Deep Learning-Driven Transformation of Remote Sensing Education for Ecological Civilization and Sustainable Development

Yuanyuan Chen, Shaohua Lei (), Qiang Yang, Jie Zhu and Yunfei Xiang
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Yuanyuan Chen: College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Shaohua Lei: National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Qiang Yang: College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Jie Zhu: College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Yunfei Xiang: College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China

Sustainability, 2025, vol. 17, issue 17, 1-24

Abstract: Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields.

Keywords: deep learning; remote sensing education; teaching reform; ecological civilization; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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