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+---
+title: Generating Realistic Satellite Imagery with Deep Neural Networks
+layout: post
+---
+
+I've been doing a lot of experimenting with [neural-style](https://github.com/jcjohnson/neural-style)
+the last month. I think I've discovered a few exciting applications of the
+technique that I haven't seen anyone else do yet. The true power of this
+algorithm really shines when you can see concrete examples.
+
+Skip to the **Applications** part of this post to see the outputs from my
+experimentation if you are already familiar with DeepDream, Deep Style, and all
+the other latest research in generating images with deep neural networks.
+
+###Background and History###
+
+On [May 18, 2015 at 2 a.m., Alexander
+Mordvintsev](https://medium.com/backchannel/inside-deep-dreams-how-google-made-its-computers-go-crazy-83b9d24e66df#.g4t69y8wy),
+an engineer at Google, did something with deep neural networks that no one had
+done before. He took a net designed for *recognizing* objects in images and used
+it to *generate* objects in images. In a sense, he was telling these systems
+that mimic the human visual cortex to hallucinate things that weren't really
+there. The [results](https://i.imgur.com/6ocuQsZ.jpg) looked remarkably like LSD
+trips or what a [schizophrenic person sees on a blank
+wall](https://www.reddit.com/r/deepdream/comments/3cewgn/an_artist_suffering_from_schizophrenia_was_told/).
+
+Mordvintsev's discovery quickly gathered attention at Google once he posted
+images from his experimentation on the company's internal network. On June 17,
+2015, [Google posted a blog post about the
+technique](http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html)
+(dubbed "Inceptionism") and how it was useful for opening up the notoriously
+black-boxed neural networks using visualizations that researchers could examine.
+These machine hallucinations were key for identifying the features of objects
+that neural networks used to tell one object from another (like a dog from a
+cat). But the post also revealed the [beautiful
+results](https://goo.gl/photos/fFcivHZ2CDhqCkZdA) of applying the algorithm
+iteratively on it's own outputs and zooming out at each step.
+
+The internet exploded in response to this post. And once [Google posted the code
+for performing the
+technique](http://googleresearch.blogspot.com/2015/07/deepdream-code-example-for-visualizing.html?m=1),
+people began experimenting and sharing [their fantastic and creepy
+images](https://www.reddit.com/r/deepdream) with the world.
+
+Then, on August, 26, 2015, a paper titled ["A Neural Algorithm of Artistic
+Style"](http://arxiv.org/abs/1508.06576) was published. It showed how one could
+identify which layers of deep neural networks recognized stylistic information
+of an image (and not the content) and then use this stylistic information in
+Google's Inceptionism technique to paint other images in the style of any
+artist. A [few](https://github.com/jcjohnson/neural-style)
+[implementations](https://github.com/kaishengtai/neuralart) of the paper were
+put up on Github. This exploded the internet again in a frenzy. This time, the
+images produced were less like psychedelic-induced nightmares but more like the
+next generation of Instagram filters ([reddit
+how-to](https://www.reddit.com/r/deepdream/comments/3jwl76/how_anyone_can_create_deep_style_images/)). 
+
+People began to wonder [what all of this
+meant](http://www.hopesandfears.com/hopes/culture/is-this-art/215039-deep-dream-google-art)
+to [the future of
+art](http://kajsotala.fi/2015/07/deepdream-today-psychedelic-images-tomorrow-unemployed-artists/).
+Some of the results produced where [indistinguishable from the style of dead
+artists'
+works](https://raw.githubusercontent.com/jcjohnson/neural-style/master/examples/outputs/tubingen_starry.png).
+Was this a demonstration of creativity in computers or just a neat trick?
+
+On November, 19, 2015, [another paper](http://arxiv.org/abs/1511.06434) was
+released that demonstrated a technique for generating scenes from convolutional
+neural nets ([implementation on Github](https://github.com/Newmu/dcgan_code)).
+The program could generate random (and very realistic) [bedroom
+images](https://github.com/Newmu/dcgan_code/raw/master/images/lsun_bedrooms_five_epoch_samples.png)
+from a neural net trained on bedroom images. Amazingly, it could also generate
+[the same bedroom from any
+angle](https://github.com/Newmu/dcgan_code/blob/master/images/lsun_bedrooms_five_epochs_interps.png).
+It could also [produce images of the same procedurally generated face from any
+angle](https://github.com/Newmu/dcgan_code/blob/master/images/turn_vector.png).
+Theoretically, we could use this technology to create *procedurally generated
+game art*.
