Identifying Deforestation from Satellite Images

This is a computer vision model that detects if forest areas experienced deforestation. Logistics regression is a method to help predict the probability rather the image we inputted belongs to which possible category. We can input a satellite image of a forest region into the model, which then categorizes the image classified as experienced deforestation or hasn't experienced deforestation. By logistic regression and Python techniques, the model does image classifications and calculates the probability which category the satellite image belongs to. The model will process and analyze the image data, which can be used to understand if the forest region experienced deforestation. This project was created within the AI4ALL Ignite Program SP24 with a supportive instructor, instructor assistant and mentor.

Problem Statement

Predicting deforestation using satellite imagery is crucial because it is a step forward in climate change mitigation efforts. Deforestation contributes significantly to greenhouse gas emissions, which exacerbate climate change. By accurately predicting areas at risk of deforestation, we can take real-time measures to mitigate its impact on the climate. Secondly, preserving biodiversity and ecosystems relies heavily on monitoring deforestation patterns. Forests are home to countless endangered species of plants and animals, many of which risk losing their habitats and extinction. By predicting deforestation, we can prioritize areas for protection and restoration, helping to safeguard biodiversity. Moreover, human populations often depend on specific areas for their livelihoods. Many communities residing in or near forests rely on them for resources such as food, water, and medicine. Predicting deforestation allows for better planning and management of resources, ensuring the sustainability of these communities.

The ability to predict deforestation using satellite imagery is not only crucial for combating climate change but also for preserving biodiversity, ecosystems, and the livelihoods of human populations dependent on forested areas. The significance of deforestation extends globally for identifying deforestation patterns in different regions and contributing to initiatives of preserving natural ecosystems. It is important to identify if an ecosystem is experiencing deforestation early, so advocates can stop deforestation before a forest suffers from negative environmental effects

Key Results

Enumerate the main results of this project in a list and describe them.

EXAMPLE:

1. Recorded over 1,000 unique prompts and their responses generated by ChatGPT
2. Identified three biases in ChatGPT's responses
   • When prompted about this world event
   • When prompted about this field of science
   • When prompted about this political party

Methodologies

(UPDATE IN README.md)

EXAMPLE: To accomplish this, we utilized the OpenAI API to interact with ChatGPT, and we designed a custom Python script to generate diverse prompts and collect corresponding responses. The data was then processed and analyzed using pandas, enabling us to detect patterns and biases in the AI model's outputs. Engineered a Python script to generate over 1,000 prompts and elicit their responses from ChatGPT, utilizing pandas to collect the data. When prompted for solutions to this specific relevant crisis, nearly 80% of ChatGPT's responses promoted a certain worldview.

Data Sources

Kaggle Dataset: Link

Technologies Used

(UPDATE IN README.md) List the technologies, libraries, and frameworks used in your project.

• Python
• pandas
• numpy
• Google Colaboratory

Authors

This project was completed in collaboration with:

• Betty Cheng (bettyc1@uw.edu and GitHub profile)
• Emily Rosenfeld (erosenfeld7@gatech.edu and GitHub profile)
• Brianna Stan (brianna-alexand.stan@yale.edu and GitHub profile)
• Mridul Pahwa (mpahw002@fiu.edu and GitHub profile)