Air pollution kills 7 million people annually. Brick manufacturing industry
is the second largest consumer of coal contributing to 8%-14% of air pollution
in Indo-Gangetic plain (highly populated tract of land in the Indian
subcontinent). As brick kilns are an unorganized sector and present in large
numbers, detecting policy violations such as distance from habitat is
non-trivial. Air quality and other domain experts rely on manual human
annotation to maintain brick kiln inventory. Previous work used computer vision
based machine learning methods to detect brick kilns from satellite imagery but
they are limited to certain geographies and labeling the data is laborious. In
this paper, we propose a framework to deploy a scalable brick kiln detection
system for large countries such as India and identify 7477 new brick kilns from
28 districts in 5 states in the Indo-Gangetic plain. We then showcase efficient
ways to check policy violations such as high spatial density of kilns and
abnormal increase over time in a region. We show that 90% of brick kilns in
Delhi-NCR violate a density-based policy. Our framework can be directly adopted
by the governments across the world to automate the policy regulations around
brick kilns.

空气污染每年夺走 700 万人的生命。砖瓦制造行业是最大的煤炭消费者之一，导致印度 - 恒河平原（印度次大陆人口密集地带）空气污染的 8%-14%。由于砖瓦窑是一个无组织的部门，并且数量庞大，所以检测与居住地的距离等政策违规行为并不容易。空气质量和其他领域的专家依赖于人工手动标注来维护砖瓦窑清单。以往的研究使用基于计算机视觉的机器学习方法从卫星图像中检测砖瓦窑，但其范围有限且标记数据的工作量很大。本文中，我们提出了一个框架，用于在像印度这样大的国家中部署可扩展的砖瓦窑检测系统，并从印度 - 恒河平原的 28 个地区的 5 个州识别出 7477 个新的砖瓦窑。然后，我们展示了检查政策违规行为（如高空间密度的窑炉和某一地区随时间异常增长）的有效方式。我们发现 90% 的德里 - 国家首都区的砖瓦窑违反了基于密度的政策。我们的框架可以直接被全球各国政府采用，以自动化砖瓦窑的政策监管。

基于卫星影像的砖窑检测与合规监测的可扩展方法：印度部署案例研究

Scalable Methods for Brick Kiln Detection and Compliance Monitoring from  Satellite Imagery: A Deployment Case Study in India

Foundation models, i.e. large neural networks pre-trained on large text
corpora, have revolutionized NLP. They can be instructed directly (e.g.
(arXiv:2005.14165)) - this is called hard prompting - and they can be tuned
using very little data (e.g. (arXiv:2104.08691)) - this technique is called
soft prompting. We seek to leverage their capabilities to detect policy
violations. Our contributions are: We identify a hard prompt that adapts
chain-of-thought prompting to policy violation tasks. This prompt produces
policy violation classifications, along with extractive explanations that
justify the classification. We compose the hard-prompts with soft prompt tuning
to produce a classifier that attains high accuracy with very little
supervision; the same classifier also produces explanations. Though the
supervision only acts on the classifications, we find that the modified
explanations remain consistent with the (tuned) model's response. Along the
way, we identify several unintuitive aspects of foundation models. For
instance, adding an example from a specific class can actually reduce
predictions of that class, and separately, the effects of tokenization on
scoring etc. Based on our technical results, we identify a simple workflow for
product teams to quickly develop effective policy violation detectors.

利用大规模文本数据训练的基础模型可以通过硬提示和软提示技术检测政策违规，本文采用链式思考提示模型对该任务进行硬提示，并结合软提示技术进行模型训练和分类解释，基于实验结果提出了有效的政策违规检测工作流程。