This paper describes a deep learning based model for roads detection in Aerial image. In general, standard CNN networks would have less ability for tiny ...
The evaluation results demonstrate that the HydroGAN model competes with the state-of-the-art models for dense labeling of aerial and satellite imagery and ...
Abstract—this paper describes a deep learning based model for roads detection in Aerial image. In general, standard CNN networks would have less ability for ...
Rao et al. (2018) utilized a fully connected network (FCN) to detect roads in satellite images with 92.5% accuracy. While this approach provided high accuracy, ...
This paper proposes a novel approach for knowledge distillation, which effectively enhances the robustness of the distilled student model.
Jun 23, 2021 · The authors propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high resolution remote sensing image.
Oct 22, 2024 · Our goal is to develop a system for automatically detecting buildings and roads directly from aerial imagery.
To address this problem, we designed an inner convolutional network and a directional CRF for road extraction to segment roads from remote sensing images more ...
The aim of this work is to extract the road network from aerial images. What makes the problem challenging is the complex structure of the prior: roads form ...
Missing: FCN- | Show results with:FCN-