深度学习模型用于监测联合国教科文组织全球地质公园的景观变化
Thi Tram Pham1, Kinh Bac Dang2, Tuan Linh Giang3
1Institute of Human Geography, Vietnam Academy of Social Sciences, No.176, Thai Ha, Dong Da, Hanoi, Viet Nam.
Journal of environmental management
|February 28, 2024
概括
联合国教科文组织全球地质公园面临旅游业发展带来的挑战. 这项研究开发了一种深度学习模型,即双边细分网络 (BiSeNet),用于监测土地使用变化和保护地质遗址.
科学领域:
- 地质科学是地球科学.
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 联合国教科文组织全球地质公园 (UGGp) 促进地质旅游,但由于发展而面临景观退化.
- 监测土地使用/覆盖变化和景观指标对于可持续管理至关重要.
研究的目的:
- 开发和评估双边细分网络 (BiSeNet) 模型,用于监测越南UGGp的土地使用/覆盖变化和景观指标.
- 评估旅游业和城市化对地质遗产遗址的影响.
主要方法:
- 开发了升级的U结构神经网络BiSeNet模型用于图像细分.
- 对传统机器学习算法 (最大概率,SVM,随机森林) 进行了评估,对八个训练好的BiSeNet模型进行了评估.
- 应用景观指数 (DCAD,TECI,SHDI) 来监测30年来Dak Nong UGGp的变化.
主要成果:
- BSN-Nadam模型以0.1的信息损失达到94%的精度,在每月的土地使用/覆盖面变化分析中被证明是有效的.
- 监测显示,自2010年以来,农业和旅游活动对Daknong地质公园的18个地理地点产生了重大影响.
- 鉴定了由于城市化和旅游业而导致的地质遗址的不可持续管理和恶化.
结论:
- 开发的BiSeNet模型是实时监测UGGps土地利用变化的宝贵工具.
- 调查结果强调迫切需要有效的土地利用政策,以减轻对地质遗产的负面影响.
- 该研究为联合国教科文组织管理层提供了关于遗址恶化的及时警报.
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