Automated Road Extraction From High Resolution Satellite Images

TL;DR: A semi-automated process to extract object motion and attributes from utilizing a remote sensing methodology using Earth Observation data from single pass satellite imagery is presented in this paper, which performs image processing, object segmentation, object measurement, image normalization and image matching and velocity calculation to extract physical attributes of the object.

TL;DR: In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

TL;DR: The road network detection based on deep learning sementic segmentation architecture such as U-Net and SegNet from Remote Sensing Images (RSI) from Remote Sensing Images (RSI) is addressed.

TL;DR: The experimental results demonstrate that the models that integrate with the Coordinate Attention Module and the Micro Token Pyramid Module surpass the limitations of current research and are suitable for road extraction from remote sensing images on mobile devices.

TL;DR: The state of the art on automatic road extraction for GIS update from aerial and satellite imagery is surveyed and a novel classification of road extraction methods according to the criteria is included in order to provide a significant contribution to research in this topic.

TL;DR: An automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree and introduces a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree.

TL;DR: Two salient features of roads, namely, distinct spectral contrast and locally linear trajectory, are exploited to design a multistage framework to extract roads from high-resolution multispectral satellite images and are compared against a few state-of-the-art methods to validate the superior performance.

TL;DR: In this article, the authors provide an overview of the types of imagery being used for linear feature extraction and also describe methods used for feature extraction, and consider quantitative and qualitative accuracy assessment of these procedures.