Raster Time Series: Learning and Processing

As the amount of remote sensing data is increasing at a high rate, due to great improvements in sensor technology, efficient processing capabilities are of utmost importance. Remote sensing data from satellites is crucial in many scientific domains, like biodiversity and climate research. Because weather and climate are of particular interest for almost all living organisms on earth, the efficient classification of clouds is one of the most important problems. Geostationary satellites such as Meteosat Second Generation (MSG) offer the only possibility to generate long-term cloud data sets with high spatial and temporal resolution. This work, therefore, addresses research problems on efficient and parallel processing of MSG data to enable new applications and insights. First, we address the lack of a suitable processing chain to generate a long-term Fog and Low Stratus (FLS) time series. We present an efficient MSG data processing chain that processes multiple tasks simultaneously, and raster data in parallel using the Open Computing Language (OpenCL). The processing chain delivers a uniform FLS classification that combines day and night approaches in a single method. As a result, it is possible to calculate a year of FLS rasters quite easy. The second topic presents the application of Convolutional Neural Networks (CNN) for cloud classification. Conventional approaches to cloud detection often only classify single pixels and ignore the fact that clouds are highly dynamic and spatially continuous entities. Therefore, we propose a new method based on deep learning. Using a CNN image segmentation architecture, the presented Cloud Segmentation CNN (CS-CNN) classifies all pixels of a scene simultaneously. We show that CS-CNN is capable of processing multispectral satellite data to identify continuous phenomena such as highly dynamic clouds. The proposed approach provides excellent results on MSG satellite data in terms of quality, robustness, and runtime, in comparison to Random Forest (RF), another widely used machine learning method. Finally, we present the processing of raster time series with a system for Visualization, Transformation, and Analysis (VAT) of spatio-temporal data. It enables data-driven research with explorative workflows and uses time as an integral dimension. The combination of various raster and vector data time series enables new applications and insights. We present an application that combines weather information and aircraft trajectories to identify patterns in bad weather situations

Towards an EBV Analyzer based on VAT

The Essential Biodiversity Variables (EBVs) proposed by GEO BON offer great benefits for decision makers and scientists if they are made readily available. Currently, the usage of EBVs is, however, still a challenge. The required data sets are very large, heterogeneous and temporal. This makes the usage in common tools like GIS very cumbersome. We propose the VAT system, a web-based processing engine for spatio-temporal data, as an ideal tool for EBV visualization and analysis. VAT is being developed as part of GFBio, but was designed to work independently from the start. Here, users can access a rich set of environmental layers and combine them with their own, private data. So-called exploratory workflows allow users to process data in an interactive fashion while guaranteeing reproducibility of the result. In cooperation with GEO BON we are working towards using VAT as the future EBV Browser & Analyzer. In our first use case we considered the global forest change EBV that measures the yearly global loss and gain of tree cover. In particular we wanted to investigate the change over time for a given region of interest. This information is highly relevant for decision makers as well as scientists. In our application users can choose between forest loss and forest gain and then specify their region of interest. This is either done by selecting a country from a list, or by drawing a custom polygon on the map. The relevant part of the EBV data set is added to the map as a new layer. In addition, a plot visualizes the aggregated changes over time. On the technical side this requires intersecting the raster data from the EBV with the user-defined polygon. The size of the raster makes it necessary to work with overviews in order to achieve a low-latency computation. For plotting the temporal development, we make use of our interface to R which allows us exploiting its powerful plotting functionality. In our ongoing work we will implement a public EBV catalogue that allows users to search and submit EBVs. Quality indicators will assist users to decide on the suitability of the data for their given use case. The data from the EBV catalogue will be available in the VAT system for scientists. In addition, VAT also offers simplified report views that hide the complexity of the actual computations and are thus suitable for decision makers and even the general public

Analyzing Essential Biodiversity Variables with the VAT System

The Essential Biodiversity Variables (EBVs) are important information sources for scientists and decision makers. They are developed and promoted by the Group on Earth Observations Biodiversity Observation Network (GEO BON) together with the community. EBVs provide an abstraction level between measurements and indicators. This enables access to biodiversity observations and allows different groups of users to detect temporal trends as well as regional deviations. In particular, the analysis of EBVs supports finding countermeasures for current important challenges like biodiversity loss and climate change. A visual assessment is an intuitive way to drive the analysis. As one example, researchers can recognize and interpret the changes of forest cover maps over time. The VAT System, in which VAT is an acronym for visualization, analysis and transformation, is an ideal candidate platform for the creation of such an analytical application. It is a geographical processing system that supports a variety of spatio-temporal data types and allows computations using heterogeneous data. For user interaction, it offers a web-based user interface that is built with state-of-the-art web technology. Users can perform interactive analysis of spatio-temporal data by visualizing data on maps and using various graphs and diagrams that are linked to the user’s area of interest. Furthermore, users are enabled to browse through the temporal dimension of the data using a time slider tool. This provides an easy access to large spatio-temporal data sets. One exemplary use case is the creation of EBV statistics for selected countries or areas. This functionality is provided as an app that is built upon the VAT System. Here, users select EBVs, a time range and a metric, and create temporal charts that display developments over time. The charts are constructed internally by employing R scripts that were created by domain experts. The scripts are executed using VAT’s R connectivity module. Finally, users can export the results to their local computers. An export contains the result itself and additionally, a list of citations of the included EBVs as well as a workflow description of all processing steps for reasons of reproducibility. Such a use case exemplifies the suitability of the VAT System to facilitate the creation of similar projects or applications without the need of programming, using VAT’s modular and flexible components

Fast Cloud Segmentation Using Convolutional Neural Networks

Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels instead of using closely integrated spatial information, ignoring the fact that clouds are highly dynamic, spatially continuous entities. This paper proposes a novel cloud classification method based on deep learning. Relying on a Convolutional Neural Network (CNN) architecture for image segmentation, the presented Cloud Segmentation CNN (CS-CNN), classifies all pixels of a scene simultaneously rather than individually. We show that CS-CNN can successfully process multispectral satellite data to classify continuous phenomena such as highly dynamic clouds. The proposed approach produces excellent results on Meteosat Second Generation (MSG) satellite data in terms of quality, robustness, and runtime compared to other machine learning methods such as random forests. In particular, comparing CS-CNN with the CLAAS-2 cloud mask derived from MSG data shows high accuracy (0.94) and Heidke Skill Score (0.90) values. In contrast to a random forest, CS-CNN produces robust results and is insensitive to challenges created by coast lines and bright (sand) surface areas. Using GPU acceleration, CS-CNN requires only 25 ms of computation time for classification of images of Europe with 508 × 508 pixels