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Found 8 papers, 2 papers with code
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model
Specifically, we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas.
DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data
Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission.
A review of individual tree crown detection and delineation from optical remote sensing images
); (2) traditional machine learning methods (such as random forest, decision tree, etc.
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation
Moreover, we propose the Class Center Contrast method to jointly utilize the labeled and unlabeled data.
Cross-regional oil palm tree counting and detection via multi-level attention domain adaptation network
In this paper, we propose a novel domain adaptive oil palm tree detection method, i. e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection.
swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight
Second, we propose a set of optimization strategies for redesigning a variety of neural network layers based on Caffe.
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes.
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources
Conditional probability distributions were computed based on data quality and reliability by using information selectively.
