Removed batch normalisation, leaky relu and zero paddingĭ. ![]() In comparison, images from non-equatorial climates are clearer with distinguishable features and have less colorisation.īatch normalisation and dropout to the innermost layers Majority from cities that have similar aerial morphology to African cities and clear imagery, and we looked at european cities for clear images used as referenceĬ. We sampled images from different locations. While google maps has slightly better images than bing maps, they have strict copyright restrictions regarding editing or transformation of images, and as such, we went with bing maps. PREPROCESSINGįirst of all, we considered which platforms to download images from. The super resolution Gan is also a form of conditional gan with resnet block generator and upsampling discriminatorįor our workflow we will be preprocessing our data and collecting them form satellite images and will be fed into our stacked gans process of deep colorization and super resolution then we will be tiling them back to generate an enhanced version of the satellite imagery B. METHODOLOGYĪ bit about the architectures, the deep colorisation is a conditional GAN with a generator that takes in the intensity L values of an image and generates the corresponding RGB values, the discriminator is also fed the condition image alongside either the generated or input image and tries to tell if it is real or not. Our project seeks to create a large-scale open dataset using GAN -generated super-resolution satellite imagery to support urban design applications in contexts where datasets are not readily available and data collection is difficult A. Urban data have a range of applications from population estimation, urban planning and humanitarian response, to environmental and climate science. If we think out of the box, there are many creative applications that GANs are equally effective in! Apart from image quality restoration, another cool example would be image colorization, where we can go through the same process to generate a discoloured dataset and use that to train our GAN. These datasets will support a wide range of applications in architectural and urban design research.Įven though random image generation is a hot topic today, GANs are not limited in usage to these generation tasks (Faces, scenery, paintings etc). We hope to improve the quality of open source aerial images and generate large-scaurban satellite image datasets in contexts where open gov datasets are not existent ,and where means of aerial image data collection is out of reach. GAN technologies have new application in select architectural design tasks involving the creation and analysis of 2D and 3D designs from specific architectural styles and how GANs can be used analytically to gain insights into different architectural worksĭigital Image Restoration is a technique used to recover original image information from degraded images and plays a significant role in improving quality of noisy, blurred and camera-misfocused images obtained from aircrafts, poor-quality videotapes and satellite imagesįor our project, we will be exploring the use of GANs for image restoration of aerial satellite imagery in regions sub-saharan Africa where satellite imagery are of very low and unusable quality and generate higher resolution images out of it STIR.GANS – SATELLITE TILE IMAGE RESTORATION GANS is an exploration on restoring Satellite imagery. ![]() ![]() STIR.GANS SATELLITE TILE IMAGE RESTORATION GANSĭeep Learning and Generative Adversarial Networks are an emerging research areas has successfully been used for high-fidelity natural image synthesis, data augmentation tasks, improving image compressions, and many more.
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