Realistic River Image Synthesis using Deep Generative Adversarial Networks

Gautam, Akshat; Sit, Muhammed; Demir, İbrahim

doi:10.48550/arxiv.2003.00826

Cited by 7 publications

(8 citation statements)

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“…Mohandoss et al [5] use a purely generative approach to synthesize multiresolution, multi-spectral terrain imagery. Gautam et al [6] specifically zero in on the problem of generating satellite images with rivers. Andrade and Fernandes [7] synthesize satellite images from historical maps using conditional GANs.…”

Section: Related Workmentioning

confidence: 99%

Video modification in drone and satellite imagery

Reale,

Murphy,

Cornacchia

et al. 2024

Disruptive Technologies in Information Sciences VIII

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The ability to create and detect synthetic video is becoming critically important to scene understanding. Techniques for synthetic manipulation and augmentation of data increase diversity within available datasets, while not requiring laborious labeling efforts. That is, the ability to create synthetic video can enable augmentation of small realistic datasets on which to further train Artificial Intelligence and Machine Learning (AI/ML) algorithms. Thus, it may be desirable to add, remove, or modify vehicles in satellite and overhead video. In our previous work, we leveraged generative adversarial networks (GANs) to transform cars into trucks (and vice versa) in static images. We utilized an attention-based masking approach that assists the network in transformation of the object and not background. In addition, we demonstrated the benefits of numerous data augmentation procedures, including presenting a new artificial dataset of vehicles from an aerial perspective and introducing novel augmentation techniques appropriate for our network architectures. This work extends the applied techniques from still imagery to video. We employ a few different architectures: (1) a fully dynamic 3D convolutional discriminator network with static generators, (2) a fully dynamic 3D convolutional discriminator and generator network, and (3) an architecture that computes "warp" between frames for input to a static generator. Additionally, to help enforce consistency, we experiment with an interframe classifier that verifies whether two frames belong to the same video sequence or not. We run experiments on a real-world dataset, presenting promising results in terms of FID, KID, and metrics developed from a classifier trained on our dataset.

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Section: Related Workmentioning

confidence: 99%

Video modification in drone and satellite imagery

Reale,

Murphy,

Cornacchia

et al. 2024

Disruptive Technologies in Information Sciences VIII

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“…These modules are: (1) Data, specifically for retrieving data from different data source APIs, data transformation, uploading and downloading; (2) Visualize, module for data visualization using the Google Charts API as well as JSON object renderers; (3) Maps, used for creating maps rendered on the browser to view georeferenced data and data selection; and (4) Analyze, containing 3 main components for performing hydrological subroutines used in academia and industry (hydro), performing statistical analysis on data (stats), and creating simple neural network models (NN) (Figure 1). While deep learning applications becoming popular in recent years for hydrology and water resources applications (Gautam et al, 2020;Demiray at al., 2021), web based neural network libraries and platforms (Sit and Demir, 2022) making access to these models easier for domain experts. The library was developed using JavaScript, considering the advantages the programming language has for the development of web applications, integration with other opensource libraries, usage of standards, and lexical instructions that aids on learning.…”

Section: Hydrolangjs Frameworkmentioning

confidence: 99%

HydroLang Markup Language: Community-Driven Web Components for Hydrological Analyses

Erazo¹,

Sermet²,

Demir³

2022

Preprint

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We introduce HydroLang Markup Language (HL-ML), a programming interface that uses markup language to perform environmental analyses using the hydrological and environmental framework HydroLang. The software acts as a self-contained interface that uses HTML tags powered by the web components specification to generate simple hydrological computations that enable data analysis, visualization and manipulation via semantically driven instructions. It enables hydrological researchers and professionals to use markup language to retrieve, analyze, visualize, and map data without the need for a programming experience. The components' adaptability enables users to run analytical routines that perform simple and complex analyses on the client side. We present the implementation details of the approach, the use of custom elements in web technologies and academia, and share sample usages to demonstrate the simplicity of use of the human-readable and computer-executable framework.

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“…GANs are initially used as generative models, as in randomly generating new samples from a dataset to appear as if they are from the originating dataset when visualized (Gautam et al, 2020). They achieve this goal by mapping random noise to real samples from the given dataset, and then they generate new instances from new random noise tensors.…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

Sit¹,

Demiray²,

Xiang³

et al. 2020

Preprint

Self Cite

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The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research which incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.

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Realistic River Image Synthesis using Deep Generative Adversarial Networks

Cited by 7 publications

References 0 publications

Video modification in drone and satellite imagery

Video modification in drone and satellite imagery

HydroLang Markup Language: Community-Driven Web Components for Hydrological Analyses

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

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