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Palabras clave – provistas por la editorial

full convolutional network; U-Net; cultivated land extraction; deep learning; remote sensing; target detection; high resolution remote sensing image; chimney; faster R-CNN; spatial analysis; super-resolution; Generative Adversarial Networks; Convolutional Neural Networks; disease classification; changes detection; fully convolutional feature maps; outdated building map; VHR images; gully erosion susceptibility; deep learning neural network; DLNN; particle swarm optimization; PSO; geohazard; geoinformatics; ensemble model; erosion; hazard map; spatial model; natural hazard; extreme events; rural settlements; fully convolutional network; multi-scale context; high spatial resolution images; flash-flood potential index; remote sensing sensors; bivariate statistics; alternating decision trees; ensemble models; deep-learning; fusion; mask R-CNN; object-based; optical sensors; scattered vegetation; very high-resolution; off-grid; DOA estimation; circularly fully convolutional networks; space-frequency pseudo-spectrum; high resolution; typhoon; rainfall; convolutional networks; image segmentation; prediction; ensemble learning; machine learning; feature extraction; AGB; NSFs; radar modulation signal; time–frequency analysis; complex Morlet wavelet; image enhancement; channel-separable ResNet; remote sensing images; change detection; attention mechanism; cross-layer feature fusion; power transmission lines; vibration dampers detection; unmanned aerial vehicle (UAV); deep neural networks; wildfire detection; fire classification; fire segmentation; vision transformers; UAV; aerial images; three-dimensional scene; temperature field; intelligent prediction; network; geometry structure; meteorological parameters; thermophysical parameters