Extensibility Of U-Net Neural Network Model For Hydrographic Feature Extraction And Implications For Hydrologic Modeling
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https://doi.org/10.3390/rs13122368 <-- shared paper
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#GIS #spatial #mapping #machinelearning #neuralnetwork #Unet #featureextraction #hydrography #hydrology #water #automation #remotesensing #surfacewater #NHD #bluelines #topography #deeplearning #AI #artificialintelligence #LiDAR #ifSAR #elevation #topography #ANN #HPC #model #modeling #trainingdata #Alaska #USA #watersheds #DTM #DSM #DEM #routing #network #D8 #maps #opendata #3dep #elevation #topography #USGS
#gis #spatial #mapping #machinelearning #neuralnetwork #unet #featureextraction #hydrography #hydrology #water #automation #remotesensing #surfacewater #nhd #bluelines #topography #deeplearning #AI #artificialintelligence #lidar #ifsar #elevation #ann #hpc #model #modeling #trainingdata #alaska #USA #watersheds #dtm #dsm #dem #routing #network #d8 #Maps #opendata #3dep #usgs
📊 Feature creation in #DataMining & #MachineLearning is 🔑! It allows us to capture essential info efficiently. From extracting higher-level features to constructing new ones, and mapping data to a new space, these methodologies enhance classification techniques. #FeatureExtraction #FeatureConstruction
#datamining #machinelearning #featureextraction #featureconstruction
Group Generators select a subset of features, and Group Encoders output latent representations (similar to #FeatureExtraction methods, like PCA). Each group has to minimize the resulting loss of their Group Predictors, and the overall loss is calculated using:
"A Topological Deep Learning Framework for Neural Spike Decoding"
https://arxiv.org/abs/2212.05037
#Neuroscience #Neuro #Brain #MachineLearning #DeepLearning #FeatureExtraction #SpikeTrains #GridCells #HeadDirectionCells
#arxivfeed #neuroscience #neuro #brain #machinelearning #deeplearning #featureextraction #spiketrains #gridcells #headdirectioncells