This repository contains code for implementing multi class semantic segmentation (specifically applied to satellite image classification) with PyTorch implementation of UNet.
-
Updated
Oct 7, 2022 - Jupyter Notebook
#
deepglobe-dataset
Here are 4 public repositories matching this topic...
This software is a modification of Visualizer from Spacenet competition for road extraction. I made it work for DeepGlobe dataset also.
-
Updated
Mar 20, 2021 - Java
Multi-scale patch-wise semantic segmentation of satellite images using U-Net architecture.
opencv
computer-vision
deep-learning
pytorch
convolutional-neural-networks
semantic-segmentation
u-net
deeplab-v3-plus
deepglobe-dataset
land-cover-classification
-
Updated
Mar 14, 2023 - Jupyter Notebook
Multi-scale patch-wise semantic segmentation of satellite images using U-Net architecture.
opencv
computer-vision
deep-learning
pytorch
convolutional-neural-networks
semantic-segmentation
u-net
deeplab-v3-plus
deepglobe-dataset
land-cover-classification
-
Updated
Mar 14, 2023 - Jupyter Notebook
Improve this page
Add a description, image, and links to the deepglobe-dataset topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the deepglobe-dataset topic, visit your repo's landing page and select "manage topics."