+
+The main thing holding this technology back from revolutionizing procedurally
+generated video games is that it is not real-time. Using
+[neural-style](https://github.com/jcjohnson/neural-style) to apply artistic
+style to a 512 by 512 pixel content image could take minutes even on the
+top-of-the-line GTX Titan X graphics card. Still, I believe this technology has
+a lot of potential for generating game art even if it can't act as a real-time
+filter.
+
+###Applications: Generating Satellite Images for Procedural World Maps###
+
+I personally know very little machine learning, but I have been able to produce
+a lot of interesting results by using the tool provided by
+[neural-style](https://github.com/jcjohnson/neural-style).
+
+Inspired by [Kaelan's procedurally generated world
+maps](http://blog.kaelan.org/randomly-generated-world-map/), I wanted to extend
+the idea by generating realistic satellite images of the terrain maps. The
+procedure is simple: take a [generated terrain map](/assets/kaelan_terrain1.png)
+and apply the style of a [real-world satellite image](/assets/uk_satellite.jpg)
+on it using neural-style.
+
+![Output of generated map plus real-world satellite
+imagery](/assets/satellite_terrain1_process.png)
+
+The generated output takes on whatever terrain is in the satellite image. Here
+is an output processing one of Kaelan's maps with a [arctic satellite
+image](/assets/svalbard_satellite.jpg):
+
+![Kaelan's terrain map](/assets/kaelan_terrain2.jpg)
+![Output of terrain map plus arctic satellite imagery](/assets/satellite_terrain2.png)
+
+And again, with one of Kaelan's desert maps and a [satellite image of a
+desert](/assets/desert_satellite.jpg):
+
+![Kaelan's desert terrain map](/assets/kaelan_terrain3.jpg)
+![Output of terrain map plus desert satellite imagery](/assets/satellite_terrain3.png)
+
+It even works with [Kaelan's generated hexagon
+maps](http://blog.kaelan.org/hexagon-world-map-generation/). Here's an island
+hexagon map plus a [satellite image of a volcanic
+island](/assets/volcano_satellite.jpg):
+
+![Kaelan's island hexagon map](/assets/kaelan_hex_terrain.jpg)
+![Output of hexagon map plus island satellite
+imagery](/assets/satellite_hex_terrain.png)
+
+This image even produced an interesting three-dimensional effect because of the
+volcano in the satellite image.
+
+By the way, this also works with minecraft maps. Here's a minecraft map I found
+on the internet plus a [satellite image from Google
+Earth](/assets/river_satellite.png):
+
+![Minecraft map](/assets/minecraft_map.jpg)
+![Output of minecraft map plus river satellite
+imagery](/assets/satellite_minecraft_map.png)
+
+No fancy texture packs or 3-D rendering needed :).
+
+Here is the Fallout 4 grayscale map plus a
+[satellite image of Boston](/assets/boston_aerial.jpg):
+
+![Fallout 4 grayscale map](/assets/fallout4_map.png)
+![Output of Fallout 4 map plus Boston satellite
+imagery](/assets/satellite_fallout4_map.png)
+
+Unfortunately, it puts the built-up dense part of the city in the wrong part of
+the geographic area. But, this is understandable since we gave the algorithm no
+information on where that is on the map.
+
+We can also make the generated terrain maps look like old hand-drawn maps using
+neural-style. With Kaelan's terrain map as the
+content and [the in-game Elder Scrolls IV Oblivion map of
+Cyrodiil](/assets/cyrodiil_ingame.jpg) as the style we get this:
+
+![Kaelan's terrain map](/assets/kaelan_terrain1.png)
+![Output of terrain map plus map of Cyrodiil](/assets/cyrodiil_terrain1.png)
+
+It looks cool, but the water isn't conveyed very clearly (e.g. makes deep water
+look like land). Neural-style seems to work better when there is lots of color
+in both images.
+
+Here is the output of the hex terrain plus satellite map above and the Cyrodiil
+map which looks a little cleaner:
+
+![Satellite-like hex terrain map](/assets/satellite_hex_terrain.png)
+![Output of hex terrain plus satellite and map of
+Cyrodiil](/assets/cyrodiil_satellite_hex_terrain.png)
+
+I was interested to see what neural-style could generate from random noise, so I
+rendered some clouds in GIMP and ran it with a satellite image of [Mexico City
+from Google Earth](/assets/mexico_city.jpg) (by the way, I've been getting high
+quality Google Earth shots from
+[earthview.withgoogle.com](https://earthview.withgoogle.com)).
+
+![Random clouds](/assets/blurry_clouds.png)
+![Output of random clouds and Mexico City](/assets/random_mexico_city.png)
+
+Not bad for a neural net without a degree in urban planning.
