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![]() Gambino’s traditional beachy lightness is present at times on the album, layering ukulele over melancholic electric guitar and a choir in “The Night Me and Your Mama Met.” Gambino creates this vibe instrumentally in “California” with judicious use of bongo drums, maracas, and güiros, but deprives the song of any pleasant airiness with urgent vocals that feel somehow compressed. This lack of lyrical value leaves the album feeling empty, devoid of what helped Childish Gambino carve his niche in the rap industry. In Camp (2011) and Because the Internet (2013), Gambino showed himself to be a highly gifted lyricist, mixing social commentary, ballads, and braggadocio through puns, alliteration, and unflinching willingness to defy traditional rap norms. This shifts the focus onto the instrumentals, which proves to be a wildly mixed bag. While most of the songs are more than three minutes long, they all have repetitive, distorted choruses occasionally punctuated by single-stanza verses. Throughout the song, Gambino dramatically distorts his voice, making the song feel more fit for an ill-conceived Halloween album than awards-season release.Ī noticeable feature of the album is an unexpected lack of lyrics. Gambino’s use of auto-tune in the song is one of the least necessary and poorly executed instances of auto-tune I have heard. “Zombies” is almost a complete mirror image of “Boogieman,” with entertaining funk instrumentals that are bogged down by grating vocals and uninspired lyrics. Gambino’s approach to these ideas through an extended metaphor is refreshing and interesting, but instrumentally the song is jarring, confusing, and hard to listen to. “Boogieman” addresses themes of police bias and brutality, both highly relevant themes that musicians like Kendrick Lamar, A Tribe Called Quest, and Beyoncé have confronted. Where the singles took the best parts of these things and brought them to exciting and compelling new heights, much of the album paired them with messy production, indecipherable vocals, and mystifying songwriting to draw these ideas to unexpected lows. ![]() The album is experimental, funk and R&B inspired, and entirely devoid of rapping. Gambino keeps a lot of what built up the initial excitement for the singles. These expectations changed drastically through the album’s 49 minutes. The anticipation for the album was fervent, and expectations were high. ![]() ![]() Both of these songs are catchy, inventive, and retro departures for Gambino, and seemed to signal a sea change in the musician’s style for his upcoming album. Gambino’s voice is distorted beyond recognition, to a falsetto reminiscent of Prince, that bounces over compelling funk instrumentals. The second song, “Redbone,” was released in a similar way and garnered similar excitement. “Me and Your Mama” is unlike anything Childish has released, featuring a full choir, a mixture of synths and full-fledged electric guitar, and a conspicuous lack of rapping by the famously articulate and clever emcee. The first single from Childish Gambino’s latest album “Awaken, My Love!” was released with little fanfare: a Youtube video of an eerie, gleeful, blue face, accompanied by a song entitled “Me and Your Mama.” However, the buzz around the song immediately snowballed beyond its understated release. ![]() After each area of reflection and each rocky area have unique values, they can be treated separately.įigure 3: Results of the Region Group tool. The Region Group tool reclassifies the image by grouping adjacent pixels with a common value together. This is largely due to the pixel values of the reflections in the water resembling those of the highly reflective rocks on land. While some classes are now clearly either land or water, many are still found in both regions. Separate the Feature from the Surrounding Area Majority filter and boundary clean tools.įigure 2: The classified imagery after use of generalization tools and reclassification. Isolates more stray pixels which can now be eliminated by re-running the The remainingĬlasses should be left separate until later. Reclassified together, as can those which are all water. I found that by the fourth run there were no longer any significant improvements being made to the image, so I then ran the Boundary Clean tool to tidy up the few remaining stray pixels.īy this point some of the classes are exclusively either water or land. The image is quite pixelated, even though only five classes were used, but running the Majority Filter tool a few times eliminated many of the smaller clusters of pixels. It is usually best to use the lowest number possible, which will save you some extra work later on. Some images may require more, others fewer. This image required five classes to adequately capture the major land/ocean difference. I have chosen to use Iso Cluster Unsupervised classification (new at 10.0) because the value of the extra control offered by a supervised classification is outweighed by the time required to designate the training areas.įigure 1: The results of a 5-class Iso Cluster Unsupervised classification In this case, where the objective is to classify the entire image as either ‘island’ or ‘ocean’, either supervised or unsupervised classification could be used to distinguish between the island and the ocean. A more detailed explanation of image classification and the differences between supervised and unsupervised classification can be found in the help topic What is image classification. Unsupervised classifications don’t require the designation of training areas and are therefore quicker and easier to perform, but there is much less control over how the pixel values are grouped. ![]() The training areas used in a supervised classification can be time consuming to create, but they offer significantly more control over the classification process. When the classes are defined by an automatic process without the user’s input, the classification is considered “Unsupervised”. When the classes are user-defined, usually by using a series of training areas, the classification is considered “Supervised”. Image classification, in its most general form, is the process of sorting or arranging pixels in an image into classes or clusters. If the feature is small and simple, digitizing it by hand may be an option, but I prefer to let ArcGIS do the heavy work whenever possible. ![]() Here I will create a polygon which will eventually serve as a mask to isolate the island.Įxtracting a single feature from the surrounding image while preserving the edge detail can be a daunting task. By removing the area covered by water from the image, I was able to focus the classification on just the vegetation itself. The ability to create an accurate classification of the entire image is complicated by the effect of sunlight reflecting off the waves in the ocean. The first two steps in this process, georeferencing the images and mosaicking them together, were covered in the first and second posts. The final goal of the project was to produce a detailed classification of the island’s vegetation from a series of digital aerial photographs. These images are from a project I recently completed looking at the structure of a seabird colony off the coast of Nova Scotia, Canada, and are representative of the less-than-ideal imagery many of us have to work with regularly. This is the third in a series of blog posts that will cover some tips and tricks for performing the following operations on a series of aerial images using ArcGIS 10.0: |
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