Search Results for author:
Found 8 papers, 2 papers with code
Leveraging feature communication in federated learning for remote sensing image classification
In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies.
Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data
Multi-task partially annotated data where each data point is annotated for only a single task are potentially helpful for data scarcity if a network can leverage the inter-task relationship.
Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection
Experimental results show the improvement of performance when using a weak teacher with unseen data for training a multi-task student.
Knowledge Distillation for Object Detection: from generic to remote sensing datasets
Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities.
Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN
Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging.
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery
Deep-learning-based image classification and object detection has been applied successfully to tree monitoring.
CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data
It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery.
Automatic Generation of Dense Non-rigid Optical Flow
There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today.
