# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 2 Related Work  
### Disaster Response  
With every \(1\)\({}^{\circ}\)C increase in the Anthropogenic Climate Change Index (ACCI, a measure for the anthropogenic portion of climate change) the share of strong Category 4-5 events among all hurricanes across the globe increases by 40% [18]. Given the large human and economic cost that such natural disasters cause every year [19], much effort and attention has been put into studying and understanding their effects [20; 21]. Early responses during post-disaster phases are crucial to mitigate short- and mid-term consequences. Governments and non-governmental organisations need to take action quickly, not only in the short term to provide relief materials, health assistance and rescue operations; but also in the mid term to mitigate socioeconomic impacts with suitable relief strategies [22; 23]. A clear understanding of the magnitude of the damage is crucial to focus efforts and provide effective policies [24]. Common sources of information are news, satellite imagery, and social-media reports from affected users. Analysing this vast amount of information in a timely and concise way is challenging [25]. Many automated methods have been proposed to forecast hazards, to assess vulnerabilities, and to track the recovery in affected regions [26; 27]. Notably, several works attempt to extract information about such events from remote sensing data sources [27; 28; 29; 30; 31; 32; 33]. This is especially valuable for rapid mapping tasks, since access to affected regions is often hindered during and immediately after the event. Other works use alternative data sources, such as social media [34], instead of remote sensing data. Yet others also use field surveys to better understand the consequences of natural disasters and to better prepare for future events [35]. While several methods for damage assessment in natural disasters are presented in general, there are also dedicated works that target a single event or type of disaster, such as tsunamis [36], floods [30; 37; 38], earthquakes [39], hurricanes [28; 40], and typhoons [33; 34]. Some studies aim to monitor the evolution of a given region over longer periods of time, usually focusing on measuring the recovery of the considered region [31; 33], while others focus on single instants (e.g., single image analysis) [29]. Most works on damage assessment focus on urban areas, usually attempting to detect damaged buildings [30; 32; 36; 41; 42; 43]. There is a large diversity of data-driven remote sensing methods designed to tackle specific needs after a hazard event. [PERSON] et al. [44] aim to detect objects of interest in the context of search-and-rescue missions after avalanche events, using a convolutional neural network coupled with a support vector machine. For flood detection, [PERSON] et al. [37] use Landsat imagery and compare the performance of different machine learning methods. [PERSON] and [PERSON] [38] provide damage assessments on thousands of aerial images with limited human input. To assist with the development of data-driven approaches for post-disaster building damage evaluation, [PERSON] et al. [43] propose the xBD dataset, a collection that is large enough to train modern, data-hungry deep learning methods. More closely related to our task are data-driven methods to assess damages from tropical storms, i.e., typhoons, cyclones and hurricanes. [PERSON] et al. [33] adopt a random forest algorithm to derive land cover maps from cloud-free composites from Landsat 7 and 8 images. Damage and recovery maps were produced according to the changes observed between different dates. Such approach provides valuable insights on the temporal evolution of the region during and after a natural disaster, although post-classification change analysis is prone to errors especially near region boundaries, and end-to-end classification methods have recently been preferred over random forests [45; 46]. [PERSON] et al.