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Using Deep Learning for Automatic Water Stage Measurements
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Photogrammetric discharge monitoring of small tropical mountain rivers: A case study at Rivière des Pluies, Réunion Island
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Application of an automated LSPIV system in a mountainous stream for continuous flood flow measurements
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Topological structural analysis of digitized binary images by border following
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Image analysis techniques to estimate river discharge using time-lapse cameras in remote locations
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Fully automated snow depth measurements from time-lapse images applying a convolutional neural network
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Automatic Image‐Based Water Stage Measurement for Long‐Term Observations in Ungauged Catchments
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NIH Image to ImageJ: 25 years of image analysis
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Deep learning for geometric and semantic tasks in photogrammetry and remote sensing
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The interpretation of structure from motion
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Deep Learning-Based Unmanned Surveillance Systems for Observing Water Levels
inferredcites →https://doi.org/10.1109/cvpr.2006.19openalex-snapshot
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
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Fully convolutional networks for semantic segmentation
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Learning Deconvolution Network for Semantic Segmentation
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Surface Water Mapping by Deep Learning
inferredcites →https://doi.org/10.1109/jstars.2019.2929601openalex-snapshot
Recognizing Global Reservoirs From Landsat 8 Images: A Deep Learning Approach
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Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model
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Internet of Things in Industries: A Survey
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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Photogrammetry: Geometry from Images and Laser Scans
inferredcites →https://doi.org/10.1515/9783111029672openalex-snapshot
Close-Range Photogrammetry and 3D Imaging
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Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images
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Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery
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Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.5194/esurf-4-359-2016openalex-snapshot
Image-based surface reconstruction in geomorphometry – merits, limits and developments
inferredcites →https://doi.org/10.5194/hess-22-1-2018openalex-snapshot
Technical note: Stage and water width measurement of a mountain stream using a simple time-lapse camera
inferredcites →https://doi.org/10.5194/hess-24-1429-2020openalex-snapshot
Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery
inferredcites →https://doi.org/10.5194/isprs-archives-xlii-2-543-2018openalex-snapshot
VERSATILE MOBILE AND STATIONARY LOW-COST APPROACHES FOR HYDROLOGICAL MEASUREMENTS
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Comment on egusphere-2022-735
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Comment on egusphere-2022-735
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Comment on egusphere-2022-735
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