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Abstract. As part of AMADEE-20, an integrated Mars analog field mission in the Negev Desert in Israel conducted by the Austrian Space Forum, an exploration cascade for the remote sensing of extraterrestrial terrain was implemented. For this purpose, aerial robots were conceptualized, which were used in an iterative process to generate a navigational map for an autonomous ground vehicle. This work presents the process for generating navigation maps using multiple aerial image sources from satellites as well as from low orbiting aerial vehicles. First, Deep Learning methods are used to analyze a high altitude aerial images of a large area, creating a basis map for mission planning and navigation. Second, high resolution unmanned aerial vehicle (UAV) images were recorded on low altitude for a pre-defined area of interest, processed with Deep-Learning and Structure from Motion and used to update the basis map. This approach results in a high accuracy navigation map for autonomous, off-road robot navigation. Experiments during the AMADEE-20 mission in the Israeli Negev Desert validated the proposed methods by sending an autonomous ground vehicle through the environment using the generated map.

Decision makers in the tourism sector deal with various issues and need high-quality information to support their decisions. We propose a data-centric approach that analyses historical point of interest (POI) check-in data to determine parameters for an Agent Based Model (ABM). ABM simulation is then run multiple times to simulate possible outcomes in terms of the tourist flow. We have tested the proposed approach on the city of Salzburg using check-in data from Salzburg Card users across 29 POIs. These data were used to parameterize the ABM model with the number of people, the number of POIs a person visits per day, and the preference for selecting POIs to visit. The simulation was performed in GAMA ABM platform and the spatial environment was based on buildings and roads from OpenStreetMap (OSM). Simulation for the duration of 1 day has been repeated 50 times to generate POI visiting patterns. The simulation results have been compared to the ground truth data for the same day and they show that the approach can recreate the long-term pattern of POI visits, but has over-estimated several POIs that had lower visitor counts on that specific day.

<p><strong>Abstract.</strong> Traveling is a basic part of our daily life, whenever a person wants to travel e.g. from home to workplace, the essential question that rises is which route to follow. The choice of a route also varies based on traveler’s interest e.g. visiting hospital on way back to home or traveling on a greener route. This varied route planning may be easy for any person in his local neighborhood, however in a new neighborhood and increasing number of options e.g. possible restaurant options to visit, a guiding system is required that suggests an optimal route according to traveler’s interests i.e. answering semantic queries. Most of the existing routing engines only answer geometric queries e.g. shortest route due to lack of data semantics and adding semantics to a routing graph requires a semantic data source. Geo-semantics can be added through combination of GIS and semantic web. Semantic web is an extension of World Wide Web (WWW) where the content is maintained and structured in a standard way that is understandable by machines; hence providing linked data as a way for semantic enrichment, in this study the semantic enrichment of routing dataset. To use this semantically enriched routing network a routing application needs to be developed that can answer the semantic queries. This research serves as a proof of concept for how linked data can be used for semantic enrichment of routing networks and proposes a prototype routing framework and application designed using open source technologies along with use cases where semantic routing queries are addressed. It also highlights the challenges of this approach and future research perspectives.</p>