Community Blogs

Our project for SEES 2024 was "Creating a Model to Identify High-Risk Areas for Flash Flooding in Houston, Texas." In this blog, I will discuss how we calculated which areas are "high-risk." There are many ways to identify the most at-risk points of flooding in a given region. Such points are commonly identified in a flood map, such as the one here: For our project, we decided to look at elevation data, which is perhaps the most intuitive: water flows downhill and will accumulate in the lowest regions. To get our elevation data, we used ...

SEES 2024 Experience Getting the opportunity to participate in the NASA SEES Earth System Explorers program has positively impacted my educational and career journey in more ways than I could've imagined. Throughout this internship, I gained an immense amount of knowledge in Earth science, utilizing the GLOBE Observer app, ArcGIS, Collect Earth Online, Python, and many other tools to better understand the environment around us. During the last few weeks of the program, a team of seven other interns and I completed our own research project called "Harnessing ...

In this blog post, I will go through every step of the internship and outline my experience! Before I do that, first of all, I would like to say that I am so happy that I got to spend my summer with SEES! This program was so amazing and helped guide me a bit more on what I want to do in my future career and in college! I am very grateful for all of our mentors who guided us through the program and provided great knowledge and support to all of us! Thank you for this opportunity. Modules: Climate Science Background and Exploring the Earth To prepare for the ...

The wildfire data analysis journey began with a dataset comprising over 1200 points located in the California region. To uncover the correlation between land cover types and wildfire risk, we first needed to determine the wildfire risk for each point using the FEMA Wildfire Risk Map. This step involved entering coordinates into the software to obtain the wildfire risk index for each location. From this dataset, we categorized approximately 325 points as having a very low risk, 350 as moderate risk, and 578 as very high risk for wildfires. These categories served as the ...

Roboflow is an open-source dataset management platform, where users can import data, label it, and then have a model train on it. For my group's project, CS-FLARE, we used Roboflow to label brush, grass, trees, and leaf litter in our NESW Images as shown below.  When I initially began labeling images, I tried using bounding boxes for each picture like the leaf litter in the image above. This became a bit annoying as sometimes the bounding boxes would cut parts of objects like trees, but it was still faster than using the polygon tool to manually trace around objects. ...