- Feb 15, 2023 7:51 pm GMT
I just recently moved into my newly built house. As empty nesters, we realized we might as well downsize. So we opted for a tidy 1200 square foot single-story house. However, one of the things we wanted was a big kitchen with lots of cabinet space. The problem was after about a month in our new home, we ended up with three junk drawers. So whenever we discover something we can’t figure out where it should be stored, we put it into one of the junk drawers. Storage was easy, retrieval, not so much. They are loaded with pens, birthday candles, car keys from cars that have long since been crushed, pencils, and phone chargers, some new, some obsolete.
How many do you have?
I have created a digital version of the junk drawer. Except I don’t have three digital junk drawers. I have hundreds, maybe thousands, clogging up my hard drive. The simple solution is to store these junk file drawers in the cloud. Yet, storage is not the problem. Just like my kitchen junk drawers, retrieval is. The other problem is duplication. I bet I have buried a dozen double-A batteries in at least two of my junk drawers. However, instead of looking for them, I buy another batch of batteries. I will use the two I need and store the rest of the package in one of the junk drawers. I have the same problem with my digital junk drawer.
Imagery Ends Up in the Digital Junk Drawer
Utilities are actively supporting many of their workflows with imagery. Examples include satellite, drones, orthophotos, 3D meshes, video, Lidar, and Phodar. In fact, imagery supports many utilities’ digital twin initiatives. The workflows for this imagery supplement the traditional GIS tasks. Capturing high-resolution photos with drones provides stunning detail of damaged insulators, leaking bushings, and any flaws that helicopter or ground patrols would be hard to find. Utilities leverage pre- and post-storm imagery to facilitate power failures or analyze landslides and fire damage. Imagery often forms the backbone for vegetation management. Utilities add this data to the traditional GIS layers for spectacular analysis.
So, what’s the problem? The imagery data often ends up in the junk drawer. Imagery exists in so many forms. Images cover overlapping areas. Once an image is created, it becomes out of date quickly. Utilities sometimes store the image files on hard drives, DVDs, in files in the cloud, on file servers, you name it.
Image Management to the Rescue
The junk drawer situation with imagery is relatively new due to an explosion of image creation, variety, and time dependency of the images. The solution to my junk drawer problem is to organize all that junk and throw away duplicate and obsolete stuff (like chargers with connectors that have long been replaced). Then pick one drawer and put only what is important in it. That’s what ArcGIS Image Server does with imagery. It is a full system for imagery. It can be implemented on-premise or in the cloud. It includes reality capture, dynamic observations, storage, retrieval, and analysis. It is part of ArcGIS Enterprise, the basis for nearly every utility’s GIS today. A utility with any decent-sized GIS has Enterprise. ArcGIS Image Server provides a distributed computing and storage system that powers the analytical processing and serving of large collections of imagery, elevation data, rasters, and other remotely sensed data. It manages virtually any imagery a utility has today and will likely need in the future.
Here’s the beauty of it, like my junk draw problem, where retrieval is the big issue. ArcGIS Image Server lets users select an area it needs. Then, the image service collects all the images, rasters, orthophotos, and Lidar and shows the users what is contained within the area of interest. It even shows specific images containing specified point locations and assets, like the details of a specific insulator. No more searching through file systems, or worse, rummaging through cardboard boxes of poorly labeled DVDs, looking for the right image to solve my problem.
The Real Bonus
ArcGIS Image Server allows you to assemble, process, analyze, and manage large collections of overlapping, multiresolution imagery and raster data from different sensors, sources, and periods. That’s right. You can piece together all that stuff and perform analysis, like using tools such as machine learning to see patterns you would never be able to figure out, like where little trees can cause big problems. And get this. You can publish your analysis results to web apps. desktops, and mobile devices. So you don’t have to keep this great analysis to yourself. Instead, you can share it with everyone who needs it.
Dump the Junk Drawer
GIS revolutionized the management of utility maps, automated scores of utility workflows, and created a huge variety of analytics. However, until enterprise GIS became common, utility mapping was a mess. Utilities created all kinds of maps, many duplicated, inconsistent, and outdated. Today, GIS again allows utilities to clean up their imagery problem with tools such as ArcGIS Image Server.
For more information on how utilities can use the power of GIS, click here.
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