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230202 ||| eng |
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|a 9783036546308
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|a books978-3-0365-4630-8
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|a 9783036546292
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1 |
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|a Akhloufi, Moulay A.
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245 |
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|a Deep Learning Methods for Remote Sensing
|h Elektronische Ressource
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260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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300 |
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|a 1 electronic resource (344 p.)
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653 |
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|a temperature field
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653 |
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|a machine learning
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653 |
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|a off-grid
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|a natural hazard
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653 |
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|a optical sensors
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|a remote sensing sensors
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653 |
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|a U-Net
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653 |
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|a geoinformatics
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|a extreme events
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|a outdated building map
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653 |
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|a VHR images
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|a fire classification
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653 |
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|a fusion
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|a deep learning
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|a very high-resolution
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|a super-resolution
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|a geometry structure
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|a AGB
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|a alternating decision trees
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|a spatial model
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|a wildfire detection
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|a network
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|a deep learning neural network
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|a Technology: general issues / bicssc
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|a target detection
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653 |
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|a aerial images
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|a attention mechanism
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|a remote sensing images
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|a convolutional networks
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|a UAV
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|a prediction
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|a chimney
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|a meteorological parameters
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|a change detection
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|a NSFs
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|a faster R-CNN
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|a particle swarm optimization
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|a space-frequency pseudo-spectrum
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|a radar modulation signal
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|a high spatial resolution images
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|a typhoon
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|a bivariate statistics
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|a hazard map
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|a PSO
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|a unmanned aerial vehicle (UAV)
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|a object-based
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|a History of engineering and technology / bicssc
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|a rural settlements
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|a thermophysical parameters
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|a full convolutional network
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|a fire segmentation
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|a high resolution remote sensing image
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|a image enhancement
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|a mask R-CNN
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|a scattered vegetation
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|a geohazard
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|a multi-scale context
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|a deep neural networks
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|a circularly fully convolutional networks
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|a disease classification
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|a feature extraction
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|a ensemble learning
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|a DOA estimation
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|a time-frequency analysis
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|a high resolution
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|a deep-learning
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|a complex Morlet wavelet
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|a power transmission lines
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|a cross-layer feature fusion
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653 |
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|a ensemble models
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653 |
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|a Environmental science, engineering and technology / bicssc
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653 |
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|a fully convolutional feature maps
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|a flash-flood potential index
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|a DLNN
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653 |
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|a remote sensing
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653 |
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|a vision transformers
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|a Convolutional Neural Networks
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653 |
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|a erosion
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653 |
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|a Generative Adversarial Networks
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653 |
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|a channel-separable ResNet
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|a changes detection
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|a rainfall
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|a gully erosion susceptibility
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|a spatial analysis
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|a three-dimensional scene
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|a intelligent prediction
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|a cultivated land extraction
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|a vibration dampers detection
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|a ensemble model
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|a fully convolutional network
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|a image segmentation
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700 |
1 |
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|a Shahbazi, Mozhdeh
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700 |
1 |
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|a Akhloufi, Moulay A.
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700 |
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|a Shahbazi, Mozhdeh
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
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989 |
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|b DOAB
|a Directory of Open Access Books
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500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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024 |
8 |
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|a 10.3390/books978-3-0365-4630-8
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856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/6279
|7 0
|x Verlag
|3 Volltext
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856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/93850
|z DOAB: description of the publication
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|a 363
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|a 900
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|a 610
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|a 700
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|a 600
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|a 620
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|a Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing.
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