APLICACIONES DE APRENDIZAJE AUTOMÁTICO Y TELEDETECCIÓN EN LA INVESTIGACIÓN DE ISLAS DE CALOR
DOI:
https://doi.org/10.56238/revgeov17n2-098Palabras clave:
Islas de Calor Urbanas, Aprendizaje Automático, Inteligencia Artificial, Teledetección, Análisis BibliométricoResumen
Las islas de calor urbanas (ICU) plantean desafíos críticos de adaptación climática conforme se acelera la urbanización global. Si bien la inteligencia artificial y la teledetección han surgido como herramientas poderosas para el análisis térmico urbano, el crecimiento acelerado de la investigación en esta intersección carece de una síntesis exhaustiva. Este estudio bibliométrico examinó 381 publicaciones (2004–2026) de la Web of Science Core Collection para mapear la estructura, evolución y bases de conocimiento del área. Utilizando el paquete bibliometrix en R, realizamos un análisis de rendimiento (productividad, citas) y un mapeo científico (coautoría, co-palabras, acoplamiento bibliográfico y redes de cocitación). Los resultados revelaron un crecimiento reciente exponencial: las publicaciones aumentaron de 11 (2019) a 135 (2025), concentrando el 95% de la producción en siete años (2019–2025). La concentración geográfica es acentuada: China (33,6%), India (9,45%) y EE. UU. (7,61%) dominan la producción, mientras que el África Subsahariana y América Latina permanecen subrepresentadas, pese a su alta vulnerabilidad térmica. La investigación converge en la teledetección térmica mejorada por aprendizaje automático, con Random Forest (7,87% de los artículos) como algoritmo dominante y la temperatura de la superficie terrestre (28,87%) como variable principal. Las métricas de citas indican madurez del campo (índice h = 47, índice g = 75, promedio de citas = 20,93), consolidándose sobre fundamentos de climatología urbana, metodología de teledetección y aprendizaje automático. No obstante, el análisis temático reveló brechas críticas: la investigación prioriza la detección en detrimento de los impactos en la salud, la validación de mitigación e integración de políticas públicas.
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