Recent geospatial machine learning studies have shown that the results of
model evaluation via cross-validation (CV) are strongly affected by the
dissimilarity between the sample data and the prediction locations. In this
paper, we propose a method to quantify such a dissimilarity in the interval 0
to 100%, and from the perspective of the data feature space. The proposed
method is based on adversarial validation, which is an approach that can check
whether sample data and prediction locations can be separated with a binary
classifier. To study the effectiveness and generality of our method, we tested
it on a series of experiments based on both synthetic and real datasets and
with gradually increasing dissimilarities. Results show that the proposed
method can successfully quantify dissimilarity across the entire range of
values. Next to this, we studied how dissimilarity affects CV evaluations by
comparing the results of random CV and of two spatial CV methods, namely block
and spatial+ CV. Our results showed that CV evaluations follow similar patterns
in all datasets and predictions: when dissimilarity is low (usually lower than
30%), random CV provides the most accurate evaluation results. As dissimilarity
increases, spatial CV methods, especially spatial+ CV, become more and more
accurate and even outperforming random CV. When dissimilarity is high (>=90%),
no CV method provides accurate evaluations. These results show the importance
of considering feature space dissimilarity when working with geospatial machine
learning predictions, and can help researchers and practitioners to select more
suitable CV methods for evaluating their predictions.

通过对地理空间机器学习预测结果和样本数据之间的差异度进行量化，在 0 到 100% 的区间内，基于对抗验证的方法可以准确度量差异度。此方法在合成和真实的数据集上测试，并逐渐增加差异度来研究其效果和普适性。测试结果表明该方法可以成功地量化整个数值范围内的差异度，并揭示出在地理空间机器学习预测中考虑特征空间差异度的重要性，以便选择更适合的交叉验证方法进行预测评估。

利用对抗验证量化地理空间机器学习预测的差异性

On the use of adversarial validation for quantifying dissimilarity in  geospatial machine learning prediction

Rare object detection is a fundamental task in applied geospatial machine
learning, however is often challenging due to large amounts of high-resolution
satellite or aerial imagery and few or no labeled positive samples to start
with. This paper addresses the problem of bootstrapping such a rare object
detection task assuming there is no labeled data and no spatial prior over the
area of interest. We propose novel offline and online cluster-based approaches
for sampling patches that are significantly more efficient, in terms of
exposing positive samples to a human annotator, than random sampling. We apply
our methods for identifying bomas, or small enclosures for herd animals, in the
Serengeti Mara region of Kenya and Tanzania. We demonstrate a significant
enhancement in detection efficiency, achieving a positive sampling rate
increase from 2% (random) to 30%. This advancement enables effective machine
learning mapping even with minimal labeling budgets, exemplified by an F1 score
on the boma detection task of 0.51 with a budget of 300 total patches.

本文提出了一种解决罕见物体检测任务的方法，通过离线和在线聚类方法，显著提高样本曝光效率并实现有效的机器学习地图绘制。以肯尼亚和坦桑尼亚的塞伦盖蒂玛拉地区的 bomas 检测为例，实验证明了检测效率的显著提升，预算为 300 个样本总数时的 boma 检测任务的 F1 分数为 0.51。

高分辨率遥感卫星图像中稀有目标检测的引导方法

Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery

As geospatial machine learning models and maps derived from their predictions
are increasingly used for downstream analyses in science and policy, it is
imperative to evaluate their accuracy and applicability. Geospatial machine
learning has key distinctions from other learning paradigms, and as such, the
correct way to measure performance of spatial machine learning outputs has been
a topic of debate. In this paper, I delineate unique challenges of model
evaluation for geospatial machine learning with global or remotely sensed
datasets, culminating in concrete takeaways to improve evaluations of
geospatial model performance.

本文旨在讨论全球或遥感数据下地理空间机器学习模型的评估挑战，以及如何改进评估方法，以确保其准确性和适用性。