As the global population continues to expand, the demand for natural
resources increases. Unfortunately, human activities account for 23% of
greenhouse gas emissions. On a positive note, remote sensing technologies have
emerged as a valuable tool in managing our environment. These technologies
allow us to monitor land use, plan urban areas, and drive advancements in areas
such as agriculture, climate change mitigation, disaster recovery, and
environmental monitoring. Recent advances in AI, computer vision, and earth
observation data have enabled unprecedented accuracy in land use mapping. By
using transfer learning and fine-tuning with RGB bands, we achieved an
impressive 99.19% accuracy in land use analysis. Such findings can be used to
inform conservation and urban planning policies.

全球人口不断增长，对自然资源的需求也在增加。遗憾的是，人类活动占到了 23% 的温室气体排放。幸运的是，遥感技术已经成为管理我们的环境的有价值的工具。这些技术使我们能够监测土地利用，规划城市区域，并推动农业、气候变化缓解、灾难恢复和环境监测等领域的进展。通过利用迁移学习和细调 RGB 波段，我们在土地利用分析方面取得了令人印象深刻的 99.19% 准确度。这样的研究结果可以用来制定保护和城市规划政策。

使用 EuroSAT 和迁移学习进行土地利用和土地覆盖（LULC）的映射

Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer  Learning

With the looming threat of climate change, neglected tropical diseases such
as dengue, zika, and chikungunya have the potential to become an even greater
global concern. Remote sensing technologies can aid in controlling the spread
of Aedes Aegypti, the transmission vector of such diseases, by automating the
detection and mapping of mosquito breeding sites, such that local entities can
properly intervene. In this work, we leverage YOLOv7, a state-of-the-art and
computationally efficient detection approach, to localize and track mosquito
foci in videos captured by unmanned aerial vehicles. We experiment on a dataset
released to the public as part of the ICIP 2023 grand challenge entitled
Automatic Detection of Mosquito Breeding Grounds. We show that YOLOv7 can be
directly applied to detect larger foci categories such as pools, tires, and
water tanks and that a cheap and straightforward aggregation of frame-by-frame
detection can incorporate time consistency into the tracking process.

应对气候变化带来的威胁，利用遥感技术和 YOLOv7 识别昆虫繁殖地点，对防止登革热等热带病的传播具有潜在的全球影响力。

YOLOv7 用于蚊虫繁殖地检测和跟踪

YOLOv7 for Mosquito Breeding Grounds Detection and Tracking

Timely and accurate assessment of hurricane-induced building damage is
crucial for effective post-hurricane response and recovery efforts. Recently,
remote sensing technologies provide large-scale optical or Interferometric
Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous
event, which can be readily used to conduct rapid building damage assessment.
Compared to optical satellite imageries, the Synthetic Aperture Radar can
penetrate cloud cover and provide more complete spatial coverage of damaged
zones in various weather conditions. However, these InSAR imageries often
contain highly noisy and mixed signals induced by co-occurring or co-located
building damage, flood, flood/wind-induced vegetation changes, as well as
anthropogenic activities, making it challenging to extract accurate building
damage information. In this paper, we introduced an approach for rapid
post-hurricane building damage detection from InSAR imagery. This approach
encoded complex causal dependencies among wind, flood, building damage, and
InSAR imagery using a holistic causal Bayesian network. Based on the causal
Bayesian network, we further jointly inferred the large-scale unobserved
building damage by fusing the information from InSAR imagery with prior
physical models of flood and wind, without the need for ground truth labels.
Furthermore, we validated our estimation results in a real-world devastating
hurricane -- the 2022 Hurricane Ian. We gathered and annotated building damage
ground truth data in Lee County, Florida, and compared the introduced method's
estimation results with the ground truth and benchmarked it against
state-of-the-art models to assess the effectiveness of our proposed method.
Results show that our method achieves rapid and accurate detection of building
damage, with significantly reduced processing time compared to traditional
manual inspection methods.

我们提出了一种从 InSAR 图像中快速检测飓风后建筑损伤的方法，通过使用综合性因果贝叶斯网络对风、洪水、建筑损伤和 InSAR 图像的复杂因果依赖关系进行编码，并将 InSAR 图像的信息与洪水和风的先验物理模型相融合，无需地面真实标签，进一步共同推断大规模未观测到的建筑损伤。验证结果表明，相比传统的手动检查方法，我们的方法在迅速准确检测建筑损伤方面，大大减少了处理时间。