GIS Colloquium – Talks (Winter Term 2017/2018)

Automatic Reconstruction of Buildings with Complex Roof Shapes

Andreas Wichmann
Mon, October 23, 2.15 pm (Venue: INF 348, Room 015)

Digital 3D city models are of crucial importance in many applications such as urban and regional planning and enable in the environmental field precise analyses and simulations of pollutant, flood, and noise propagation. Their manual reconstruction provides good results, but is usually very time-consuming and expensive. In order to overcome this issue, the development of automatic reconstruction approaches for the time-efficient and cost-effective generation of 3D building models has become of great interest in recent years. In this talk, a fully automatic building reconstruction approach will be presented which uses building points of an aerial LiDAR data set. The approach is characterized by a strong integration of building knowledge, which is automatically derived during the reconstruction through the application of a graph grammar. It utilizes half-space modeling techniques for the construction of 3D building models to ensure their topological correctness. The resulting building models feature many details and provide in addition to the geometric information also semantic information if required. Thus, they are well suited for different applications. The talk will conclude with a brief overview of related research activities of the speaker.

State of the Art of Event Detection from Geo-tagged Twitter Data

Diao Lin
Mon, November 27, 2.15 pm (Venue: INF 348, Room 015)

The speaker tries to give a structured and comprehensive overview of event detection from geo-tagged twitter data and present some open questions. Precisely, it starts with an introduction of the basic conceptions to answer questions like: what is event and event detection in social media, then an overall workflow of event detection will be given. Based on existed techniques applied in recent papers, three different approaches of event detection focus on: clustering driven approaches, anomaly detection driven approaches, and topic modelling driven approaches will be analysed in terms of their algorithms and advantages and disadvantages. The talk ends with some open questions regarding the research challenges (e.g. scale problem), and trends (e.g. multi-source event detection) in the field of event detection from the perspective of GIScience.

Geospatial Visual Analytics Applications for Predictive Analysis

Dr. Alexandra Diehl
Mon, December 11, 2.15 pm (Venue: INF 348, Room 015)

In this talk, I will introduce my work in the area of Visual Analytics and Predictive Analytics with applications in Weather Forecasting and, more recently, in Social Media. When Predictive Analytics reaches its limits, Visual Analytics can help to recalibrate, change, and optimize models, and to validate results. Putting the user in the loop – the main goal of Visual Analytics – allows the analysts to inspect internal parts of predictive algorithms, such as regression models, and optimize them for a better fitting. I will present several design studies and contributions to this research area in the form of Multiple Coordinated View System and Visual Analytics workflows. My main goal for the ongoing and future projects is to overcome the limitations of automated Predictive Models using the experience and knowledge of the user.

Travel History: Reconstructing Travelers Semantic Trajectories Based on Heterogeneous Social Footprints

Amon Veiga Santana
Mon, December 18, 2.15 pm (Venue: INF 348, Room 015)

Travel specialized services on the web have increased their sociability and usage by adopting mechanisms that facilitates content sharing in real time between users. These web applications, however, lack tools that allow travelers to share their experiences, such as places they have visited, itineraries they have performed, and other activities of a typical touristic trip. These inds of information, when available, are insufficient and incomplete. The process of generating structured and semantic rich datasets based on recommended trips, routes and destinations usually requires high effort to be generated. This task is frequently manual, cumbersome, inaccurate, time-consuming, and depends on user’s willingness to cooperate. This work proposes a solution for reconstructing travel histories using heterogeneous social sources, such as posts in social networks, GPS positioning data, location history data generated by cloud services or any digital footprint with an associated geographic position. The solution encompasses a conceptual model; a methodology to reconstruct travel histories based on heterogeneous social tracks sources; and an application to present the reconstructed travel itinerary in a graphical and interactive fashion. An experiment conducted with real travelers showed that the proposed solution is a reasonable way to reconstruct semantic-rich travel histories in an automatic fashion.

Datacubes: A Step Towards Analysis-Ready Big Data

Prof. Dr. Peter Baumann
Fri, January 19, 10.15 pm (Venue: INF 348, Room 013)

Datacubes form an enabling paradigm for serving massive spatio-temporal Earth data in an analysis-ready way by combining individual files into single, homogenized objects for easy access, extraction, analysis, and fusion - "one cube says more than a million images". In common terms, goal is to allow users to "ask any question, any time, on any size" thereby enabling them to "build their own product on the go". Today, large-scale datacubes are becoming reality: For server-side evaluation of datacube requests, a bundle of enabling techniques is known which can massively speed up response times, including adaptive partitioning, parallel and distributed processing, dynamic orchestration of mixed hardware, and even federations of data centers. Known datacube services exceed 600 TB, and datacube analytics queries have been split across 1,000+ cloud nodes. Intercontinental datacube fusion has been accomplished between ECMWF/UK and NCI Australia, as well as between ESA and NASA. From a standards perspective, as per ISO and OGC, datacubes belong to the family of coverages, aka "spatio-temporally varying objects". the coverage data model is represented by the OGC Coverage Implementation Schema (CIS) standard, the service model by OGC Web Coverage Service (WCS) together with its OGC Web Coverage Processing Service (WCPS), OGC's geo datacube query language. Additionally, ISO is finalizing application-independent query support for massive multi-dimensional arrays in SQL. In our talk we present the concept of datacubes, the standards that play a role, as well as interoperability successes and issues existing, based on our work on the OGC Reference Implementation, rasdaman.

Context-Aware Movement Analysis: An Application to Similarity Search of Trajectories

Dr. Mohammad Sharif
Mon, January 22, 2.15 pm (Venue: INF 348, Room 015)

Studying movement in geographic information science (GIScience) has received attention in recent years because it plays a crucial role in understanding and modeling various spatial activities and processes. In reality, movement of an object is embedded in context and is highly affected by both internal and external contexts. The former is any factor that is related to the object’s characteristic, state, and condition, while the latter is dedicated to the environmental conditions during the move. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. Among the potential movement analysis research, studying moving point objects (MPOs) and measuring the similarities between their trajectories have been of interest recently because it can be the basis for understanding objects’ behaviors, extracting their movement patterns, and predicting their future movement trends. Despite such importance, less attention has been paid to contextualizing similarity search of trajectories, so far. In this research, after providing a new definition and a taxonomy for context in movement analysis, a series of distance functions have been developed for assessing the similarities of trajectories, by including not only the spatial footprints of MPOs but also a notion of their internal and external contexts. In other words, the degree of similarity between two trajectories not only is related to the spatial and temporal closeness of trajectories but also is highly associated with the commonalities in the contexts that they share. The effectiveness of the developed methods have been examined in several experiments on real datasets, i.e., commercial airplanes’, pedestrians’, and cyclists’ trajectories, in separate study areas, while accounting the internal and external context information during the movement. The results of these implementations demonstrate the significance of incorporating contextual information in movement studies, as movement is highly affected by context in both positive and negative manners.

Delineating and describing landscapes and landform elements: challenges and novel methodological approaches

Prof. Dr. Ross Purves
Mon, January 29, 2.15 pm (Venue: INF 348, Room 015)

Representation of landscape in information systems, enabling their analysis and integration in decision making processes, is a basic requirement if political and legal tools such as the European Landscape Convention are to be effective. However, landscape is not directly measureable, and has multiple properties which make its representation challenging. In my talk I will describe some of the limitations of current representations, and compare these to some theoretical notions about landscape drawn from GIScience and linguistics. I will illustrate a number of methods aimed at both delineating and describing landscapes in ways which consider spatial and semantic vagueness, and suggest possible applications of such methods.

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