Black carbon (BC) is an important pollutant aerosol emitted by numerous human
activities, including gas flaring. Improper combustion in flaring activities
can release large amounts of BC, which is harmful to human health and has a
strong climate warming effect. To our knowledge, no study has ever directly
monitored BC emissions from satellite imagery. Previous works quantified BC
emissions indirectly, by applying emission coefficients to flaring volumes
estimated from satellite imagery. Here, we develop a deep learning framework
and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and
quantify BC emissions from gas flaring. We find that BC emissions in this
region amount to about 1 million tCO$_{2,\mathrm{eq}}$, or 1 million passenger
cars, more than a quarter of which are due to 10 sites alone. This work
demonstrates the operational monitoring of BC emissions from flaring, a key
step in implementing effective mitigation policies to reduce the climate impact
of oil and gas operations.

通过卫星图像检测和量化燃气喷烧导致的黑碳排放量，结果显示该地区的黑碳排放量相当于 100 万吨 CO2 等效，大约占总排放量的四分之一，该研究为实施有效的减轻石油和天然气运营的气候影响的政策提供了关键性步骤。

利用深度学习从多光谱影像中识别的北非燃气燃烧引起的黑碳喷流

Black carbon plumes from gas flaring in North Africa identified from  multi-spectral imagery with deep learning

Landslides are one of the most critical and destructive geohazards.
Widespread development of human activities and settlements combined with the
effects of climate change on weather are resulting in a high increase in the
frequency and destructive power of landslides, making them a major threat to
human life and the economy. In this paper, we explore methodologies to map
newly-occurred landslides using Sentinel-2 imagery automatically. All
approaches presented are framed as a bi-temporal change detection problem,
requiring only a pair of Sentinel-2 images, taken respectively before and after
a landslide-triggering event. Furthermore, we introduce a novel deep learning
architecture for fusing Sentinel-2 bi-temporal image pairs with Digital
Elevation Model (DEM) data, showcasing its promising performances w.r.t. other
change detection models in the literature. As a parallel task, we address
limitations in existing datasets by creating a novel geodatabase, which
includes manually validated open-access landslide inventories over
heterogeneous ecoregions of the world. We release both code and dataset with an
open-source license.

本文研究了利用 Sentinel-2 影像自动地绘制新发生滑坡的方法。我们提出了一种新颖的深度学习架构，用于融合 Sentinel-2 双时相影像和数字高程模型数据，并展示了其在与文献中的其他变化检测模型相比具有良好性能。同时，我们还创建了一个包括全球异质生态区域的人工验证开放式滑坡清单的新型地理数据库。我们以开源许可证发布了代码和数据集。

基于变化检测的 Sentinel-2 卫星图像滑坡地形图绘制

Landslide mapping from Sentinel-2 imagery through change detection

Mapping buildings and roads automatically with remote sensing typically
requires high-resolution imagery, which is expensive to obtain and often
sparsely available. In this work we demonstrate how multiple 10 m resolution
Sentinel-2 images can be used to generate 50 cm resolution building and road
segmentation masks. This is done by training a `student' model with access to
Sentinel-2 images to reproduce the predictions of a `teacher' model which has
access to corresponding high-resolution imagery. While the predictions do not
have all the fine detail of the teacher model, we find that we are able to
retain much of the performance: for building segmentation we achieve 78.3%
mIoU, compared to the high-resolution teacher model accuracy of 85.3% mIoU. We
also describe a related method for counting individual buildings in a
Sentinel-2 patch which achieves R^2 = 0.91 against true counts. This work opens
up new possibilities for using freely available Sentinel-2 imagery for a range
of tasks that previously could only be done with high-resolution satellite
imagery.

使用 Sentinel-2 卫星图像进行建筑物和道路的自动标记，为高分辨率遥感图像的代替提供了可能。学生模型通过训练来复原教师模型的预测结果，虽然没有教师模型那么精细，但建筑物分割中的 mIoU 达到了 78.3%、个别建筑物计数达到 R^2=0.91。新的方法可以利用免费的 Sentinel-2 图像进行以往只能使用高分辨率卫星图像完成的各种任务。