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River deltas are complex, dynamic systems whose channel networks evolve in response to internal and external forcings. To capture these changes, methods to extract and analyze deltaic morphodynamics automatically using available remotely sensed imagery and experimental observations are needed. Here, we apply a promising method for the automatic extraction of channel presence called RivaMap, on both synthetic and experimental data sets, to investigate the changes experienced by the system in response to five changes in forcings. RivaMap is an automated method to extract nonbinarized channel locations from imagery based on a singularity index that combines the multiscale first and second derivatives of the image intensity to favor the identification of curvilinear features and suppress edges. We quantify how the channelization varies by computing the channelized response variance (CRV), which we define as the variance of each pixel's singularity index response through time. We find that increasing magnitudes of sediment inflow (Qs) and water inflow (Qw) result in corresponding increases in the maximum CRV. We find that increasing the ratio of Qs to Qw results in increased number of channelized areas. We see that adding cohesion to the exposed sediment surface of the experimental delta results in decreased magnitude and decreased number of channelized areas in the CRV. Finally, by observing changes to the CRV over time, we are able to quantify the timescale of internal channel reorganization events as the experimental delta evolves under constant forcings.

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

Traditional image signal processors (ISPs) are primarily designed and optimized to improve the image quality perceived by humans. However, optimal perceptual image quality does not always translate into optimal performance for computer vision applications. We propose a set of methods, which we collectively call VisionISP, to repurpose the ISP for machine consumption. VisionISP significantly reduces data transmission needs by reducing the bit-depth and resolution while preserving the relevant information. The blocks in VisionISP are simple, content-aware, and trainable. Experimental results show that VisionISP boosts the performance of a subsequent computer vision system trained to detect objects in an autonomous driving setting. The results demonstrate the potential and the practicality of VisionISP for computer vision applications.