Seeing Through the Clouds With DeepWaterMap

Isikdogan, Leo F.; Bovik, Alan C.; Passalacqua, Paola

doi:10.1109/lgrs.2019.2953261

Cited by 62 publications

(58 citation statements)

References 10 publications

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“…While under some circumstances having a non-binary representation of channel presence gives us useful information about overall change occurring, it is also insightful to have a representation of channel presence where the middle and edges of the channel are represented equally, allowing for the analysis of just the effect of channel bank movement. For this reason, we produce a second set of images using DeepWaterMap, a fully convolutional neural network that has been trained to extract water features from land, snow, ice, clouds, and shadows 33,34 . We observe that DeepWaterMap produces water presence quasi-probability maps where after normalization, land pixels are given a value of 0, channel features are given a value of 255, and non-channelized land features (such as the shrimp ponds and flooded poldered areas) are given values in between, effectively resulting in an almost binary channel map.…”

Section: Methodsmentioning

confidence: 99%

“…When using RivaMap, channel presence is detected in a non-binary manner and channel strength can be affected by channel bank movement, channel centerline movement, depth changes, and changes to the suspended sediment load inside the channel. We also extract channel presence from imagery using DeepWaterMap, a fully convolutional neural network trained to distinguish water from land, snow, ice, clouds, and shadows 33,34 . DeepWaterMap extracted channel imagery represents a nearly binary interpretation of channel presence where near bank water and channel centerline water are equally represented.…”

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confidence: 99%

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System wide channel network analysis reveals hotspots of morphological change in anthropogenically modified regions of the Ganges Delta

Jarriel

Isikdogan

Bovik

et al. 2020

Sci Rep

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the Ganges Brahmaputra Meghna Delta (GBMD) is a large and complex coastal system whose channel network is vulnerable to morphological changes caused by sea level rise, subsidence, anthropogenic modifications, and changes to water and sediment loads. Locating and characterizing change is particularly challenging because of the wide range of forcings acting on the GBMD and because of the large range of scales over which these forcings act. in this study, we examine the spatial variability of change in the GBMD channel network. We quantify the relative magnitudes and directions of change across multiple scales and relate the spatial distribution of change to the spatial distribution of a variety of known system forcings. We quantify how the channelization varies by computing the Channelized Response Variance (CRV) on 30 years of remotely sensed imagery of the entire delta extent. The CRV analysis reveals hotspots of morphological change across the delta. We find that the magnitude of these hotspots are related to the spatial distribution of the dominant physiographic forcings in the system (tidal and fluvial influence levels, channel connectivity, and anthropogenic interference levels). We find that the anthropogenically modified embanked regions have much higher levels of geomorphic change than the adjacent natural Sundarban forest and that this change is primarily due to channel infilling and increased rates of channel migration. Having a better understanding of how anthropogenic changes affect delta channel networks over human timescales will help to inform policy decisions affecting the human and ecological presences on deltas around the world. Many river deltas are large and complex systems whose channels evolve in response to changes in internal and external forcings. Changes to water inflow, sediment load, land use, subsidence, modern climate, and anthropogenic fluvial modification structures such as dams, levees, embankments, and dredging operations can all play a role in delta morphodynamics. With more than 500 million people living on river deltas around the world 1 , detecting the magnitudes of changes to deltas is critical for managing these systems, and it is especially important to detect the portions of the system that are changing the most. The goal of this study is to examine a large delta channel network to identify if change is occurring, where hotspots of change are occurring, what the rates of change are, and what may be causing the changes in certain areas to be higher than others. This information will help us better understand morphodynamic patterns in delta systems and make predictions to inform policy decisions affecting the human and ecological presences on deltas. Deltas are currently threatened by a variety of factors. As inflow conditions of water and sediment change, channel movement such as channel widening, narrowing, lateral migrations, meandering, and avulsions all act to rework delta landscapes. In addition to the natural morphological evolution of delta systems, changes can also be

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

System wide channel network analysis reveals hotspots of morphological change in anthropogenically modified regions of the Ganges Delta

Jarriel

Isikdogan

Bovik

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…Most importantly, the DeepWaterMap model also works across different terrains and in different weather conditions, although it is still affected by clouds. The same authors released a second version of the model, DeepWaterMap v2, in [ 40 ]. The major improvement from v1 is that the new version allows users to input large RS scenes without the need for tiling, and the authors made their network run efficiently with constant memory at inference time.…”

Section: The State Of the Art: Advances In Intelligent Waterbody Info...mentioning

confidence: 99%

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Yang

Driscol

Sarigai

et al. 2022

Sensors

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Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

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“…Climate in the GBMD is subtropical and dominated by the Southeast Asian monsoon, resulting in strongly seasonal fluvial discharges (Islam et al, 1999;Goodbred et al, 2003). Monsoonal rains between June and September are the primary source of runoff for the Brahmaputra and Ganges rivers (Best et al, 2007;Singh, 2007).…”

Section: Case Study: the Ganges-brahmaputra-meghna Deltamentioning

confidence: 99%

Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct

et al. 2020

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Abstract. The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help, but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas with 10 parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the 10 morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally controlled region of the delta. Islands at the delta front under the mixed influence of tides, fluvial–estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method is not able to distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous “natural” morphology or that the input data (time, resolution) used in this study are preventing the identification of a human signature. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems.

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Seeing Through the Clouds With DeepWaterMap

Cited by 62 publications

References 10 publications

System wide channel network analysis reveals hotspots of morphological change in anthropogenically modified regions of the Ganges Delta

System wide channel network analysis reveals hotspots of morphological change in anthropogenically modified regions of the Ganges Delta

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct

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