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Computer Science > Machine Learning

arXiv:2406.17147 (cs)
[Submitted on 24 Jun 2024]

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
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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.
Comments: This work has been submitted to the IEEE for possible publication
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2406.17147 [cs.LG]
  (or arXiv:2406.17147v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2406.17147

Submission history

From: Zhihui Tian [view email]
[v1] Mon, 24 Jun 2024 21:38:13 UTC (8,207 KB)
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