In recent years, California's electrical grid has confronted mounting
challenges stemming from aging infrastructure and a landscape increasingly
susceptible to wildfires. This paper presents a comprehensive framework
utilizing computer vision techniques to address wildfire risk within the
state's electrical grid, with a particular focus on vulnerable utility poles.
These poles are susceptible to fire outbreaks or structural failure during
extreme weather events. The proposed pipeline harnesses readily available
Google Street View imagery to identify utility poles and assess their proximity
to surrounding vegetation, as well as to determine any inclination angles. The
early detection of potential risks associated with utility poles is pivotal for
forestalling wildfire ignitions and informing strategic investments, such as
undergrounding vulnerable poles and powerlines. Moreover, this study
underscores the significance of data-driven decision-making in bolstering grid
resilience, particularly concerning Public Safety Power Shutoffs. By fostering
collaboration among utilities, policymakers, and researchers, this pipeline
aims to solidify the electric grid's resilience and safeguard communities
against the escalating threat of wildfires.

通过利用计算机视觉技术，在加州电力网络中借助谷歌街景图像识别电线杆和评估与周围植被的距离以及倾斜角度，这项研究提供了一个全面的框架，重点关注易燃的电线杆，以防止野火起火并做出战略投资决策来加强电力网络的韧性。

基于 2D 街景图像的电线杆火灾风险检查

Utility Pole Fire Risk Inspection from 2D Street-Side Images

Carbon offset programs are critical in the fight against climate change. One
emerging threat to the long-term stability and viability of forest carbon
offset projects is wildfires, which can release large amounts of carbon and
limit the efficacy of associated offsetting credits. However, analysis of
wildfire risk to forest carbon projects is challenging because existing models
for forecasting long-term fire risk are limited in predictive accuracy.
Therefore, we propose an explainable artificial intelligence (XAI) model
trained on 7 million global satellite wildfire observations. Validation results
suggest substantial potential for high resolution, enhanced accuracy
projections of global wildfire risk, and the model outperforms the U.S.
National Center for Atmospheric Research's leading fire model. Applied to a
collection of 190 global forest carbon projects, we find that fire exposure is
projected to increase 55% [37-76%] by 2080 under a mid-range scenario
(SSP2-4.5). Our results indicate the large wildfire carbon project damages seen
in the past decade are likely to become more frequent as forests become hotter
and drier. In response, we hope the model can support wildfire managers,
policymakers, and carbon market analysts to preemptively quantify and mitigate
long-term permanence risks to forest carbon projects.

提出了可解释人工智能（XAI）模型，用于预测更精确的全球野火风险，以支持野火管理者、政策制定者和碳市场分析师事先量化和减轻森林碳项目的长期稳定性风险。