Geospatial Data Science Student

Jessica Marin

+ College Park, MD # ********@***.*** 267-***-**** ð jess-marin12

Education

University of Maryland

BS in Geospatial Data Science

Expected December 2025

Geospatial Data Science Cumulative GPA: 3.25/4.0

Coursework Highlights: Spatial Data Analysis and Visualization, Geographic Information System (GIS), Machine Learning for Geospatial Data

Projects

Developing a Predictive Machine Learning Model for Crop Health University of Maryland

GEOG442

Sept 2024 - Dec 2024

Analyzed environmental parameters to assess the health of corn and soybean crops.

Conducted literature review to identify common predictor and response variables in crop health studies and explored various machine learning techniques.

Developed an ensemble-boosted regression tree model to account for spatial and temporal variations in land surface temperature and precipitation affecting the growing seasons of corn and soybeans.

Collaborated effectively with two graduate students and one other undergraduate student for the duration of the project, enhancing my ability to work in a team of people across academic levels. Monitoring Land Cover Change in the Amazon Using Satellite Data University of Maryland

GEOG272

Sept 2024 - Dec 2024

Conducted independent research to evaluate the relevance of forest loss in the Amazon, utilizing data analysis and literature review to justify the need for further environmental regulation.

Extracted data from the Hansen Global Forest Change dataset using Google Earth Engine to analyze temporal changes in forest cover.

Created visualizations to highlight forest density and areas of forest loss to aid in understanding deforestation patterns over time.

Satellite Image Analysis and NDVI Change Detection Using Python University of Maryland

GEOG276

Sept 2024 - Dec 2024

Developed a Python-based analysis to calculate and visualize NDVI from Landsat satellite imagery.

Utilized rasterio to process red and near-infrared bands from Landsat images collected in 2017 and 2018, and normalized raster data for consistent analysis.

Calculated NDVI for both time periods and identified changes in vegetation health by comparing two datasets.

Technologies

Languages: Python, RStudio, SQL, JavaScript

Technologies: ESRI ArcGIS, QGIS, Geomatica 2017, Google Earth Engine, Microsoft Office Suite Jessica Marin - Page 1 of 1

Last updated in March 2025

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