# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 1 Introduction  
Marine oil spills have been shown to cause significant environmental degradation, impacting ecosystems, fisheries, and the tourism industry. These effects, in turn, can lead to a wide range of economic, biological, and social consequences. Hence, it is crucial to detect marine oil spills rapidly and disseminate up-to-date information to minimize the damage. Given the challenges of accessing marine accidents and disasters from land, as well as the potential for these incidents to cover extensive areas, the use of remote sensing technologies--including satellites, aircraft, and unmanned aerial vehicles (UAVs)--offers distinct advantages. Satellite technology is particularly effective, on par with aircraft and UAVs, in detecting marine oil spills due to its capacity for rapid monitoring of vast maritime areas [1]. Several organizations, including the United States Coast Guard, the National Oceanic and Atmospheric Administration (NOAA), and the European Maritime Safety Agency (EMSA), utilize satellite-based oil spill detection. Synthetic aperture radar (SAR) images have traditionally been the primary tool for marine oil spill detection due to their ability to operate in all weather conditions. However, distinguishing oil spills in SAR images remains a significant challenge when ocean winds, internal waves, and biogenicfilms are present [2; 3]. Recently, deep learning technologies for image recognition from satellite images have been actively studied, offering high performance and accuracy in detecting oil spills. Prior research on the satellite-based detection of marine oil spills has predominantly employed synthetic aperture radar (SAR) images. To date, studies on oil spill detection using optical satellite images are scarce. When using optical sensors, the satellite images are typically of medium and low resolution. [PERSON] et al. (2014) used Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Landsat 7/8 images to investigate the feasibility of detecting oil spills [4]. [PERSON] et al. (2016) aimed to enhance the precision of oil spill detection by integrating SAR and optical images, including Envisat Advanced Synthetic Aperture Radar (ASAR), Sentinel-1 SAR, and Landsat 5/7/8 [5]. In a related study, [PERSON] et al. (2018) applied the Object-Based Image Analysis (OBIA) method using Sentinel-2 optical images to examine an object-based oil spill analysis [6]. [PERSON] and [PERSON] (2018) utilized Sentinel-1 SAR and Landsat 8 images [7], while [PERSON] et al. (2021) employed a combination of Sentinel-1 and Sentinel-2 images, for oil spill detection [8]. Additionally, unmanned aerial vehicles (UAVs) have been increasingly used for oil spill detection in recent years due to their ability to capture high-resolution images. The studies by [PERSON] et al. (2016) and [PERSON] et al. (2020) demonstrate how UAV imagery facilitates the differentiation between oil and similar materials [5; 9]. [PERSON] et al. (2014) employed a UAV constellation for oil spill detection modeling [10], and [PERSON] et al. (2019) conducted research into mapping coastal oil spills using a UAV team [11]. The advent of deep learning technology has been propelled by the accumulation of datasets and concurrent advancements in computing capabilities. [PERSON] et al. (2007) and [PERSON] et al. (2013) applied deep learning techniques to differentiate between dark spots and oil spills [12; 13]. [PERSON] et al. (2019) employed a variety of deep learning models, including U-Net, LinkNet, the Pyramid Scene Parsing Network (PSPNet), and three variants of the DeepLab model, to evaluate the efficacy and efficiency of oil spill detection [14]. [PERSON] et al. (2020) developed a novel oil spill detection technique based on the Mask Regional Convolutional Neural Network (Mask R-CNN), an instance segmentation model [15]. [PERSON] et al. (2019) also utilized deep learning for the detection of oil spills from UAV images [16]. Additionally, [PERSON] et al.