VTOL Drone with ML-Based Fire Detection Part 2

The project aims to design and develop a small-scale, cost-effective VTOL (Vertical Take-Off and Landing) drone equipped with a machine learning-based fire detection system. The primary goal is to create a modular drone that can be easily upgraded with additional sensors and components in the future. Students will focus on integrating standard cameras and developing a machine learning model for fire detection, avoiding the high costs associated with thermal cameras. The project will also involve developing flight control software to ensure the drone's stability and maneuverability. This initiative provides an opportunity for students to apply their classroom knowledge in drone design, software development, and machine learning, while contributing to climate action through enhanced fire detection capabilities.

Low-Cost VTOL Development with Fire Detection via ML

The objective of this project is to design and develop a small-scale, cost-effective VTOL drone equipped with a flight controller and a fire detection system using machine learning (ML). The drone should be designed with modularity in mind, allowing for future sensor and component upgrades. Given budget constraints, the project will focus on using standard cameras and ML-based fire detection instead of expensive thermal cameras. Students will be responsible for developing the drone’s software, integrating sensors, and implementing the ML-based fire detection system while ensuring the design is scalable for larger applications.

Business Analytics for Alvia Systems Inc

The main objective of this project is to equip students with advanced geospatial intelligence and business‐driven spatial analytics skills by integrating provided wildfire maps with oil and gas pipeline datasets to identify high‑risk segments and their owning companies. Leveraging these insights, students will design targeted customer‑acquisition strategies for pipeline operators—using proximity‑based market segmentation and value‑proposition modeling—to drive proactive stakeholder engagement and market expansion. Students will collect and preprocess GIS data, execute spatial overlays and buffer analyses in QGIS/ArcGIS, automate workflows with Python scripts, and synthesize their findings into annotated risk maps and a concise, stakeholder‑ready assessment report.

Kickstarter Campaign Strategy for Alvia Systems

Alvia Systems Inc. is seeking a strategic plan for launching a successful Kickstarter campaign for one of its innovative products. The goal of this project is to develop a comprehensive campaign strategy that effectively communicates the product's value proposition, engages potential backers, and achieves the desired funding target. Learners will apply their knowledge of marketing, digital communication, and project management to create a compelling campaign narrative and promotional plan. The project involves researching successful Kickstarter campaigns, identifying target audiences, and designing engaging content that aligns with Alvia Systems' brand identity. The team will also outline a timeline for campaign activities and propose metrics for evaluating success. This project provides an opportunity to bridge classroom learning with real-world application in the dynamic field of crowdfunding.