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.

利用遥感技术和机器学习方法，本文展示了如何使用软件、多标签分类器来预测具有复杂异质性的生态系统服务。

利用多标签软分类量化异质生态系统服务

Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft  Classification

Remote sensing technology has become a promising tool in yield prediction.
Most prior work employs satellite imagery for county-level corn yield
prediction by spatially aggregating all pixels within a county into a single
value, potentially overlooking the detailed information and valuable insights
offered by more granular data. To this end, this research examines each county
at the pixel level and applies multiple instance learning to leverage detailed
information within a county. In addition, our method addresses the "mixed
pixel" issue caused by the inconsistent resolution between feature datasets and
crop mask, which may introduce noise into the model and therefore hinder
accurate yield prediction. Specifically, the attention mechanism is employed to
automatically assign weights to different pixels, which can mitigate the
influence of mixed pixels. The experimental results show that the developed
model outperforms four other machine learning models over the past five years
in the U.S. corn belt and demonstrates its best performance in 2022, achieving
a coefficient of determination (R2) value of 0.84 and a root mean square error
(RMSE) of 0.83. This paper demonstrates the advantages of our approach from
both spatial and temporal perspectives. Furthermore, through an in-depth study
of the relationship between mixed pixels and attention, it is verified that our
approach can capture critical feature information while filtering out noise
from mixed pixels.

遥感技术在产量预测中已成为一种有前途的工具，本研究运用多实例学习在像素级别对每个县进行研究，利用细粒度数据的详细信息，并通过引入注意机制来解决特征数据集和作物掩膜之间分辨率不一致导致的混合像素问题，实验结果表明该模型在美国玉米带的过去五年中优于其他四个机器学习模型，并在 2022 年达到最佳性能，R2 值为 0.84，RMSE 为 0.83，进一步通过对混合像素和注意力之间关系的深入研究验证了该方法能够捕捉关键特征信息并过滤混合像素中的噪声。

像素中的县区学习：基于注意力加权多实例学习的玉米产量预测

Learning county from pixels: Corn yield prediction with  attention-weighted multiple instance learning

In the last several years, remote sensing technology has opened up the
possibility of performing large scale building detection from satellite
imagery. Our work is some of the first to create population density maps from
building detection on a large scale. The scale of our work on population
density estimation via high resolution satellite images raises many issues,
that we will address in this paper. The first was data acquisition. Labeling
buildings from satellite images is a hard problem, one where we found our
labelers to only be about 85% accurate at. There is a tradeoff of quantity vs.
quality of labels, so we designed two separate policies for labels meant for
training sets and those meant for test sets, since our requirements of the two
set types are quite different. We also trained weakly supervised footprint
detection models with the classification labels, and semi-supervised approaches
with a small number of pixel-level labels, which are very expensive to procure.

利用遥感技术的建筑物检测创建人口密度地图，包括数据采集、半监督方法等解决策略。