Quantifying GHG Emission Reductions from AI-Driven Wildfire Detection

The goal of this project is to develop a scientifically robust framework for quantifying the greenhouse gas (GHG) emission reductions enabled by Bayes Studio’s AI-driven wildfire detection and monitoring technology. By leveraging satellite data, remote sensing techniques, and wildfire emission models, the project aims to accurately estimate the carbon emissions avoided through early wildfire intervention. This research will support grant compliance, provide data-driven evidence of environmental impact, and contribute to broader climate change mitigation efforts by informing policy decisions, carbon credit opportunities, and sustainable wildfire management strategies.

Sensor Enclosure Industrial Design

The main objective of this project is to design and develop a robust, modular, and weather-resistant enclosure specifically tailored for multi-sensor forest monitoring devices. This enclosure will securely house sensors such as cameras, air quality detectors, humidity, and temperature sensors, ensuring accurate data collection while protecting the components from harsh environmental conditions such as rain, humidity, extreme temperatures, dust, and debris. The design will prioritize modularity to allow for easy assembly, maintenance, and adaptability for various sensor configurations. Additionally, the enclosure will include provisions for a power supply, such as integrated wire inlets and optional solar panel support, enabling sustainable and uninterrupted operation in remote forest environments. The final product will meet functional, environmental, and manufacturing requirements.

Modular Weather-Resistant Sensor Enclosure Design

The main objective of this project is to design and develop a robust, modular, and weather-resistant enclosure specifically tailored for multi-sensor forest monitoring devices. This enclosure will securely house sensors such as cameras, air quality detectors, humidity, and temperature sensors, ensuring accurate data collection while protecting the components from harsh environmental conditions such as rain, humidity, extreme temperatures, dust, and debris. The design will prioritize modularity to allow for easy assembly, maintenance, and adaptability for various sensor configurations. Additionally, the enclosure will include provisions for a power supply, such as integrated wire inlets and optional solar panel support, enabling sustainable and uninterrupted operation in remote forest environments. The final product will meet functional, environmental, and manufacturing requirements, with the design optimized for 3D printing and scalability.

Literature Review on Mycelium Fire-Retardant

Bayes Studio is exploring innovative solutions to prevent wildfires using natural materials. This project focuses on compiling, analyzing, and synthesizing existing research on mycelium as a fire-retardant. The primary goal is to understand the current landscape of mycelium-based fire-retardant research, identify any knowledge gaps, and provide insights that can guide future research and innovation efforts at Bayes Studio. By leveraging existing studies, the project aims to assess the effectiveness of mycelium in fire prevention and its potential applications. This will help Bayes Studio in strategizing their research direction and innovation in sustainable fire-retardant solutions.