Evaluating the accuracy of manual classification in satellite images using supervised algorithms

Zulema Yamileth  Cervantes Hernández; Emmanuel Morales García; Cecilia  Cruz López; Itzel Alessandra Reyes Flores

Autores/as

Zulema Yamileth Cervantes Hernández Universidad Veracruzana https://orcid.org/0009-0000-9453-2582
Emmanuel Morales García Universidad Veracruzana https://orcid.org/0000-0002-6837-9227
Cecilia Cruz López Universidad Veracruzana https://orcid.org/0000-0002-9156-5669
Itzel Alessandra Reyes Flores Universidad Veracruzana https://orcid.org/0000-0003-0733-8453

Palabras clave:

Remote sensing, Machine learning, Infrared imaging, Data science

Resumen

This research evaluated manual land-cover classification using images extracted from Sentinel-2. Supervised algorithms were applied to validate and enhance this process. Three algorithms were selected based on their computational efficiency: K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM).The results show that KNN achieved optimal performance, demonstrating a strong balance between accuracy, F1 score, and runtime compared to RF. RF, in turn, obtained higher precision and F1 scores, indicating its superior ability to correctly identify classes; however, it required greater computational resources. SVM exhibited lower performance in the evaluated metrics but achieved a shorter runtime. Nevertheless, it was identified as the algorithm with the greatest limitations in separating classes within this dataset derived from the different study areas.Overall, the comparison confirmed that the manual classifications developed in QGIS are supported and validated by the application of these supervised methods. The use of such algorithms contributes to improving accuracy, consistency, and efficiency in geospatial classification tasks.

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Biografía del autor/a

Zulema Yamileth Cervantes Hernández, Universidad Veracruzana

Zulema Yamileth Cervantes Hernández is a student pursuing a bachelor's degree in Statistics at the Faculty of Statistics and Computer Science, Universidad Veracruzana. She is currently in her final semester of her degree. Her research interests include data science, machine learning, and spatial statistics.
Emmanuel Morales García, Universidad Veracruzana

He holds a Bachelor's degree in Statistical Sciences and Techniques and a Specialist in Statistical Methods from the University of Veracruz, with a Master's degree in Geospatial Information Sciences from the Geo Center, Mexico City (CONAHCYT Center). He is currently pursuing a PhD in Computer Science at the University of Veracruz. He is a professor in the Bachelor's program in Statistics and in the Specialization in Statistical Methods at the same university, where he has supervised 12 undergraduate theses and 4 specialized theses. He also has experience as a Statistical Analyst at the Government Program Office of the State of Veracruz. My research interests include computational methodologies, statistical programming, multivariate statistics, spatial analysis, data science, and statistical models, with applications in biology, medicine, and administrative and social sciences. He has participated in various national and international conferences.
Cecilia Cruz López , Universidad Veracruzana

She holds a PhD in Educational Research (Universidad Veracruzana), a Master of Science degree specializing in Applied Statistics (ITESM Monterrey Campus), and a Bachelor of Statistics degree (Universidad Veracruzana). She has published four books, nine book chapters, ten research articles, and one article in Extended Proceedings. She has supervised three master's theses, 24 specialized theses, and 26 undergraduate theses. Her research interests include Statistics Education and Applications of Statistical Methodology.

She collaborated with CENEVAL from 2010 to 2017, advising, evaluating, and developing items for the Extra-Es Statistics exam.
She is currently the coordinator of the Specialization in Statistical Methods.
Itzel Alessandra Reyes Flores , Universidad Veracruzana

With a solid academic background and a passion for Computer Science, I have forged a career dedicated to advancing Computer Science and improving the digital User Experience (UX). I hold a Ph.D. in Computer Science and a Master's degree in User-Centered Interactive Systems.

My work experience spans teaching in programming, web and mobile development, UX, and software engineering, as well as consulting in UX/UI design and software development. I possess knowledge in programming languages such as C#, Java, Python, PHP, and JavaScript; development frameworks including .NET, React, and Node.js; web design technologies such as HTML, CSS, and Wordpress; web service development and usage with REST and SOAP APIs; mobile development with Android Studio; design tools like Photoshop, Figma, Adobe XD, and Corel Draw; database management with MySQL, SQLite, MongoDB, and SQL Server; development methodologies like SCRUM and Design Thinking; and the use of user-centered interface design guidelines.

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