Improving Information Sharing and Collaborative Analysis for Remote GeoSpatial Visualization Using Mixed Reality Remote collaboration systems allow users at different sites to perform joint tasks, which are required by many reallife applications. For example, environmental pollution is a complex problem requiring many kinds of expertise to fully understand, as pollutants disperse not only locally but also regionally or even globally. This paper presents a remote collaborative visualization system through providing copresence, information sharing, and collaborative analysis functions based on mixed reality techniques. We start with developing an immersive visualization approach for analyzing multiattribute and geospatial data with intuitive multimodel interactions, simulating colocated collaboration effects. We then go beyond by designing a set of information sharing and collaborative analysis functions to support different users to share and analyze their sensemaking processes collaboratively. We provide example results and usage scenario to demonstrate that our system enables users to perform a variety of immersive and collaborative analytics tasks effectively. Through two small user studies focusing on evaluating our design of information sharing and system usability, the evaluation results confirm the effectiveness of comprehensive sharing among user, data, physical, and interaction spaces for improving remote collaborative analysis experience.

Geospatial Computing Collaborations Geospatial algorithm development is a complex collaborative process involving remote sensing scientists with different expertise needing to cooperate on massive and diverse datasets. These datasets can range from satellite imagery to citizen scientist observations, and include both publicly available and proprietary resources. The traditional mode of work is for each participant to have their own desktop machine with large attached storage and to essentially work in a silo. This approach, however, inhibits effective collaboration because individuals must navigate a complex system setup and use adhoc techniques such as sharing a portable hard drive to coproduce data products. The limitation of running on a desktop computer also restricts the scale of questions that can be asked due to excessively long processing times, sometimes spanning several weeks. Another consequence of this process is the lack of data conformance standards, that tend to emerge from shared needs when communities collaborate to advance application frameworks.In this paper, we investigate the social and collaborative aspects of cloud architectures to support geospatial computing. These new architectures provide emergent modes of collaboration. We provide a concrete case study of a typical geospatial collaboration, and detail the build of a collaborative platform as we evolve it from a silo, to a research cluster, and finally to a public cloud. The architectures are evaluated in terms of costs and benefits from systems and software engineering perspectives.