Relevant bibliographies by topics / Geo-processing workflow

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Academic literature on the topic 'Geo-processing workflow'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Geo-processing workflow"

1

Schäffer, Bastian, and Theodor Foerster. "A client for distributed geo-processing and workflow design." Journal of Location Based Services 2, no. 3 (2008): 194–210. http://dx.doi.org/10.1080/17489720802558491.

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Chen, Nengcheng, Liping Di, Genong Yu, and Jianya Gong. "Geo-processing workflow driven wildfire hot pixel detection under sensor web environment." Computers & Geosciences 36, no. 3 (2010): 362–72. http://dx.doi.org/10.1016/j.cageo.2009.06.013.

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Lemmens, R., B. Toxopeus, L. Boerboom, et al. "IMPLEMENTATION OF A COMPREHENSIVE AND EFFECTIVE GEOPROCESSING WORKFLOW ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 123–27. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-123-2018.

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<p><strong>Abstract.</strong> Many projects and research efforts implement geo-information (GI) workflows, ranging from very basic ones to complicated software processing chains. The creation of these workflows normally needs considerable expertise and sharing them is often hampered by undocumented and non-interoperable geoprocessing implementations. We believe that the visual representation of workflows can help in the creation, sharing and understanding of software processing of geodata. In our efforts we aim at bridging abstract and concrete workflow representations for the sake of easing the creation and sharing of simple geoprocessing logic within and across projects.</p><p> We have implemented a first version of our workflow approach in one of our current projects. MARIS, the Mara Rangeland Information System, is being developed in the Mau Mara Serengeti SustainableWater Initiative (MaMaSe). It is a web client that uses the Integrated Land and Water Information System (ILWIS), our open source Remote Sensing and GIS software. It aims to integrate historic, near real time and near future forecast of rainfall, biomass, carrying capacity and livestock market information for the sustainable management of rangelands by conservancies in the Maasai Mara in Kenya. More importantly it aims to show results of a carrying capacity model implemented in a comprehensive geoprocessing workflow.</p><p> In this paper we briefly describe our software and show the workflow implementation strategy and discuss the innovative aspects of our approach as well as our project evaluation and the opportunities for further grounding of our software development.</p>
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Li, Chunlin, Jun Liu, Min Wang, and Youlong Luo. "Fault-tolerant scheduling and data placement for scientific workflow processing in geo-distributed clouds." Journal of Systems and Software 187 (May 2022): 111227. http://dx.doi.org/10.1016/j.jss.2022.111227.

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Chen, Nengcheng, Liping Di, Genong Yu, and Jianya Gong. "Automatic On-Demand Data Feed Service for AutoChem Based on Reusable Geo-Processing Workflow." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3, no. 4 (2010): 418–26. http://dx.doi.org/10.1109/jstars.2010.2049094.

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Chen, Wuhui, Incheon Paik, and Patrick C. K. Hung. "Transformation-Based Streaming Workflow Allocation on Geo-Distributed Datacenters for Streaming Big Data Processing." IEEE Transactions on Services Computing 12, no. 4 (2019): 654–68. http://dx.doi.org/10.1109/tsc.2016.2614297.

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Toschi, I., E. Nocerino, F. Remondino, A. Revolti, G. Soria, and S. Piffer. "GEOSPATIAL DATA PROCESSING FOR 3D CITY MODEL GENERATION, MANAGEMENT AND VISUALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 527–34. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-527-2017.

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Recent developments of 3D technologies and tools have increased availability and relevance of 3D data (from 3D points to complete city models) in the geospatial and geo-information domains. Nevertheless, the potential of 3D data is still underexploited and mainly confined to visualization purposes. Therefore, the major challenge today is to create automatic procedures that make best use of available technologies and data for the benefits and needs of public administrations (PA) and national mapping agencies (NMA) involved in “smart city” applications. The paper aims to demonstrate a step forward in this process by presenting the results of the SENECA project (Smart and SustaiNablE City from Above – <a href="http://seneca.fbk.eu"target="_blank">http://seneca.fbk.eu</a>). State-of-the-art processing solutions are investigated in order to (i) efficiently exploit the photogrammetric workflow (aerial triangulation and dense image matching), (ii) derive topologically and geometrically accurate 3D geo-objects (i.e. building models) at various levels of detail and (iii) link geometries with non-spatial information within a 3D geo-database management system accessible via web-based client. The developed methodology is tested on two case studies, i.e. the cities of Trento (Italy) and Graz (Austria). Both spatial (i.e. nadir and oblique imagery) and non-spatial (i.e. cadastral information and building energy consumptions) data are collected and used as input for the project workflow, starting from 3D geometry capture and modelling in urban scenarios to geometry enrichment and management within a dedicated webGIS platform.
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Iacone, Brooke, Ginger R. H. Allington, and Ryan Engstrom. "A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments." Remote Sensing 14, no. 21 (2022): 5395. http://dx.doi.org/10.3390/rs14215395.

