Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

The CoFly-WeedDB contains 201 RGB images (∼436 MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The 1280 × 720 RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to –87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5 m and 3 m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.

Oil spill pollution comprises a significant threat of the oceanic and coastal ecosystems. A continuous monitoring framework with automatic detection capabilities could be valuable as an early warning system so as to minimize the response time of the authorities and prevent any environmental disaster. The usage of Synthetic Aperture Radar (SAR) data acquired from satellites have received a considerable attention in remote sensing and image analysis applications for disaster management, due to the wide area coverage and the all-weather capabilities. Over the past few years, multiple solutions have been proposed to identify oil spills over the sea surface by processing SAR images. In addition, deep convolutional neural networks (DCNN) have shown remarkable results in a wide variety of image analysis applications and could be deployed to overcome the performance of previously proposed methods. This paper describes the development of an image analysis approach utilizing the benefits of a deep CNN combined with SAR imagery to establish an early warning system for oil spill pollution identification. SAR images are semantically segmented into multiple areas of interest including oil spill, look-alikes, land areas, sea surface and ships. The model was trained and tested using multiple SAR images, acquired from the Copernicus Open Access Hub and manually annotated. The dataset is a result of Sentinel-1 missions and EMSA records for relative pollution events. The conducted experiments demonstrate that the deployed DCNN model can accurately discriminate oil spills from other instances providing the relevant authorities a valuable tool to manage the upcoming disaster effectively.