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Plastic pollution in the sea is an environmental hazard, negatively impacts marine life, and causes economic damage all over the world. It is estimated that each year 8 million tonnes of plastic are deposited in seas, the vast majority coming from rivers. In recent years, publicly available satellite imagery has been used to attempt to track floating plastic debris, using specialized hand-crafted features. In this work, we present an automatic learning approach based on satellite imagery that can detect floating plastic debris in rivers with high precision. This approach is based on well-proven image segmentation architectures, U-Net (RonnebeRger et al., 2015) and DeeplabV3+ (Chen et al., 2018), which we adapt to process high-dimensional multispectral images. To train and test the approach, we also present a dataset of images from different rivers around the world containing floating plastic debris, which is a key step to creating an automated learning solution. We test the predictive accuracy of our network, showing that our approach can correctly identify floating debris in images from regions not seen in the training set. Our results also show that a more extensive labeled dataset is necessary to generalize the approach to some types of rivers. Furthermore, we also demonstrate how our solution can also be used to monitor single areas over time to understand and predict floating debris accumulation.

Single cell profiling yields high dimensional data of very large numbers of cells, posing challenges of visualization and analysis. Here the authors introduce a method for analysis of mass cytometry data that can handle very large datasets and allows their intuitive and hierarchical exploration.

Massive procedural content creation, for example, for virtual urban environments, is a difficult, yet important challenge. While shape grammars are a popular example of effectiveness in architectural modeling, they have clear limitations regarding readability, manageability, and expressive power when addressing a variety of complex structural designs. Moreover, shape grammars aim at geometry specification and do not facilitate integration with other types of content, such as textures or light sources, which could rather accompany the generation process. We present procedural content graphs, a graph-based solution for procedural generation that addresses all these issues in a visual, flexible, and more expressive manner. Besides integrating handling of diverse types of content, this approach introduces collective entity manipulation as lists, seamlessly providing features such as advanced filtering, grouping, merging, ordering, and aggregation, essentially unavailable in shape grammars. Hereby, separated entities can be easily merged or just analyzed together in order to perform a variety of context-based decisions and operations. The advantages of this approach are illustrated via examples of tasks that are either very cumbersome or simply impossible to express with previous grammar approaches.

Accurate maps of surface water are essential for many environmental applications. Surface water maps can be generated by combining measurements from multiple sources. Precise estimation of surface water using satellite imagery remains a challenging task due to the sensor limitations, complex land cover, topography, and atmospheric conditions. As a complementary dataset, in the case of hilly landscapes, a drainage network can be extracted from high-resolution digital elevation models. Additionally, Volunteered Geographic Information (VGI) initiatives, such as OpenStreetMap, can also be used to produce high-resolution surface water masks. In this study, we derive a high-resolution water mask using Landsat 8 imagery and OpenStreetMap as well as (potential) a drainage network using 30 m SRTM. Our approach to derive a surface water mask from Landsat 8 imagery comprises the use of a lower 15% percentile of Landsat 8 Top of Atmosphere (TOA) reflectance from 2013 to 2015. We introduce a new non-parametric unsupervised method based on the Canny edge filter and Otsu thresholding to detect water in flat areas. For hilly areas, the method is extended with an additional supervised classification step used to refine the water mask. We applied the method across the Murray-Darling basin, Australia. Differences between our new Landsat-based water mask and the OpenStreetMap water mask regarding positional differences along the rivers and overall coverage were analyzed. Our results show that about 50% of the OpenStreetMap linear water features can be confirmed using the water mask extracted from Landsat 8 imagery and the drainage network derived from SRTM. We also show that the observed distances between river features derived from OpenStreetMap and Landsat 8 are mostly smaller than 60 m. The differences between the new water mask and SRTM-based linear features and hilly areas are slightly larger (110 m). The overall agreement between OpenStreetMap and Landsat 8 water masks is about 30%.