Rapid and accurate building damage assessments from high-resolution satellite
imagery following a natural disaster is essential to inform and optimize first
responder efforts. However, performing such building damage assessments in an
automated manner is non-trivial due to the challenges posed by variations in
disaster-specific damage, diversity in satellite imagery, and the dearth of
extensive, labeled datasets. To circumvent these issues, this paper introduces
a human-in-the-loop workflow for rapidly training building damage assessment
models after a natural disaster. This article details a case study using this
workflow, executed in partnership with the American Red Cross during a tornado
event in Rolling Fork, Mississippi in March, 2023. The output from our
human-in-the-loop modeling process achieved a precision of 0.86 and recall of
0.80 for damaged buildings when compared to ground truth data collected
post-disaster. This workflow was implemented end-to-end in under 2 hours per
satellite imagery scene, highlighting its potential for real-time deployment.

通过人机协作工作流程，利用高分辨率卫星图像快速训练建筑损伤评估模型，并在密西西比州滚动叉市的龙卷风事件中运用，取得了 0.86 的精确度和 0.80 的召回率，证明该工作流程对于实时部署具有潜力。

快速建筑损害评估工作流程：适用于 2023 年密西西比州 Rolling Fork 龙卷风事件的实施

Rapid building damage assessment workflow: An implementation for the  2023 Rolling Fork, Mississippi tornado event

After a natural disaster, such as a hurricane, millions are left in need of
emergency assistance. To allocate resources optimally, human planners need to
accurately analyze data that can flow in large volumes from several sources.
This motivates the development of multimodal machine learning frameworks that
can integrate multiple data sources and leverage them efficiently. To date, the
research community has mainly focused on unimodal reasoning to provide granular
assessments of the damage. Moreover, previous studies mostly rely on
post-disaster images, which may take several days to become available. In this
work, we propose a multimodal framework (GaLeNet) for assessing the severity of
damage by complementing pre-disaster images with weather data and the
trajectory of the hurricane. Through extensive experiments on data from two
hurricanes, we demonstrate (i) the merits of multimodal approaches compared to
unimodal methods, and (ii) the effectiveness of GaLeNet at fusing various
modalities. Furthermore, we show that GaLeNet can leverage pre-disaster images
in the absence of post-disaster images, preventing substantial delays in
decision making.

提出多模态机器学习框架，并将预灾图像、气象数据和飓风轨迹相结合，以有效评估灾害的严重程度，从而避免决策延迟。

GaLeNet: 灾害预测、管理和救援的多模态学习

GaLeNet: Multimodal Learning for Disaster Prediction, Management and  Relief

Fast and reliable wireless communication has become a critical demand in
human life. When natural disasters strike, providing ubiquitous connectivity
becomes challenging by using traditional wireless networks. In this context,
unmanned aerial vehicle (UAV) based aerial networks offer a promising
alternative for fast, flexible, and reliable wireless communications in
mission-critical (MC) scenarios. Due to the unique characteristics such as
mobility, flexible deployment, and rapid reconfiguration, drones can readily
change location dynamically to provide on-demand communications to users on the
ground in emergency scenarios. As a result, the usage of UAV base stations
(UAV-BSs) has been considered as an appropriate approach for providing rapid
connection in MC scenarios. In this paper, we study how to control a UAV-BS in
both static and dynamic environments. We investigate a situation in which a
macro BS is destroyed as a result of a natural disaster and a UAV-BS is
deployed using integrated access and backhaul (IAB) technology to provide
coverage for users in the disaster area. We present a data collection system,
signaling procedures and machine learning applications for this use case. A
deep reinforcement learning algorithm is developed to jointly optimize the tilt
of the access and backhaul antennas of the UAV-BS as well as its
three-dimensional placement. Evaluation results show that the proposed
algorithm can autonomously navigate and configure the UAV-BS to satisfactorily
serve the MC users on the ground.

本文研究了无人机基站在自然灾害中提供快速，灵活和可靠的无线通信的控制方式，使用机器学习算法控制无人机的三维位置和天线倾斜角度，以便在灾区为遭受自然灾害影响的用户提供快速的通信服务。