Latest Contributions by larryzhang

Hello, all, Have you experianced 'customized' Widget, which is used to Export /Clip a 'georeferanced' image (change image) in Web App from ArcGIS Online / Portal for ArcGIS? The widget of 'Export' should be similar to one that is used in desktop ArcGIS... (i.e., georeferanced image, rather than 'snapshot'). Of course, the image service in Web App (ArcGIS Online /Portal for ArcGIS) is 'exportable', which is configured at GIS server-side. Can you share any hands-on or thoughts? ... View more

Today, data are exponentially growing everywhere, which make it so large and complex that it becomes difficult to handle those effectively through using traditional computing and algorithms, including geospatial data (GIS, remote sensing) and (upstream) big data. Big Data in E&P sector The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. For example, in an oilfiled, thousands of oil production wells are equipped with many sensors that daily capture massive amounts of information about the well flowing conditions. Yearly, tens of TB data are collected and stored into data stores, which are required to effectively process and analyze. Similarly in geospatial domain, when dealing with (hundreds of GB to Terabytes of high-resolution) 'continuous' image data, most CPUs-based algorithms in research and development do not have sufficient computing power to perform traditional image processing tasks in a timely fashion ('on-demand' or real-time), even though the latest development of GIS and remote sensing can demonstrate the capability to deal with real-time 'discrete' event-based data /signal /network, for example, by ArcGIS GeoEvent and ESRI GIS tools for Hadoop. In operation, therefore, the processing power of the typical CPU desktop workstations can become a severe bottleneck in the process of viewing and enhancing high resolution image data, including color-balancing, bundle adjustment, real-time change detection, oil-spills, etc. All those data applications require timely responses for swift decisions, which depend upon real-time /near real-time performance of algorithm analysis and imagery processing (like color balancing) in advanced IT environments (i.e., GPUs cluster computing, 'Cloud' computing, MapReduce-enabled applications). These on-demand systems and applications can greatly benefit from high performance computing techniques and practices to speed up data processing and visulization, either after the data has been collected and transmitted to a ground station on Earth, or during the data collection procedure onboard the sensor, in real-time fashion. Parallel and distributed computing facilities and algorithms as well as high-performance FPGA and DSP systems have become indispensable tools to tackle the issues of processing massive stream GIS, remote sensing, and upstream big data (below). In order to make use of those facilities and algorithms properly, some solution vendores offer the optimal tools like SAS ® Grid Manager SAS Grid Manager | SAS to do so. In recent years, GPUs have evolved into highly parallel many-core processors with tremendous computing power and high memory bandwidth to offer two to three orders of magnitude speedup over the CPUs. A cost-effective GPU computer has become an affordable alternative to an expensive CPU computer cluster for many engineers and researchers performing various engineering and scientific applications. Comparison of Laptop Graphics Cards - NotebookCheck.net Tech In operation, many advanced high-performance computing algorithms of big data with SAS and R have been successfully applying to oil producing data analytics. However, the research and solution development in remote sensing community are still facing the innovative challenges to meet the operational requirements in real-time or MapReduce-enabled applications, especially, like automation of color-balancing and bundle adjustment, in addition to real-time detection of changes & oil spills, etc. In practice, we should use effective tools to monitor /manage /diagnose ‘high availability’ computing performance, especially as a single computing system, like Parallel Processing and (load-balancing) cluster computing. As well know, those high performance architectures are usually performed as a single computing system, including N nodes (CPU-GPU), shared memory, and /or virtual machines, which are obviously performed unlike grid computing and cloud computing. For example, the simplest task is to monitor how good /bad the geoprocessing tools from ArcGIS 10.3 and Pro 1.0 use the multiple cores and processors. +++++++++++++++++ Linux and CUDA-enabled GPU Computing CUDA ® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). Refer to Getting Started Linux :: CUDA Toolkit Documentation Windows and CUDA-enabled GPU Computing Refer to Getting Started Windows :: CUDA Toolkit Documentation WebGL and the implementation of MPEG Dash –a streaming video standard that has been slowly picking up steam among industry players — in IE11 Microsoft (Finally) Confirms WebGL Support For Internet Explorer 11 | TechCrunch ... View more

Dear all, For enterprise-wide ‘Cloud’ GIS solution platform like Google Earth Enterprise (GEE), Skyline Globe Enterprise, and ESRI Portal for ArcGIS Server, have you experienced any hands-on to discuss different advantages/ disadvantages over 3D GIS integration and solution? When using one of those three platforms as an enterprise solution for assets management system, what are the challenges that you are facing? In other word, when integrating and managing your organization’s 3D vector models (including 3D city model, 3D plant models, 3D utility models) in the platform, does it lose any intelligence? Any thoughts to share? ++++++++++++++++ Google Earth Enterprise (GEE) at https://support.google.com/earthenterprise/answer/2716884?hl=en Skyline Globe Enterprise at http://www.skylineglobe.com/SkylineGlobe/WebClient/PresentationLayer/WebClient/3DWebClient.aspx? ESRI Portal for ArcGIS Server (ArcGIS Online) ... View more

In operation, people very commonly think that today powerful computers would help imagery specialists effectively to perform automation of color-balancing and spatial rectification for imagery processing (i.e., without intensively human involvements). Obviously, it is wrong impression,  Including the use of GPUs-based supercomputing workstation, or multi-core /processor CPU-based server machines (over load-balancing cluster or Cloud infrastructure)... In fact, imagery specialists are still facing challenges ‘effectively and accurately’ to process high-resolution (0.31-2.5 m) optical imagery for larger coverage (from hundreds of thousands to millions KM 2 ), which is critical to support operations and many applications (# 1 below). From frontline experiences, in addition to technical challenges, there is major barrier to tell people the limitations of current computer solutions with any latest imagery-processing-related algorithms, no matter how powerful those solution packagess are… Certainly, massively 'manual' adjustment in color and spatial accuracy are highly required, to meet high-standard operation requirements like features' enhancement in mosaic, in addition to color-balanced requirement (#2 & 3 below). Due to spectral variations, there are still some issues like seamlines in some areas (# 4 below). 1. High resolution (0.5-m) satellite imagery mosaic covering the area of over-2-million KM 2 ,  which is well-balanced in color and spatially rectified 2. Overall, visible and near-infra remote sensing technology can be perfectly applied to the areas with limited variations to the landscape, which allows for nice color balancing in the entire region 3. High-level and clear features for feature extraction, mapping, and cartography in well-balanced color mosaic 4. However, some seamlines are still visible among scenes in some areas (around 3-5% of scenes) , due to 'huge' spectral differences, including atmospheric conditions ... View more