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Modeling urbanization patterns with generative adversarial networks [article]

Adrian Albert, Emanuele Strano, Jasleen Kaur, Marta Gonzalez
2018 arXiv   pre-print
In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory.  ...  We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level  ...  Comparing real urban built land use maps (left) with synthetic maps (right) simulated with a Generative Adversarial Network (GAN).  ... 
arXiv:1801.02710v1 fatcat:eyymm3ovyfgmxhetnj7kgkplfe

TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning [article]

Seongjin Choi, Jiwon Kim, Hwasoo Yeo
2021 arXiv   pre-print
This research proposesTrajGAIL, a generative adversarial imitation learning framework for the urban vehicle trajectory generation.  ...  The model is trained by the generative adversarial framework, which uses the reward function from the adversarial discriminator.  ...  In particular, Goodfellow et al. (2014) , a new generative model called Generative Adversarial Networks (GAN), addressed inherent difficulties of deep generative models associated with intractable probabilistic  ... 
arXiv:2007.14189v4 fatcat:svsumpjklncxdh62g2zwyhmvnq

Preserving Query Privacy in Urban Sensing Systems [chapter]

Emiliano De Cristofaro, Roberto Di Pietro
2012 Lecture Notes in Computer Science  
We introduce a realistic network model and two novel adversarial models: resident and non-resident adversaries.  ...  Urban Sensing is an emerging paradigm that combines the ubiquity of smartphones with measurement capabilities of sensor networks.  ...  partially supported by: the grant HPC-2011 from CASPUR (Italy); Prevention, Preparedness and Consequence Management of Terrorism and other Security-related Risks Programme European Commission -Directorate-General  ... 
doi:10.1007/978-3-642-25959-3_17 fatcat:ebvyag5n2vgvhgb5qmfbpqv34m

Infrared Image Colorization Based on a Triplet DCGAN Architecture

Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
This paper proposes a novel approach for colorizing near infrared (NIR) images using a Deep Convolutional Generative Adversarial Network (GAN) architecture.  ...  The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way.  ...  On the other hand, the generator is then optimized in order to increase the probability of the generated data being highly rated: CNN Generative Adversarial Architecture (G) Generator Network with Model  ... 
doi:10.1109/cvprw.2017.32 dblp:conf/cvpr/SuarezSV17 fatcat:amzq2uj3qrclrekrssx3gjas5m

Deep Learning based Urban Vehicle Trajectory Analytics [article]

Seongjin Choi
2021 arXiv   pre-print
As a result, the objective of this dissertation is to develop deep-learning based models for urban vehicle trajectory analytics to better understand the mobility patterns of urban traffic networks.  ...  The urban vehicle trajectory analytics offers unprecedented opportunities to understand vehicle movement patterns in urban traffic networks including both user-centric travel experiences and system-wide  ...  ., 2014] introduced a new generative model called Generative Adversarial Networks (GAN), which addressed inherent difficulties of deep generative models associated with intractable probabilistic computations  ... 
arXiv:2111.07489v1 fatcat:zanf5aj7unfb5joey3f7lzhtbm

Automatic Large-Scale 3D Building Shape Refinement Using Conditional Generative Adversarial Networks

Ksenia Bittner, Marco Korner
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
To this end, a conditional generative adversarial network (cGAN) is trained to generate accurate LIDAR DSM-like height images from noisy stereo DSMs.  ...  The introduction of generative adversarial networks (GANs) attracted a lot of attention in the field of machine learning as they offer a new possibility to generate highquality images across a wide range  ...  To this end, a conditional generative adversarial network (cGAN) is trained to generate accurate LIDAR DSM-like height images from noisy stereo DSMs.  ... 
doi:10.1109/cvprw.2018.00249 dblp:conf/cvpr/Bittner018 fatcat:x76e233upvgyngtib4j3i7hg3m

GENERATIVE ADVERSARIAL NETWORKS TO GENERALISE URBAN AREAS IN TOPOGRAPHIC MAPS

A. Courtial, G. Touya, X. Zhang
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
This article presents how a generative adversarial network (GAN) can be employed to produce a generalised map that combines several cartographic themes in the dense context of urban areas.  ...  with enlarged individual buildings.  ...  Segmentation and sampling method for complex polyline generalization based on a generative adversarial network.  ... 
doi:10.5194/isprs-archives-xliii-b4-2021-15-2021 fatcat:mjxn3w6s6bgvjajin36kxyumou

Graph-GAN: A spatial-temporal neural network for short-term passenger flow prediction in urban rail transit systems [article]

