led by Dr. Joseph Percival and Dr. Ryan Perroy from the Spatial Data Analysis and Visualization (SDAV) Lab.

This free, hands-on workshop demonstrates how Python-based data science techniques can be applied to remote sensing and environmental research. Attendees will explore the intersection of ecology and geospatial analysis by working with lidar point clouds and satellite imagery to gain insights into forest structure and environmental features. They will also be introduced to PyForestScan, a newly developed Python library from the SDAV Lab, which is tailored for studying forest structure using lidar data. Attendees will work through practical coding exercises in JupyterLab, where they will analyze lidar point clouds with PyForestScan to extract key forest metrics such as canopy height, plant area density, and foliage height diversity. They will also learn how to integrate satellite imagery for feature detection and classification, applying advanced AI and computer vision algorithms to map environmental features like individual trees.

Prerequisites

• Basic familiarity with Python is recommended.
• Laptop with reliable internet access.

Learning objectives

By the end of the workshop, participants will understand how to acquire and preprocess both lidar and satellite datasets, compute forest metrics in Python, and experiment with AI-driven computer vision methods for detecting and classifying environmental features. They will also learn to create reproducible geospatial workflows and visualizations, ultimately gaining practical skills for environmental data analysis and remote sensing applications.

Tools Used for this workshop

• This workshop will use Python as the core language within JupyterLab. Key libraries and tools include PyForestScan for lidar analysis, PDAL and rasterio for geospatial data processing, NumPy and pandas for data manipulation, and computer vision frameworks (such as PyTorch) for feature detection. All required tools will be introduced during the session, and example notebooks will be made available to participants.