+
+I also tried generating on random noise with a satellite image of [a water
+treatment plant in Peru](/assets/treatment_plant.jpg)
+
+![Random clouds](/assets/blurry_clouds2.png)
+![Output of random clouds and water treatment
+plant](/assets/random_treatment_plant.png)
+
+###Applications: More Fun###
+
+For fun, here are some other outputs that I liked.
+
+[My photo of Boston's skyline as the content](/assets/boston_skyline.jpg) and
+[Vincent van Gogh's The Starry Night as the style](/assets/starry_night.jpg):
+
+![Output of Boston skyline and starry night](/assets/starry_boston.png)
+
+[A photo of me](/assets/standing_forest.jpg) (by Aidan Bevacqua) and [Forrest in
+the end of Autumn by Caspar David Friedrich](/assets/forrest_autumn.jpg):
+
+![Output of me and Forrest in the end of
+Autumn](/assets/dead_forest_standing.png)
+
+[Another photo of me by Aidan](/assets/sitting_forest.jpg) in the same style:
+
+![Output of me and Forrest in the end of Autumn](/assets/dead_forest_sitting.png)
+
+[A photo of me on a mountain](/assets/mountain_view.jpg) (by Aidan Bevacqua) and
+[pixel art by Paul Robertson](/assets/pixels.png)
+
+![Output of me on a mountain and pixel art](/assets/mountain_view_pixels.png)
+
+[A photo of a park in Copenhagen I took](/assets/copenhagen_park.jpg) and a
+painting similar in composition, [Avenue of Poplars at Sunset by Vincent van
+Gogh](/assets/avenue_poplars.jpg):
+
+![Output of park in Copenhagen and Avenue of Poplars at
+Sunset](/assets/poplars.png)
+
+[My photo of the Shenandoah National Park](/assets/shenandoah_mountains.jpg) and
+[this halo graphic from GMUNK](/assets/halo_ring_mountains.jpg)
+([GMUNK](http://www.gmunk.com/filter/Interactive/ORA-Summoners-HALO)):
+
+![Output of Shenandoah mountains and halo ring
+mountains](/assets/halo_shenandoah.png)
+
+[A photo of me by Aidan](/assets/me.png) and a [stained glass
+fractal](/assets/stained_glass.jpg):
+
+![Output of me and a stained glass fractal](/assets/stained_glass_portrait.png)
+
+Same photo of me and some [psychedelic art by GMUNK](/assets/pockets.jpg)
+
+![Output of me and psychedelic art](/assets/pockets_portrait.png)
+
+[New York City](/assets/nyc.jpg) and [a rainforest](/assets/rainforest.jpg):
+
+![Output of New York City and a rainforest](/assets/jungle_nyc.png)
+
+[Kowloon Walled City](/assets/kowloon.jpg) and [a National Geographic
+Map](/assets/ngs_map.jpg):
+
+![Output of Kowloon and NGS map](/assets/kowloon_ngs.png)
+
+[A photo of me by Aidan](/assets/side_portrait.jpg) and [Head of Lioness by
+Theodore Gericault](/assets/head_lioness.jpg):
+
+![Output of photo of me and ](/assets/lion_portrait.png)
+
+[Photo I took of a Norwegian forest](/assets/forest_hill.jpg) and [The Mountain
+Brook by Albert Bierstadt](/assets/mountain_brook.jpg):
+
+![Output of Norwegian forest and The Mountain
+Brook](/assets/mountain_brook_hill.png)
+
+###Limitations###
+
+I don't have infinite money for a GTX Titan X, so I'm stuck with using OpenCL on
+my more-than-a-few-generations-old AMD card. It takes about a half-hour to
+generate one 512x512 px image in my set-up (which makes the feedback loop for
+correcting mistakes *very* long). And sometimes the neural-style refuses to run
+on my GPU (I suspect it runs out of VRAM), so I have to run it on my CPU which
+takes even longer...
+
+I am unable to generate bigger images (though
+[the author has been able to generate up to 1920x1010
+px](https://github.com/jcjohnson/neural-style/issues/36#issuecomment-142994812)).
+As the size of the output increases the amount of memory and time to generate
+also increases. And, it's not practical to just generate thumbnails to test
+parameters, because increasing the image size will probably generate a very
+different image since all the other parameters stay the same even though they
+are dependent on the image size.
+
+Some people have had success running these neural nets on GPU spot instances in
+AWS. It would be certainly cheaper than buying a new GPU in the short-term.
+
+So, I have a few more ideas for what to run, but it will take me quite a while
+to get through the queue.
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