As efforts to mitigate the effects of climate change grow, reliable and
thorough reporting of greenhouse gas emissions are essential for measuring
progress towards international and domestic emissions reductions targets. New
Zealand's national emissions inventories are currently reported between 15 to
27 months out-of-date. We present a machine learning approach to nowcast
(dynamically estimate) national greenhouse gas emissions in New Zealand in
advance of the national emissions inventory's release, with just a two month
latency due to current data availability. Key findings include an estimated
0.2% decrease in national gross emissions since 2020 (as at July 2022). Our
study highlights the predictive power of a dynamic view of emissions intensive
activities. This methodology is a proof of concept that a machine learning
approach can make sub-annual estimates of national greenhouse gas emissions by
sector with a relatively low error that could be of value for policy makers.

通过机器学习方法预测新西兰国家温室气体排放量的动态变化，得出国家总排放量 20 年以来下降 0.2% 的结果，这种方法为政策制定者提供了一个较低误差的子年度温室气体排放估计的概念证明。

新西兰温室气体清单的动态预测

Dynamic nowcast of the New Zealand greenhouse gas inventory

The aquaculture sector in New Zealand is experiencing rapid expansion, with a
particular emphasis on mussel exports. As the demands of mussel farming
operations continue to evolve, the integration of artificial intelligence and
computer vision techniques, such as intelligent object detection, is emerging
as an effective approach to enhance operational efficiency. This study delves
into advancing buoy detection by leveraging deep learning methodologies for
intelligent mussel farm monitoring and management. The primary objective
centers on improving accuracy and robustness in detecting buoys across a
spectrum of real-world scenarios. A diverse dataset sourced from mussel farms
is captured and labeled for training, encompassing imagery taken from cameras
mounted on both floating platforms and traversing vessels, capturing various
lighting and weather conditions. To establish an effective deep learning model
for buoy detection with a limited number of labeled data, we employ transfer
learning techniques. This involves adapting a pre-trained object detection
model to create a specialized deep learning buoy detection model. We explore
different pre-trained models, including YOLO and its variants, alongside data
diversity to investigate their effects on model performance. Our investigation
demonstrates a significant enhancement in buoy detection performance through
deep learning, accompanied by improved generalization across diverse weather
conditions, highlighting the practical effectiveness of our approach.

使用深度学习方法提高浮标检测和管理的准确性和鲁棒性，通过使用源自新西兰贻贝养殖场的多样化数据集和迁移学习技术，研究表明深度学习通过提高鲁棒性能和跨多样的天气条件进行浮标检测的实际有效性。