Methane emissions from oil and gas infrastructure, wetlands, and livestock contribute to the greenhouse gas inventory. The analysis of satellite short-wave infrared imagery offers opportunities for screening large areas to detect methane leaks. Deep learning algorithms excel at analyzing these data, however, they require large annotated datasets for model calibration that are difficult to get. To overcome this limitation, we explore a methodology to spot methane plumes using deep binary classifiers trained on a large dataset of synthetically created methane plumes, customized for this specific task, using publicly available images of the Sentine1-2 satellites. To build the database, we simulate plume patterns using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and use a simple stochastic model to account for reflectance attenuation due to methane in band 12 centered at 2190 nm. To help distinguish methane plumes from the image background, we compute a methane signature image based on a background subtraction technique. Once calibrated, the classification model is applied to image patches centered in the local minima of the methane signature within the satellite image, scoring a value ranging from 0 to 1 associated with the presence of a methane plume. We compare experimentally the general-purpose ResNet architecture and MethaNet, a domain-specific convolutional neural network, using simulated data. Then, we evaluate the feasibility of our approach in detecting large methane leaks at two study sites located in the Hassi Messaoud oil field in Algeria and the Permian Basin in the US, each covering an area of 0.25$\times$ 0.25 degrees. We found that ResNet is effective in identifying large, known methane plumes that were set aside for testing purposes. This method could be considered as a component of a solution for planning mitigation activities.

Floods are among the most costly weather hazards for societies and businesses globally. With increasing global warming, these events have become even more frequent and more devastating. Thus, accurate flood mapping has become critical for disaster relief, risk management and mitigation. Current flood segmentation methods use either threshold-based approaches or deep-learning schemes, e.g. using the U-Net architecture, to differentiate between water-covered bodies or dry land on Earth observation images. Many schemes are exploiting imagery from synthetic aperture radar (e.g. Sentinel 1 satellites) or visual bands of satellites such as the Sentinel 2, but often restrict themselves to using one or very few modalities, i.e. spectral wavelengths, despite the availability of many more wavelengths or pre-processed indices with potential value to the challenge. In support of operationalizing flood segmentation on a global scale using deep learning, we propose semantic flood segmentation exploiting optionally many different modalities (i.e. multimodal flood segmentation), making the approach largely immune to geographic differences across the globe. Using U-Net at the core of our work, we observe very good generalisation of our segmentation model to unseen flood events in our holdout set at the level of 0.95 F1 Score (0.92 IoU) for both no water and water class, and 0.53 F1 Score (0.43 IoU) for water class, respectively.