Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification

• URL: http://arxiv.org/abs/2406.17147v1
• Date: Mon, 24 Jun 2024 21:38:13 GMT
• Title: Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification
• Authors: Zhihui Tian, John Upchurch, G. Austin Simon, José Dubeux, Alina Zare, Chang Zhao, Joel B. Harley,
• Abstract summary: quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. Ground truth labels, such as biodiversity, are very difficult and expensive to measure. proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem.
• Score: 2.4213557151485396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding and quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. The advancement of remote sensing technology and machine learning techniques has greatly facilitated this process. Yet, ground truth labels, such as biodiversity, are very difficult and expensive to measure. In addition, more easily obtainable proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem. In this paper, we demonstrate how land use proxy labels can be implemented with a soft, multi-label classifier to predict ecosystem services with complex heterogeneity.

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