Hello OpenAI Community,
I’m currently developing an AI-driven deforestation detection pipeline, codename EVERGREEN, with the goal of using satellite imagery to identify logging activities and predict future deforestation. The project’s core purpose is to address global environmental issues like deforestation, biodiversity loss, and climate change.
About the Project:
The system will leverage AI and machine learning to analyze satellite data (Sentinel-1/2, Landsat 8) and detect:
-
Logging Activities – Accurately identify deforestation, including selective logging and clear-cutting.
-
Water-Body Distinction – Differentiate between logged areas and water bodies.
-
Pre-Logging Indicators – Detect roads, small clearings, and infrastructure that often precede logging.
-
Wildfire Detection – Integrate real-time monitoring for wildfire outbreaks affecting forests.
The vision is to create a scalable, actionable solution that can be used globally to monitor forests, predict logging hotspots, and mitigate the impact of deforestation.
Key Questions:
-
Model Development: I’m exploring the use of both optical (Sentinel-2) and SAR (Sentinel-1) data. What are best practices for integrating these two sources, especially for identifying small-scale logging and other land-use changes?
-
Real-Time Monitoring: What approaches can be used to process satellite data in near real-time, especially considering cloud cover and seasonal changes that affect imagery?
-
Additional Use Cases: Beyond logging, I’m considering adding functionality to detect mining activities and land-use changes related to agriculture. Would this be feasible, or is it too ambitious for the current scope?
-
Scaling and Deployment: What resources, platforms, or models would you recommend for scaling this project globally? How can I ensure the pipeline is robust enough to handle a diverse set of regions?
Funding Challenges:
As I move forward, securing funding for the project has been a challenge. I don’t have formal accreditation or a registered company yet, which limits my access to traditional funding sources. I am exploring:
• Government Grants (such as the Canada Digital Adoption Program, Mitacs)
• Crowdfunding options
• Partnerships with Universities and AI-focused organizations
However, I’m finding it difficult to identify seed funding sources that are available for individuals or early-stage innovators like myself. Any advice on navigating these challenges or suggestions for alternative funding routes would be greatly appreciated.
Next Steps:
I’m currently focusing on data collection, preprocessing, and setting up the AI models for initial testing in Pontiac, Quebec. After that, I plan to expand testing to regions with significant deforestation concerns, including the Amazon, Southeast Asia, and Africa.
Seeking Advice:
I’m looking for advice on model development, data integration, and suggestions for the best platforms/tools to use for large-scale processing. Additionally, any insights into overcoming the funding challenges I’m facing would be invaluable.
Thank you for your help, and I’m excited to connect with others working on AI for environmental monitoring.
Project Codename: EVERGREEN