Extensibility of U-Net neural network model for hydrographic feature extraction and implications for hydrologic modeling
Accurate maps of regional surface water features are integral for advancing ecologic,
atmospheric and land development studies. The only comprehensive surface water feature …
atmospheric and land development studies. The only comprehensive surface water feature …
Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach
ABSTRACT High-Resolution Digital Elevation Models (HRDEMs) have been used to
delineate fine-scale hydrographic features in landscapes with relatively level topography …
delineate fine-scale hydrographic features in landscapes with relatively level topography …
Classifying open water features using optical satellite imagery and an object-oriented convolutional neural network
MA Merchant - Remote Sensing Letters, 2020 - Taylor & Francis
In this study, Sentinel-2 optical satellite imagery was acquired over the Peace Athabasca
Delta and assessed for its open water classification capabilities using an object-oriented …
Delta and assessed for its open water classification capabilities using an object-oriented …
Surface water mapping by deep learning
F Isikdogan, AC Bovik… - IEEE journal of selected …, 2017 - ieeexplore.ieee.org
Mapping of surface water is useful in a variety of remote sensing applications, such as
estimating the availability of water, measuring its change in time, and predicting droughts …
estimating the availability of water, measuring its change in time, and predicting droughts …
A deep learning approach to mapping irrigation using Landsat: IrrMapper U-Net
Accurate maps of irrigation are essential for understanding and managing water resources.
We present a new method of mapping irrigation based on an ensemble of convolutional …
We present a new method of mapping irrigation based on an ensemble of convolutional …
Automated extraction of surface water extent from Sentinel-1 data
Accurately quantifying surface water extent in wetlands is critical to understanding their role
in ecosystem processes. However, current regional-to global-scale surface water products …
in ecosystem processes. However, current regional-to global-scale surface water products …
[HTML][HTML] Google Earth Engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping
JN Hird, ER DeLancey, GJ McDermid, J Kariyeva - Remote sensing, 2017 - mdpi.com
Modern advances in cloud computing and machine-leaning algorithms are shifting the
manner in which Earth-observation (EO) data are used for environmental monitoring …
manner in which Earth-observation (EO) data are used for environmental monitoring …
Urban land-use land-cover extraction for catchment modelling using deep learning techniques
Throughout the world, the likelihood of floods and managing the associated risk are a
concern to many catchment managers and the population residing in those catchments …
concern to many catchment managers and the population residing in those catchments …
DeepWaterFraction: A globally applicable, self-training deep learning approach for percent surface water area estimation from Landsat mission imagery
Surface water area estimation is essential for understanding global environmental
dynamics, yet it presents significant challenges, particularly when dealing with small water …
dynamics, yet it presents significant challenges, particularly when dealing with small water …
Artificial neural networks in remote sensing of hydrologic processes
Recent progress in remote sensing technologies, coupled with ongoing and planned remote
sensing missions, is expected to generate hydrologic data at spatial, temporal, and spectral …
sensing missions, is expected to generate hydrologic data at spatial, temporal, and spectral …