Mobilecloud data processing system on digital images The quality of measurements in modern photogrammetric complexes essentially depends on the selected scale of the survey, the location of the imaging cameras, the objectxe2x80x99s own shape, and also the limitations imposed by the survey conditions. Therefore, to ensure the required quality metrics in each specific case, it is necessary to solve the task of selecting the number, parameters and location of cameras for a given class of objects. The reason for the limited possibilities lies in the rigid fixing of the bundle of technical devices and the rules for processing images from them. At the same time, the creation of photogrammetric complexes by default implies the availability of specialized software, and the development of methods for computer image processing, the performance of graphics processors and personal devices (laptops, tablets, etc.) makes it possible to expand the functionality of this provision. Existing program components of photogrammetric complexes represent a bunch of means of preliminary processing, pattern recognition and solving countable problems in their various variations. Despite the existence of such a basic structure, software packages are built heuristically, and the set and sequence of interaction of the facilities are static. The article proposes a mobile cloud system that allows minimizing hardware requirements by implementing photogrammetry algorithms for image processing and analysis in the form of cloud services.

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.