Project TetraNet is a novel research project used for wildfire mitigation using footage from rapid nanosatellite deployed into space. We created a nanosatellite that utilizes computer vision models, image segmentation U-Net convolutional networks, linear regression artifical neural networks, and heuristic fire spread simulators in order to accurately apply wildfire patterns to a real-world setting. The idea is to allow anyone to access quality terrain analysis for a fraction of the cost.
Our project is a low-cost alternative to traditional satellites deployed into space that track geographical features and trends. Current methods of mitigation are often expensive and inaccessible for general public use, with data decentralized and hard to orchestrate for wildfire responses. TetraNet explores the viability of an open-source application with a device that anyone can use. The goal with TetraNet is to provide means for both monitoring ongoing wildfires as well as predicitng future ones with preventative measures.
Dense vegetation tracking via semantic segmentation
Heat mapping for wildfire tracking
We engineered a device to deploy into the Earth's atmosphere.
Our TetraNet nanosatellite conducts advanced image segmentation analysis to determine the terrain and topology of the land through scanning for dense vegetation on the ground. The images below provide insight into how we created the image segmentation U-Net convolutional neural network.
The above image was annotated using Apeer to produce the image below. All of our images were obtained through our original footage from our launch to space in 2021 and then annotated similarly using Apeer.
Afterward, we converted our annotated Apeer image to the "mask" that we could use to train the image segmentation U-Net convolutional network.
After applying our trained U-Net to the above image, it produced the "predicted mask" below, showing how our image segmentation model was highly accurate in detecting dense vegetation.
Lastly, we overlayed the "predicted mask" images on the original images by utilizing OpenCV to further prove how accurate the model was.
Using the forest fires data set from the UC Irvine Machine Learning Repository, we utilized the Azure Machine Learning services to automate the creation of neural network for predicting the number of hectares burned in a forest fire based on input values like temperature, relative humidity, wind, and rain as well as the FFMC, DMC, DC, and ISI indexes.
We utilized a mathematical model utilizing a heuristic to depict how fire spreading works while wind is blowing. The results of the code can be seen through this GIF.
We created an extensive front-end and back-end application to display the data like Google Maps location video, and fire simulation, utilizing the Google Maps API. Furthermore, we utilized the Python Flask back-end framework to make API calls to Azure's machine learning and cloud services. The following is the core logic for retrieving the U-Net segmentation masks:
# An example URL for accessing the web service
azure_aci_url = 'http://67534526-f00a-ds33-a447-22a76351d991.eastus.azurecontainer.io/score'
files = {'image' : open(image_directory, 'rb').read()
response = requests.post(azure_aci_url, files=files)
mask_data = response.json()
UI Demonstration
UI_Demonstration.mp4
With the trending temperature increase as a result of global warming, our nanosatellite can provide substantial impact by possibly providing real-time alerts to fire departments and public safety services across the world to not only mitigate wildfires and save lives but also prevent severe smoke inhalation and save millions of dollars in damage caused by wildfires annually. With Azure's ACI services, our U-Net model predictions were able to be processed with relative simplicity.
Project TetraNet was created by Abhinav Bichal, Sarvesh Sathish, and Pranay Shah for the 2021 Microsoft Azure AI competition.