# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery  
[PERSON]  
1 EcoVision Lab--Photogrammetry and Remote Sensing, ETH Zurich, Ramistrasse 101, 8092 Zurich, Switzerland, [EMAIL_ADDRESS] (R.C.D. );  
[EMAIL_ADDRESS] (J.D.W.) [PERSON]  
1 EcoVision Lab--Photogrammetry and Remote Sensing, ETH Zurich, Ramistrasse 101, 8092 Zurich, Switzerland, [EMAIL_ADDRESS] (R.C.D. );  
[EMAIL_ADDRESS] (S.D. );  
[EMAIL_ADDRESS] (J.D.W.) [PERSON]  
1 EcoVision Lab--Photogrammetry and Remote Sensing, ETH Zurich, Ramistrasse 101, 8092 Zurich, Switzerland, [EMAIL_ADDRESS] (R.C.D. );  
[EMAIL_ADDRESS] (R.C.D. );  
[EMAIL_ADDRESS] (J.D.W.) [PERSON]  
1 EcoVision Lab--Photogrammetry and Remote Sensing, ETH Zurich, Ramistrasse 101, 8092 Zurich, Switzerland, [EMAIL_ADDRESS] (R.C.D. );  
[EMAIL_ADDRESS] (R.C.D. );  
[EMAIL_ADDRESS] (J.D.W.) [PERSON]  
2 Institute for Computational Science, University of Zurich  
mapping of such damage to the canopy is very relevant as it scales with the amount of affected trees, ultimately leading to massive yield loss in the short- to mid-term, at country-scale. To mitigate negative impacts on the local population, rapid response actions are essential, which calls for accurate, large-scale damage maps. In this paper, we propose a machine learning-based, country-scale damage estimation approach and experimentally validate its performance for the impact of typhoon Goni on coconut trees in the Philippines. Timely and accurate mapping of natural hazard impacts is important for a precise and efficient disaster response [1]. Although in situ surveys on the ground can provide reliable data for small-scale events, rapid assessment often becomes infeasible if the affected area is large, particularly if its accessibility is compromised [8]. According to [PERSON] et al., only 40% of the information needed during disaster responses is typically supplied in a timely and pertinent manner [9]. Damage maps based on remote sensing data can reduce the response time while scaling to entire countries, if they can be generated with a high degree of automation. A valuable source of evidence are medium resolution, freely available satellite images captured by satellite constellations with Synthetic Aperture Radar (SAR) such as Sentinel-1; or multi-spectral, optical images like Sentinel-2 and Landsat. It has been argued that SAR images, acquired with active illumination in the microwave spectrum, are the first choice due to their ability to observe the Earth's surface in unfavourable weather conditions and during night time. This robustness against varying atmospheric conditions in principle allows for near-real time change detection, and C-band SAR images have been used for forest density classification [10]. This classification relies on varying back-scatter from different vegetation species. However, SAR-based methods may have limited sensitivity to subtle vegetation changes [11], especially within the same species. In our study we turn to multi-spectral optical satellite imagery, since we aim to detect small degradations in the canopies of coconut palm trees. i.e., we accept some data loss due to clouds in return for a high sensitivity to intricate vegetation changes. Multi-spectral optical satellite images from Sentinel-2 allow for fine-grained analyses of vegetation, including crop classification [12; 13], large scale mapping of tree crops [14; 15], and vegetation height estimation [16]. In previous work we have shown that Sentinel-2 imagery is suitable for mapping oil palms and coconut trees [15; 17]. Here, we build on those works and extend them to a change detection framework that has the ability to quantify subtle variations in the canopy structure of coconut trees. More specifically, we employ a deep learning method to analyse the damage caused by typhoon Goni on coconut trees. First, we apply the active learning method developed in [15] to train a convolutional neural network (CNN) which regresses the number of intact coconut tree canopies in each Sentinel-2 pixel, at 10 m GSD.