Tag Archives: Geo-reference

Understanding Projections: Geo-referencing

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Recently I started working on the year 1959, which is currently the last set of images I have to work on.

1959 was a unique set compared to 1975 and 1995 for a few reasons. The first reason being related to the significant difference in development, which is obvious due to how far back in time the images were taken. The second reason is probably common among Geo-referencing projects, but I have not encountered it until this year. The set was split into two types of images: Geo-referenced or projected images and non-referenced or non-projected images. The projected images are similar to the images I have completed in 1975 and 1995, they were already projected to the desired coordinate system and allowed for immediate Geo-referencing. The non-referenced images are a little more difficult.

These images would gray out the toolbar and not allow for any Geo-referencing. At first I was a bit confused, but quickly started brainstorming ideas. My first idea came in the form of projecting them to the same coordinate system as the data frame. Unfortunately, due to the size of the images, projecting these images was impossible on the machine I had (16 GB of RAM). Nearly instantly the tools would fail. However, I thought this had to be the reason, so I continued to experiment with different ways to project the images. The next was to project to an intermediate coordinate system, then proceed to the more advanced system in hopes of reducing the burden, but that also fizzled out. It seems that no matter what system you are coming from (although I don’t fully understand geographic transformations), it attempts to perform the full process. Running out of ideas, I tried one final solution. I thought that perhaps the size of the images were the reason and decided to slim them down by splitting them into eight sections. After creating a few scripts that would split every image, which went smoothly, it approached the projection part of the script and crashed nearly instantly (although it may have lasted slightly longer).

 

After none of my ideas worked, I decided to restart the computer…and that worked. Even though the images are not the same coordinate system as the data frame, I can still add control points, fit to display, etc. This was a bit frustrating since it was such a simple solution, but the lesson here is that it is best to try the simplest solution before trying the more difficult ideas.

After talking with a few people, the coordinate system should be irrelevant because as soon as you begin Geo-referencing the image should convert to that of the data frame and even that will not matter in the end. Once the images are completed another individual will tether or mosaic them together and assign a coordinate system that way, which will result in the current coordinate system being overwritten.

Geo-referencing: Understanding what you have time for and what you want, Quality VS. Time Spent

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I have currently been Geo-referencing historic imagery for the past 3 months and would like to share my thoughts on some issues regarding project results and time.

As you may know, I am tasked with Geo-referencing historic imagery from the years 95′, 75′, and 59′ to the most recent imagery available to me, 2015. There are 140 images per year and about 105 of them that actually overlay the 2015 imagery. I have never done another Geo-referencing project of this size, so I am not sure if 105 images is a lot, but it seems like a lot. In addition, the images have to meet a certain accuracy standard, which is reasonable and understandable, while also tethering to each other on the edges. Essentially, images and imagery have to perfectly overlap and mosaic. These are all reasonable expectations from someone tasked with Geo-referencing.

However, time must be taken into consideration, especially during a task as tedious and vast as Geo-referencing. To give a little background, I began the tasks by originally putting in a conservative amount of control points (6-12) and lining up the images, however the edges would usually not line up due to camera positioning, sun angle, etc and this resulted in a revision, which is good for myself as I acquired more practice. During my revision, I focused on tethering the images together with a similar amount of control points (6-12), not so much lining them up to the imagery, although they did line up, just not as accurately or precisely as my first run through. This attempt also resulted in a revision, accompanied by a request to tether and accurately match the images to the imagery. This is where my post comes in.

In order to acquire both accurate and tethered imagery, I needed to place many more control points. Common sense would tell you that placing more data would require more time. Control points changed from 6-12 to 14-22, and placement and transformations became more prioritized. Image placement time skyrocketed from 5-10 minutes to 30 minutes – 1 hour. This quickly resulted in a scenario of something that could have been completed in a reasonable amount of time to something that may take months depending on your devotion to such a task.

The question for management becomes: Are we willing to spend such and such on a near perfect product or should we sacrifice some accuracy and quality for time and money. This question depends on what the Geo-referencing projects final use will be. In regards to my project, I am not sure the exact intent, but I think its eventual purpose will be public viewing. If that is the end goal, I think it would be best to skimp on some accuracy as the public does not generally have a professional eye and will not notice the small details such as perfect tethering and/or complete imagery line up.

Geo-referencing: Learning to Save the Hard Way (Easy Way for you)

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Introduction:

I began my internship at the office of innovation and technology in Philadelphia tasked with Geo-referencing historic images to the most recent imagery Philadelphia has to offer. In order to ease me into the tougher, later dates, they had me begin in 1995, which is much more similar to the most recent imagery than the 1950’s to the most recent imagery.

If you have Geo-referenced before, then you know it can be quite simple, but obviously depends on how similar the images are. But, you would also know how tedious and monotonous Geo-referencing is. My undergraduate GIS classes delved into Geo-referencing a little bit just to get the student’s feet wet, but because I was not versed in the little nuances Geo-referencing requires, I shot myself in the foot and learned the hard way.

Tip of the Sprint:

In order to effectively Geo-reference I suggest understanding what actually saves your control points and what does not. If you don’t know what control points are, control points are the references you place all over your target image when you attempt to tell the software this is where this specific placement on the target image is on the reference image in order to stitch them together seamlessly.

Some images require little to no control points, while others (in my case) seem to take in the 20 range…and sometimes more. Now imagine not understanding how Geo-referencing tracked your control points and going through an entire package of images (140 in my case) only to realize none of your work actually registered.

Let me be the first to say, you and I will never make that mistake again. To cut to the chase, as far as my current knowledge is concerned, there are two ways to essentially save your control point placements. The first way is to ‘rectify’, which will create a new image with the corrections you made. This option is found in the drop down menu of the Geo-referencing toolbar in ArcMap. However, if you do not wish to create new images, option two may be a better fit. Option two revolves around updating the current image through the function ‘Update Geo-referencing’ within the Geo-referencing toolbar in ArcMap. I should have done a better job of understanding this crucial aspect before spending two days ‘completing’ a large task.

 

Josh