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Proceedings ArticleDOI

Road network mapping from aerial images

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TLDR
A road network mapping framework is proposed which uses a random forest model for pixel-wise road segmentation and computer vision post-processing steps including Connected Component Analysis (CCA) and Hough Lines method for network extraction from high-resolution aerial images.
Abstract
Building and expansion of an efficient transportation network are essential for urban city advancement. However, tracking road development in an area is not an easy task as city planners do not always have access to credible information. A road network mapping framework is proposed which uses a random forest model for pixel-wise road segmentation. Road detection is followed by computer vision post-processing steps including Connected Component Analysis (CCA) and Hough Lines method for network extraction from high-resolution aerial images. The custom dataset used consists of images collected from an urban settlement in India.

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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI

A Computational Approach to Edge Detection

TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI

Sequential Operations in Digital Picture Processing

TL;DR: The relative merits of performing local operations on ~ digitized picture in parallel or sequentially are discussed and some applications of the connected component and distance functions are presented.
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Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints

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.
Journal ArticleDOI

Computer Recognition of Roads from Satellite Pictures

TL;DR: A program which recognizes real roads, their intersections, and objects which are road-like is presented, structured in such a way that it can handle real roads as well as rivers and streams as they are seen on satellite pictures, tracks in bubble chambers, and veins under the appropriate magnification.
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