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Tops in 2015

Amanda O'Connor

Well as another year winds down I start thinking about the things that caught my attention remote sensing wise in 2015.

1)     Cloud deployments off and running! With remotely sensed data being so large there seemed to me collective industry doubt about moving applications to the cloud. Will my massive data be able to really be analyzed via an internet connection? Will it break the bank to work with my data on AWS? But I still want control? The barriers I saw broken down on cloud deployments were

a.      Back end processing and preprocessing

b.     Creation of a light weight product, like a report, automatically

c.      Interaction with existing customer databases or data repositories

Ultimately the cloud is ideal for remote sensing data and will enable the really big problems to be answered with data in one place, especially free public data, for people to ask questions about our world and others we never thought possible. For a while it was really difficult for organizations to come to terms with variable monthly cloud computing costs, it’s hard to allocate and predict. But it’s like a utility, you don’t know exactly how much your heat costs each, month, but you have an idea and over time you have a better idea of use. Governments, businesses, and institutions are starting to look at cloud computing in this model and feeling much safer with it as a robust option for data analytics.

2)     I love working with Airbus Defense and Space. I know what you’re saying; “Amanda, you’re shamelessly plugging your business partner". And what do I say? “So what! They sent me a giant chocolate bar.” And that little hammer is going to be used hence forth for all my large chocolate breakages. But in all seriousness, I see a lot more people using their data because of their flexibility on tasking, great archive, and affordability. The Tri-stereo Pleiades data makes a lovely image generated point cloud. They have a really great image offering and it’s only going to get better this year.

3)     Precision Agriculture -- a paradox. Precision agriculture has long been touted as the ideal field for remote sensing and there’s use here and there, but there is also a lot of concerns over expense of data and the margins it can help eek out. This year I saw huge excitement over Planet Labs promise of daily coverage of land masses with their constellation from the precision agriculture community. Coverage within a growing season has always been tough: too spotty and you miss something, too much and well, you have too much data. A drone seemed like the ideal platform for precision agriculture, but it depends on your market. You might not need to get leaf scale in a cornfield, and the price of corn doesn’t support that level of resolution.  And bandwidth is a HUGE issue both domestically and internationally, rural areas (i.e. where there’s a lot of agriculture) don’t have great coverage or internationally, none in places. This community is poised like no other to drive automation, cloud scaling, and more data, more data, more data. With the diversity of crops, landscapes, fertilizer regimes, water use, and land management, there are a gazillion ways remote sensing can help to turn a higher profit for growers, sellers, processers, and suppliers. We are working on a precision agriculture toolkit that will be available in January. Check out this story from precisionag.com on all the cool things happening and how many involve imagery.

4)     Drones. Yep they are everywhere and guess what? They create, really big data that’s really great! But like all wondrous things, there are a couple issues. Working with these massive datasets is not for the faint of RAM. Luckily, ENVI handles these big datasets for mosaicking, color balancing, deriving meaningful information, and a myriad of other tasks.  I see a lot of datasets that look beautiful, but then there are alignment issues either to maps or band to band. Removing artifacts can be complex and time consuming, and the bigness of the data can bring some applications to their knees. But the right software (ENVI, naturally), a good cloud computing platform and a powerful workstation, and some image processing background can get you around these issues. Our services group has been getting a lot of experience working with data that would be great if only “x, y, and z” were corrected.  Whether it’s the photogrammetry piece, the product piece, or preprocessing, we can get your drone data singing.

5)     The sharing economy. One of our other partner’s is CloudEO. They have a great product that lets you use software and data for the time you need to use it as opposed to having to make a large long-term investment. You can get ENVI for a low monthly cost of $400 and have access to Airbus DS data like SPOT and Pleiades, and Landsat and Sentinel, AND you get compute power. You rent the data, tools and space, making one time imagery projects easy to fund, keep ongoing projects going because of a lower investment of infrastructure, and collaborate with the Cloud EO community of users to accelerate your discoveries.  This and Digital Globe’s Geospatial Big Data platform is pushing pixel rental to new heights making it easier to ask big questions of big data. Why keep an image forever on a hard drive if you only need to ask it one thing?

So it’s 4:30 on New Year’s Eve and I’m the only person left here, so it's probably time to call it a wrap. It’s time for 2016 and new fun discoveries. I’ll be at AMS in January and ILMF in February. Stop by the Harris booth and tell me what’s getting you excited in remote sensing this year.

Follow me @asoconnor

 

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