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.

Links:

Citation:

Z. Tian, J. Upchurch, G. A. Simon, J. Dubeux, A. Zare, C. Zhao, and J. B. Harley, "Quantifying Heterogeneous Ecosystem Services with Multi-Label Soft Classification," in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 427–431, 2024.

@inproceedings{tian2024quantifying,
title={Quantifying Heterogeneous Ecosystem Services with Multi-Label Soft Classification},
author={Tian, Zhihui and Upchurch, John and Simon, G Austin and Dubeux, Jos{\'e} and Zare, Alina and Zhao, Chang and Harley, Joel B},
booktitle={IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium},
pages={427--431},
year={2024},
organization={IEEE}
}