Building extraction in very high resolution remote sensing imagery using deep learning and guided filters
Very high resolution (VHR) remote sensing imagery has been used for land cover
classification, and it tends to a transition from land-use classification to pixel-level semantic …
classification, and it tends to a transition from land-use classification to pixel-level semantic …
An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images
A Abdollahi, B Pradhan, AM Alamri - Geocarto International, 2022 - Taylor & Francis
Building objects is one of the principal features that are essential for updating the geospatial
database. Extracting building features from high-resolution imagery automatically and …
database. Extracting building features from high-resolution imagery automatically and …
[HTML][HTML] Learning dual multi-scale manifold ranking for semantic segmentation of high-resolution images
Semantic image segmentation has recently witnessed considerable progress by training
deep convolutional neural networks (CNNs). The core issue of this technique is the limited …
deep convolutional neural networks (CNNs). The core issue of this technique is the limited …
Detection of pictorial map objects with convolutional neural networks
R Schnürer, R Sieber, J Schmid-Lanter… - The Cartographic …, 2021 - Taylor & Francis
In this work, realistically drawn objects are identified on digital maps by convolutional neural
networks. For the first two experiments, 6200 images were retrieved from Pinterest. While …
networks. For the first two experiments, 6200 images were retrieved from Pinterest. While …