Rapid identification and response to breaking events, particularly those that
pose a threat to human life such as natural disasters or conflicts, is of
paramount importance. The prevalence of mobile devices and the ubiquity of
network connectivity has generated a massive amount of temporally- and
spatially-stamped data. Numerous studies have used mobile data to derive
individual human mobility patterns for various applications. Similarly, the
increasing number of orbital satellites has made it easier to gather
high-resolution images capturing a snapshot of a geographical area in sub-daily
temporal frequency. We propose a novel data fusion methodology integrating
satellite imagery with privacy-enhanced mobile data to augment the event
inference task, whether in real-time or historical. In the absence of boots on
the ground, mobile data is able to give an approximation of human mobility,
proximity to one another, and the built environment. On the other hand,
satellite imagery can provide visual information on physical changes to the
built and natural environment. The expected use cases for our methodology
include small-scale disaster detection (i.e., tornadoes, wildfires, and floods)
in rural regions, search and rescue operation augmentation for lost hikers in
remote wilderness areas, and identification of active conflict areas and
population displacement in war-torn states. Our implementation is open-source
on GitHub: this https URL

利用卫星图像和隐私增强的移动数据相结合的新型数据融合方法，用于增强实时或历史事件推断任务，应用场景包括农村地区的小规模灾害检测（如龙卷风、野火和洪水）、偏远荒野地区失踪徒步者的搜救以及战乱国家的活动冲突区域和人口流离失所地的识别。

利用卫星影像与 GPS 轨迹进行新数据来源的事件检测

Event detection from novel data sources: Leveraging satellite imagery  alongside GPS traces

The use of transfer learning methods is largely responsible for the present
breakthrough in Natural Learning Processing (NLP) tasks across multiple
domains. In order to solve the problem of sentiment detection, we examined the
performance of four different types of well-known state-of-the-art transformer
models for text classification. Models such as Bidirectional Encoder
Representations from Transformers (BERT), Robustly Optimized BERT Pre-training
Approach (RoBERTa), a distilled version of BERT (DistilBERT), and a large
bidirectional neural network architecture (XLNet) were proposed. The
performance of the four models that were used to detect disaster in the text
was compared. All the models performed well enough, indicating that
transformer-based models are suitable for the detection of disaster in text.
The RoBERTa transformer model performs best on the test dataset with a score of
82.6% and is highly recommended for quality predictions. Furthermore, we
discovered that the learning algorithms' performance was influenced by the
pre-processing techniques, the nature of words in the vocabulary, unbalanced
labeling, and the model parameters.

本研究探讨了采用不同类型的最先进的转换器模型对文本分类的性能，发现其中 RoBERTa 模型在测试数据集上表现最佳，可用于文本中灾难的检测。此外，我们发现预处理技术，词汇表中单词的性质，不平衡标签和模型参数对学习算法的性能产生影响。

基于 Transformer 的情感检测方法

Transformer-based approaches to Sentiment Detection

The analysis of natural disaster-related multimedia content got great
attention in recent years. Being one of the most important sources of
information, social media have been crawled over the years to collect and
analyze disaster-related multimedia content. Satellite imagery has also been
widely explored for disasters analysis. In this paper, we survey the existing
literature on disaster detection and analysis of the retrieved information from
social media and satellites. Literature on disaster detection and analysis of
related multimedia content on the basis of the nature of the content can be
categorized into three groups, namely (i) disaster detection in text; (ii)
analysis of disaster-related visual content from social media; and (iii)
disaster detection in satellite imagery. We extensively review different
approaches proposed in these three domains. Furthermore, we also review
benchmarking datasets available for the evaluation of disaster detection
frameworks. Moreover, we provide a detailed discussion on the insights obtained
from the literature review, and identify future trends and challenges, which
will provide an important starting point for the researchers in the field.

本文通过对社交媒体和卫星影像等灾害相关多媒体内容的回收、整理和分析，总结了灾害检测和多媒体内容分析的已有研究成果，包括文本、社交媒体视觉内容和卫星影像三个领域，并提出了未来趋势和面临的挑战。