Hua Li, Jinlei Zhang, Lixing Yang, Jianguo Qi, Ziyou Gao
2022 arXiv   pre-print
The Graph-GAN consists of two major parts: (1) a simplified and static version of the graph convolution network (GCN) used to extract network topological information; (2) a generative adversarial network  ...  (GAN) used to predict passenger flows, with generators and discriminators in GAN just composed of simple fully connected neural networks.  ...  Figure 3 3 Figure 3 The framework of generative adversarial network GAN is a generative model with an adversarial process and has been widely used in various fields, such as image generation, video prediction  ... 
arXiv:2202.06727v1 fatcat:p4kkqrtvlzhnxndvq5livg7gjy

Situation assessment in urban combat environments

Subrata Das
2005 2005 7th International Conference on Information Fusion  
Techniques also exist for extracting causal Bayesian belief network structures along with their strengths.  ...  networks (BNs).  ...  Techniques also exist for extracting causal Bayesian belief network structures along with their strengths.  ... 
doi:10.1109/icif.2005.1591825 fatcat:wc6u7nsdxjehfppzqn6deds3lm

Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images [article]

Edward Collier, Kate Duffy, Sangram Ganguly, Geri Madanguit, Subodh Kalia, Gayaka Shreekant, Ramakrishna Nemani, Andrew Michaelis, Shuang Li, Auroop Ganguly, Supratik Mukhopadhyay
2019 arXiv   pre-print
In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks.  ...  We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset.  ...  Generative adversarial networks (GAN) address this pitfall by simultaneously training a discriminator network to differentiate between real and generated images [26] .  ... 
arXiv:1902.04604v1 fatcat:p4rdbwllybbkjm6tz3cf47nhua

Satellite image inpainting with deep generative adversarial neural networks

Mohamed Akram Zaytar, Chaker El Amrani
2021 IAES International Journal of Artificial Intelligence (IJ-AI)  
This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution  ...  Experimental results show that the proposed system outperforms traditional and neural network baselines.  ...  As a result, the generative adversarial network is extended with a conditional layer to encode the static priors.  ... 
doi:10.11591/ijai.v10.i1.pp121-130 fatcat:463hodwy6nfmrdvrsjeimh3vxe

Intelligent Adversary Placements for Privacy Evaluation in VANET

Ikjot Saini, Benjamin St. Amour, Arunita Jaekel
2020 Information  
However, an adversary can utilize knowledge of traffic patterns and PMTs to place eavesdropping stations in a more targeted manner, leading to an increased tracking success rate.  ...  In this paper, we propose two new adversary placement strategies and study the impact of intelligent adversary placement on tracking success using different PMTs.  ...  Network and Adversary Model When evaluating PMTs, it is important to consider both the network models and an accurate and realistic adversary model.  ... 
doi:10.3390/info11090443 fatcat:45ghdwegmbczjped64nlu2jpu4

Suggestive Site Planning with Conditional GAN and Urban GIS Data [chapter]

Runjia Tian
2021 Proceedings of the 2020 DigitalFUTURES  
Pix2PixHD Conditional Generative Adversarial Neural Network is used to learn the mapping from a site boundary geometry represented with a pixelized image to that of an image containing building footprint  ...  built environment with Artificial Neural Networks.  ...  In a narrow sense, site planning could be formalized as a conditional generation problem solvable with state-of-the-art machine learning models such as Conditional Generative Adversarial Neural Networks  ... 
doi:10.1007/978-981-33-4400-6_10 fatcat:7fvwn7fm25fqxfwqfza43gaa2u

Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture

Abolfazl Abdollahi, Biswajeet Pradhan, Shilpa Gite, Abdullah Alamri
2020 IEEE Access  
Thus, we introduce an end-to-end convolutional neural network called Generative Adversarial Network (GAN) in this study to tackle these issues.  ...  In the generative model, we utilized SegNet model with Bi-directional Convolutional LSTM (BConvLSTM) to generate the segmentation map from Massachusetts building dataset containing highresolution aerial  ...  The proposed SegNet model with BConvLSTM were used in the generative part of the GAN model for adversarial training.  ... 
doi:10.1109/access.2020.3038225 fatcat:fuhwrnm5dfg7nck2d52xsb2yry

Reimagining City Configuration: Automated Urban Planning via Adversarial Learning [article]

Dongjie Wang, Yanjie Fu, Pengyang Wang, Bo Huang, Chang-Tien Lu
2020 arXiv   pre-print
The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area.  ...  Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to  ...  Generative Adversarial Networks. Generative Adversarial Networks is a hot research field in recent years.  ... 
arXiv:2008.09912v1 fatcat:ufcgtdgknvahbb5tnl3as74ysy
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