Introduction:
An orthomosaic is a georeferenced image that is is created by correcting distortion using elevation data and camera information. In general orthromosaics are used for terrain surfaces and elevation data. To create a 3D image from this data, previously taken pictures that included embedded coordinate references, were added into Pix4D. Within this week's lab, two separate locations and their associated aerial images were used to make 3D renderings of the captured landscape.
Study Area:
Although the data was previously collected for the assignment, latitude and longitude was attached to each image taken. From here, it became rather simple to find the area of interest using this data. Thus, figure 12.1 is the resulting study area based on the coordinates collected from the pictures. Originally, the study was conducted on the corner of Mitchell Ave and Hester St, within the City of Eau Claire, on the athletic grounds of South Middle School. As can be seen, the two features captured was the running track, and the adjacent baseball fields.
Methods:
The first step within this project was to download a set of aerial photos into Pix4D, so the images could be mosaicked into one continual image. This process proved to be rather easy. As data was plugged into Pix4D and then waiting for the the resulting composite image (which in all honestly did take quite some time). Once the data finished processing, two separate rasters, a digital surface model (DSM) and a othromosaic, (four in total) were added into ArcMap. To make sure the rasters ran smoothly within ArcMap, a pyramid was developed for each of them.
After this was done, map elements had to be added to the images. Since the images are in fact photos, no projection was attached to the maps. So to find the scale for each map, one of two methods could be conducted. The first was to add a base map, or reference layer. This reference layer, which does have a defined projection, could then be used to calculate scale. The other method was to measure a specific feature within the image, using the sources tab within properties to figure out which unit to use.
Once satisfied with the results, the mosaicked picture for both the baseball fields and the running track were added into ArcScene to create a 3D image of both the captured features. To do so, the Z coordinate or height, was set to "float" and the scale for height was adjusted to "satisfied". Each 3D image was then exported as a TIFF file and added back into ArcMap to finalize the image with specific map elements such as scale, direction/orientation, title, and referencing.
From this process, four final maps were completed.
Results and Discussion:
To better organize this portion of the study, two subgroups will be listed. One for the baseball fields and the other for the running track.
Base Ball Fields-
To calibrate the final image, Pix4D took a total of one hour and three minutes to overlay 131 separate images. The first image that was developed in ArcMap, figure 12.2, is a 2D rendering of the initial pictures taken. The image, has an area of 0.0284 squared kilometers, or 0.011 squared miles (roughly 7.02 acres). To find the resulting scale, which is listed in meters, the distance tool, in ArcMaps was used to calculate the width of the parking lot located in the bottom left corner. The final image is a good depiction of the area surveyed when compared with the underlying basemap of the area. Yet when re-examining figure 12.1 it can be seen that the mosaic is slightly off centered. However, it remains unclear as to whether the base map or the initial data collected was the inaccurate version.
Conclusion:
In conclusion, this project helped with understanding the process needed to create a 3D image from an initial series of images. By georeferencing these overlapped images, a better 3D image can be developed from a simple 2D case study of a specific area.
An orthomosaic is a georeferenced image that is is created by correcting distortion using elevation data and camera information. In general orthromosaics are used for terrain surfaces and elevation data. To create a 3D image from this data, previously taken pictures that included embedded coordinate references, were added into Pix4D. Within this week's lab, two separate locations and their associated aerial images were used to make 3D renderings of the captured landscape.
Study Area:
Although the data was previously collected for the assignment, latitude and longitude was attached to each image taken. From here, it became rather simple to find the area of interest using this data. Thus, figure 12.1 is the resulting study area based on the coordinates collected from the pictures. Originally, the study was conducted on the corner of Mitchell Ave and Hester St, within the City of Eau Claire, on the athletic grounds of South Middle School. As can be seen, the two features captured was the running track, and the adjacent baseball fields.
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| Figure 12.1: Area of interest, located at South Middle School. |
The first step within this project was to download a set of aerial photos into Pix4D, so the images could be mosaicked into one continual image. This process proved to be rather easy. As data was plugged into Pix4D and then waiting for the the resulting composite image (which in all honestly did take quite some time). Once the data finished processing, two separate rasters, a digital surface model (DSM) and a othromosaic, (four in total) were added into ArcMap. To make sure the rasters ran smoothly within ArcMap, a pyramid was developed for each of them.
After this was done, map elements had to be added to the images. Since the images are in fact photos, no projection was attached to the maps. So to find the scale for each map, one of two methods could be conducted. The first was to add a base map, or reference layer. This reference layer, which does have a defined projection, could then be used to calculate scale. The other method was to measure a specific feature within the image, using the sources tab within properties to figure out which unit to use.
Once satisfied with the results, the mosaicked picture for both the baseball fields and the running track were added into ArcScene to create a 3D image of both the captured features. To do so, the Z coordinate or height, was set to "float" and the scale for height was adjusted to "satisfied". Each 3D image was then exported as a TIFF file and added back into ArcMap to finalize the image with specific map elements such as scale, direction/orientation, title, and referencing.
From this process, four final maps were completed.
Results and Discussion:
To better organize this portion of the study, two subgroups will be listed. One for the baseball fields and the other for the running track.
Base Ball Fields-
To calibrate the final image, Pix4D took a total of one hour and three minutes to overlay 131 separate images. The first image that was developed in ArcMap, figure 12.2, is a 2D rendering of the initial pictures taken. The image, has an area of 0.0284 squared kilometers, or 0.011 squared miles (roughly 7.02 acres). To find the resulting scale, which is listed in meters, the distance tool, in ArcMaps was used to calculate the width of the parking lot located in the bottom left corner. The final image is a good depiction of the area surveyed when compared with the underlying basemap of the area. Yet when re-examining figure 12.1 it can be seen that the mosaic is slightly off centered. However, it remains unclear as to whether the base map or the initial data collected was the inaccurate version.
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| Figure 12.2: ArcMap 2D rendering of the baseball fields found at South Middle School. |
The second map created was done first with ArcScene and then completed again through ArcMap. Figure 12.3 is the 3D image created from the orthomosaicked image that was initially created. Since the distance of the parking lot was already calculated, the old scale bar was adjusted to fit the new image.
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| Figure 12.3: ArcScene 3D rendering of the baseball fields found at South Middle School. |
Track-
The running track is a composite image of an initial 80 images captured. To render this image in Pix4D, it took a total of one hour and ten minutes for the first processing to occur. Again the first image that was created from the resulting rasters was done through the use of ArcMap (figure 12.4). The scale was calculated using the distance tool to measure the inside portion of the actual track. When the image was compared to a basemap for reference (figure 12.1) it can be seen that the geolocation was pretty much spot on, and was even used for replacing the top portion of the track, which was cut out of the initial rendering.
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| Figure 12.4: ArcMap 2D rendering of the running track located on the ground of South Middle School. |
The final image created from this study is figure 12.5. Again this was done by first making the 3D image in ArcScene and then adding the exported image back into ArcMap to add the final map elements. However, since the orientation of this image was flipped, extra precautions needed to be made to ensure that the correct direction remained intact.
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Conclusion:
In conclusion, this project helped with understanding the process needed to create a 3D image from an initial series of images. By georeferencing these overlapped images, a better 3D image can be developed from a simple 2D case study of a specific area.
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