Emerson Lowell

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Prodigy

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Freelancer Overview

I am an Earth and Environmental Studies graduate with over four years of experience in data analysis, scientific research, and environmental science, bringing a strong foundation in handling complex datasets and ensuring data accuracy for impactful outcomes. My background includes working extensively with GIS mapping, statistical modeling, and climate data analysis, where I applied Python, SQL, R, and MATLAB to manage, annotate, and interpret large volumes of environmental and geospatial data. I am skilled in leveraging machine learning and cloud platforms to support data-driven projects, and I have hands-on experience with tools like ArcGIS and QGIS for labeling and managing spatial datasets. My work has required precise data collection, annotation, and validation to support research in climate adaptation, hydrology, and renewable energy, and I am passionate about contributing to high-quality AI training data and innovative solutions for real-world challenges.

Labeling Experience

Environmental Data Annotation for AI Models

ProdigyTextEntity Ner ClassificationQuestion Answering

I worked on environmental and geospatial datasets to support AI and machine learning models. The project scope included annotating satellite imagery for land-use classification, tagging ecological reports for biodiversity monitoring, and labeling climate time-series data for anomaly detection. Tasks performed: Drew bounding boxes, polygons, and segmentation masks on satellite images using ArcGIS, QGIS, CVAT, and Label Studio. Tagged species names, geographic locations, and environmental terms in reports using Prodigy and Python workflows. Labeled anomalies in climate datasets (temperature, rainfall) and categorized seismic/pollution readings. Project size: Annotated 10,000+ satellite images, tagged hundreds of documents, and processed multi-year climate datasets with thousands of entries. Quality measures: Applied double-blind review for image annotations. Used Python scripts for consistency checks. Maintained inter-annotator agreement scores above 95%.

I worked on environmental and geospatial datasets to support AI and machine learning models. The project scope included annotating satellite imagery for land-use classification, tagging ecological reports for biodiversity monitoring, and labeling climate time-series data for anomaly detection. Tasks performed: Drew bounding boxes, polygons, and segmentation masks on satellite images using ArcGIS, QGIS, CVAT, and Label Studio. Tagged species names, geographic locations, and environmental terms in reports using Prodigy and Python workflows. Labeled anomalies in climate datasets (temperature, rainfall) and categorized seismic/pollution readings. Project size: Annotated 10,000+ satellite images, tagged hundreds of documents, and processed multi-year climate datasets with thousands of entries. Quality measures: Applied double-blind review for image annotations. Used Python scripts for consistency checks. Maintained inter-annotator agreement scores above 95%.

2019 - 2022

Education

Work History