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High-resolution Corona imagery acquired by the United States through spy missions in the 1960s presents an opportunity to gain critical insight into historic land cover conditions and expand the timeline of available data for land cover change analyses, particularly in regions such as Northern China where data from that era are scarce. Corona imagery requires time-intensive pre-processing, and the existing literature lacks the necessary detail required to replicate these processes easily. This is particularly true in landscapes where dynamic physical processes, such as aeolian desertification, reshape topography over time or regions with few persistent features for use in geo-referencing. In this study, we present a workflow for georeferencing Corona imagery in a highly desertified landscape that contained mobile dunes, shifting vegetation cover, and a few reference points. We geo-referenced four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery with an overall accuracy of 11.77 m, and the workflow is documented in sufficient detail for replication in similar environments.
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Lucas, G. "CONSIDERING TIME IN ORTHOPHOTOGRAPHY PRODUCTION: FROM A GENERAL WORKFLOW TO A SHORTENED WORKFLOW FOR A FASTER DISASTER RESPONSE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 249–55. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-249-2015.

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This article overall deals with production time with orthophoto imagery with medium size digital frame camera. The workflow examination follows two main parts: data acquisition and post-processing. The objectives of the research are fourfold: 1/ gathering time references for the most important steps of orthophoto production (it turned out that literature is missing on this topic); these figures are used later for total production time estimation; 2/ identifying levers for reducing orthophoto production time; 3/ building a simplified production workflow for emergency response: less exigent with accuracy and faster; and compare it to a classical workflow; 4/ providing methodical elements for the estimation of production time with a custom project. <br><br> In the data acquisition part a comprehensive review lists and describes all the factors that may affect the acquisition efficiency. Using a simulation with different variables (average line length, time of the turns, flight speed) their effect on acquisition efficiency is quantitatively examined. <br><br> Regarding post-processing, the time references figures were collected from the processing of a 1000 frames case study with 15 cm GSD covering a rectangular area of 447 km<sup>2</sup>; the time required to achieve each step during the production is written down. When several technical options are possible, each one is tested and time documented so as all alternatives are available. Based on a technical choice with the workflow and using the compiled time reference of the elementary steps, a total time is calculated for the post-processing of the 1000 frames. Two scenarios are compared as regards to time and accuracy. The first one follows the “normal” practices, comprising triangulation, orthorectification and advanced mosaicking methods (feature detection, seam line editing and seam applicator); the second is simplified and make compromise over positional accuracy (using direct geo-referencing) and seamlines preparation in order to achieve orthophoto production faster. The shortened workflow reduces the production time by more than three whereas the positional error increases from 1 GSD to 1.5 GSD. The examination of time allocation through the production process shows that it is worth sparing time in the post-processing phase.
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Liakos, Leonidas, and Panos Panagos. "Challenges in the Geo-Processing of Big Soil Spatial Data." Land 11, no. 12 (2022): 2287. http://dx.doi.org/10.3390/land11122287.

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This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using advanced programming languages. Challenges to this study included the reproducibility of the results, their presentation in a communicative way, and the harmonization of complex heterogeneous data in space and time based on high standards of accuracy. Accuracy was especially important as the results needed to be identical at all spatial scales (from point counts to aggregated countrywide data). The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units (national or regional), and river catchments, to the global scale. Advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) were used to convey comprehensive and substantial information that would be of use to policymakers. More specifically, a variety of cartographic practices were employed, including vector and raster visualization and hexagon grid maps at the global or European scale and in several cartographic projections. The information was rendered in both grid format and as aggregated statistics per polygon (zonal statistics), combined with diagrams and an advanced graphical interface. The uncertainty was estimated and the results were validated in order to present the outputs in the most robust way. The study was also interdisciplinary in nature, requiring large-scale datasets to be integrated from different scientific domains, such as soil science, geography, hydrology, chemistry, climate change, and agriculture.
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