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Land Cover Classification

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Showing 76–100 of 151 papers

TitleStatusHype
CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences—0
Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy—0
Deep Convolutional Neural Networks for Map-Type Classification—0
Deep Neural Networks for automatic extraction of features in time series satellite images—0
Deep Self-taught Learning for Remote Sensing Image Classification—0
Dense Fusion Classmate Network for Land Cover Classification—0
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks—0
Dimensionality Reduction via Regression in Hyperspectral Imagery—0
Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery—0
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn—0
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach—0
Eelgrass beds and oyster farming at a lagoon before and after the Great East Japan Earthquake 2011: potential to apply deep learning at a coastal area—0
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images—0
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification—0
Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI—0
Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
FlexiMo: A Flexible Remote Sensing Foundation Model—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts—0
Generating a Training Dataset for Land Cover Classification to Advance Global Development—0
Global High Categorical Resolution Land Cover Mapping via Weak Supervision—0
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking—0
CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding—0
Randomized kernels for large scale Earth observation applications—0
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