Stimulating non-motorized transport has been a key point on sustainable mobility agendas for cities around the world. Lisbon is no exception, as it invests in the implementation of new bike infrastructure. Quantifying the connectivity of such a bicycle network can help evaluate its current state and highlight specific challenges that should be addressed. Therefore, the aim of this study is to develop an exploratory score that allows a quantification of the bicycle network connectivity in Lisbon based on open data. For each part of the city, a score was computed based on how many common destinations (e.g., schools, universities, supermarkets, hospitals) were located within an acceptable biking distance when using only bicycle lanes and roads with low traffic stress for cyclists. Taking a weighted average of these scores resulted in an overall score for the city of Lisbon of only 8.6 out of 100 points. This shows, at a glance, that the city still has a long way to go before achieving their objectives regarding bicycle use in the city.

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes.

Spatio-temporal analysis capabilities of big Earth observation (EO) data are possible now on various infrastructures, but the transferability and interoperability of analyses remain challenging. This contribution describes an approach for interacting with multiple semantic EO data cubes, where for each observation, at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Our in-house developed Web-based graphical user interface (GUI) provides technical access to multiple semantic EO data cubes, regardless of what infrastructure they are implemented on. It is designed to create semantic models using a graphical language, and an inference engine is able to evaluate these models against existing semantic EO data cubes based on a user's defined area and timespan of interest. Querying on a semantic level allows the transferability of semantic models across EO data cubes. Our contribution shows an approach towards solving this open research gap and discusses relevant challenges such as transferability of semantic models, on-demand instantiation, and federated EO data cubes. We believe that this approach offers new opportunities for improved semantic and syntactic interoperability in EO analyses and is better positioned to allow semantically-enabled queries possible in a federated EO data cube context.

<div> <p>The Sen2Cube.at is a Sentinel-2 semantic Earth observation (EO) data and information cube that combines an EO data cube with an AI-based inference engine by integrating a computer-vision approach to infer new information. Our approach uses semantic enrichment of optical images and makes the data and information directly available and accessible for further use within an EO data cube. The architecture is based on an expert system, in which domain-knowledge can be encoded in semantic models (knowledgebase) and applied to the Sentinel-2 data as well as semantically enriched, data-derived information (factbase).  </p> </div><div> <p>The initial semantic enrichment in the Sen2Cube.at system is general-purpose, user- and application-independent, derived directly from optical EO images as an initial step towards a scene classification map. These information layers are automatically generated from Sentinel-2 images with the SIAM software (Satellite Image Automated Mapper). SIAM is a knowledge-based and physical-model-based decision tree that produces a set of information layers in a fully automated process that is applicable worldwide and does not require any samples. A graphical inference engine allows application-specific Web-based semantic querying based on the generic information layer as a replicable and explainable approach to produce information. The graphical inference engine is a new Browser-based graphical user interface (GUI) developed in-house with a semantic querying language. Users formulate semantic models in a graphical way and can execute them on any area-of-interest and time interval, which will be evaluated by the core of the inference engine attached to the data cube. This also enables non-expert users to formulate analyses without requiring programming skills.  </p> </div><div> <p>While the methodology is software-independent, the prototype is based on the Open Data Cube and additional in-house developed components in the Python programming language. Scaling is possible depending on the available infrastructure resources due to the system’s Docker-based container architecture. Through its fully automated semantic enrichment, innovative graphical querying language in the GUI for semantic querying and analysis as well as the implementation as a scalable infrastructure, this approach is suited for big data analysis of Earth observation data. It was successfully scaled to a national data cube for Austria, containing all available Sentinel-2 images from the platforms A and B. </p> </div>