Deep learning has shown promising performance in submeter-level mapping tasks; however, the annotation cost of submeter-level imagery remains a challenge, especially when applied on a large scale.
Optical high-resolution imagery and OpenStreetMap (OSM) data are two important data sources for land-cover change detection.
With the success of efficient deep learning methods (i. e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis.
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping.
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
This multi-task learning with dynamic weights also boosts of the performance on the different tasks comparing to the state-of-art methods with single-task learning.
Ranked #1 on Facial Expression Recognition (FER) on Oulu-CASIA
This paper proposes a holistic multi-task Convolutional Neural Networks (CNNs) with the dynamic weights of the tasks, namely FaceLiveNet+, for face authentication.