Semantic image segmentationEncoder–decoderRoad network plays a significant role in today's urban development. These are a vital part of applications such as automatic road navigation, traffic management, route optimization, etc. In this paper, we aim to explore the potential and performance of ...
In this work, we provide a new insight on the use of morphological image analysis for road extraction in complex urban scenarios, and propose a technique for road segmentation that only relies on this domain. The keypoint of the technique is the use of skeletons as powerful descriptors for ...
Simonyan, K.; Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014) Long, J.; Shelhamer, E.; Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and ...
Image classification using Keras Semantic segmentation Pixel-wise classification Instance segmentation with keras - links to satellite examples Semantic Segmentation on Aerial Images using fastai https://github.com/Paulymorphous/Road-Segmentation Monitor water levels, coast lines, size of urban areas, wildfi...
The process of image segmentation assigns a class label to each pixel in an image, effectively transforming an image from a 2D grid of pixels into a 2D grid of pixels with assigned class labels. One common application of image segmentation is road or building segmentation, where the goal is ...
road segmentationedge detectionRoad detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images ...
We carried out classification in three modules namely (a) Preprocessing using Gaussian filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM ...
Initially devised for natural images, these networks have been revisited and adapted to tackle semantic segmentation problems (i.e., the process of linking each pixel in an image to a class label) in remote sensing, such as road extraction (Cheng et al., 2017), cloud detection (Chai et ...
Trains a model on a training set made up of(image, mask)pairs. The result ofrs trainis a checkpoint containing weights for the trained model. Thers traintool trains a fully convolutional neural net for semantic segmentation on a dataset with(image, mask)pairs generated byrs downloadandrs ras...
In our approach, to provide scalability across countries and terrains, we have explored and modified state-of-the-art image segmentation networks. Finally, processing the road topology has been studied as an example case for novel or modified clustering and graph partitioning approaches [24,25,26]...