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.

Distributed processing platform for large datasets: satellite imagery usecase For most of the programs, the most significant amount of time is spent on running a CPUintensive component, while retrieving and reading the input or formatting the output only takes an insignificant amount of time, especially if both the input and output are structured data. Thus, most of the computer science literature on distributed systems discuss the optimization of algorithms and frequently treat the IO and input preparation parts of the program execution as a much less important one for overall efficiency. That wellinformed decision of ignoring the IO part when discussing runtime optimization might change to a fault when we consider algorithms running over massive datasets that should also be retrieved at a userxe2x80x99s request. In this paper, we propose and analyze an usercentric processing platform for running algorithms over satellite imagery products, including a review of the important challenges, solutions, and opportunities on extracting valuable results from GIS products. The 4th section of the paper includes two examples of reallife usage of remote sensing for the observation of natural habitats. In the 5th section we include three practical experiments assessing different aspects of the proposed solution.