Disaster Management is one of the most promising research areas because of
its significant economic, environmental and social repercussions. This research
focuses on analyzing different types of data (pre and post satellite images and
twitter data) related to disaster management for in-depth analysis of
location-wise emergency requirements. This research has been divided into two
stages, namely, satellite image analysis and twitter data analysis followed by
integration using location. The first stage involves pre and post disaster
satellite image analysis of the location using multi-class land cover
segmentation technique based on U-Net architecture. The second stage focuses on
mapping the region with essential information about the disaster situation and
immediate requirements for relief operations. The severely affected regions are
demarcated and twitter data is extracted using keywords respective to that
location. The extraction of situational information from a large corpus of raw
tweets adopts Content Word based Tweet Summarization (COWTS) technique. An
integration of these modules using real-time location-based mapping and
frequency analysis technique gathers multi-dimensional information in the
advent of disaster occurrence such as the Kerala and Mississippi floods that
were analyzed and validated as test cases. The novelty of this research lies in
the application of segmented satellite images for disaster relief using
highlighted land cover changes and integration of twitter data by mapping these
region-specific filters for obtaining a complete overview of the disaster.

灾害管理是一个极具潜力的研究领域，因为它对经济、环境和社会产生了重大影响。本研究重点分析与灾害管理相关的各种数据（卫星图像和推特数据），以深入分析按地理位置分组的紧急需求。通过两个阶段的研究，分别是卫星图像分析和推特数据分析，最后利用位置进行整合。利用基于 U-Net 架构的多类土地覆盖分割技术对地点进行灾后卫星图像分析是第一个阶段。第二个阶段侧重于为救援行动提供有关灾情和紧急需求的重要信息的地图绘制。通过使用与该地点相关的关键词，筛选出严重受灾地区，并提取推特数据。通过基于内容词的推特摘要（COWTS）技术从大量原始推特中提取情境信息。将这些模块实时整合，利用基于位置的地图绘制和频率分析技术，在发生灾害的情况下收集多维信息，例如对喀拉拉邦和密西西比州洪水进行了测试和验证。本研究的创新之处在于应用分割的卫星图像进行灾害救援，通过突出土地覆盖变化，并通过地区特定的筛选器整合推特数据，获取灾害的完整概览。

利用卫星图像和社交媒体数据进行自然灾害分析的应急响应情况

Natural Disaster Analysis using Satellite Imagery and Social-Media Data  for Emergency Response Situations

Satellite image analysis has important implications for land use,
urbanization, and ecosystem monitoring. Deep learning methods can facilitate
the analysis of different satellite modalities, such as electro-optical (EO)
and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer
between the modalities to compensate for individual shortcomings. Recent
progress has shown how distributional alignment of neural network embeddings
can produce powerful transfer learning models by employing a sliced Wasserstein
distance (SWD) loss. We analyze how this method can be applied to Sentinel-1
and -2 satellite imagery and develop several extensions toward making it
effective in practice. In an application to few-shot Local Climate Zone (LCZ)
prediction, we show that these networks outperform multiple common baselines on
datasets with a large number of classes. Further, we provide evidence that
instance normalization can significantly stabilize the training process and
that explicitly shaping the embedding space using supervised contrastive
learning can lead to improved performance.

该研究探索了使用深度学习方法和分布对齐的神经网络嵌入可以提高卫星影像分析的准确性，尤其是在少量样本预测中，显著地超越了多种基线方法，其关键词有卫星图像分析，深度学习，迁移学习，Sentinel 和地区气候分区。