ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Jorge Gálvez
Universidad de Guadalajara
0000-0001-6595-8605
Heriberto Zapata
Universidad de Guadalajara
Omar Ávalos
Universidad de Guadalajara
0000-0003-3859-3414
Nayeli Pérez Padilla
Universidad de Guadalajara
0000-0003-4558-8322
Víctor García
Universidad de Guadalajara
Acerca de
The monitoring of large areas is essential to assess their conditions and patterns of change and support decision-making from which different ways are look for ways and optimize these problems and techniques such as photogrammetry are used to assess changes in large areas and, in conjunction with semantic segmentation, are important to extract the necessary information and to provide classification analysis at the pixel level for an accurate assessment of irregular shapes The reference ground sample distance (GSD) method translates pixels into a unit of measurement, facilitating accurate image scale calculations and precise measurements. This method explores these strategies using deep learning, potentially improving accuracy and simplifying segmentation. It also looks at different works that talk about how segmentation and photogrammetry can be used in different areas, including their methods and results, to find the best ways to do things, adapt, and lead to new developments in the field.
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