Flooded Area Detection from Uav Images Based on Densely Connected Recurrent Neural Networks

Rahnemoonfar, Maryam; Murphy, Robin R.; Vicens-Miquel, Marina; Dobbs, Dugan; Adams, Ashton

doi:10.1109/igarss.2018.8517946

Cited by 57 publications

(28 citation statements)

References 11 publications

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“…Furthermore, the image data for computer vision can be collected through various means including ground cameras and UAV. Unmanned Aerial Vehicles are known to provide a fast and cost-effective approach to collecting data [74]. For example, Sullivan et al [75] effectively utilised drones to collect stereo images of streambeds to gather information about the potential threat imposed by large woody debris (LWD) to culverts and bridges.…”

Section: Computer Vision and Iot Sensors For Early Warning Systemsmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

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Section: Computer Vision and Iot Sensors For Early Warning Systemsmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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“…In recent years UAS have been extensively used in various areas such as scene understanding and image classification [12], flood detection [16], vehicle tracking [38], forest inventory [39], soil moisture [40], and wildlife and animal management [36,41]. There has been very limited work on use of UAS for monitoring livestock particularly for animal detection, feeding behavior, and health monitoring.…”

Section: Unmanned Aerial Systemsmentioning

confidence: 99%

“…Recent advances in deep neural networks (DNNs) along with massive datasets have facilitated the progress in artificial intelligence tasks such as image classification [12], object recognition [13,14], counting [8,9], contour and edge detection [15] and semantic segmentation [16]. Most successful network architectures have improved the performance of various vision tasks at the expense of significantly increased computational complexity.…”

Section: Introductionmentioning

confidence: 99%

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

et al. 2019

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Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

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“…In recent years, the research community has witnessed advances in artificial intelligence (AI). Recent advances in deep neural networks (DNNs) and massive datasets have facilitated progress in AI tasks such as classification [7][8][9][10], object recognition [11,12], counting [13][14][15], contour and edge detection [16] and semantic segmentation [17][18][19]. Despite this progress, these algorithms are limited to cases where large labeled datasets are available.…”

Section: Introductionmentioning

confidence: 99%

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Rahnemoonfar

Johnson

Paden

2019

Sensors

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Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

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Flooded Area Detection from Uav Images Based on Densely Connected Recurrent Neural Networks

Cited by 57 publications

References 11 publications

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

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