It is very useful and increasingly popular to extract accurate road centerlines from very-high-resolution (VHR) remote sensing imagery for various applications, such as road map generation and updating etc. There are three shortcomings of current methods: (a) Due to the noise and occlusions (owing to vehicles and trees), most road extraction methods bring in heterogeneous classification results...

High Resolution satellite Imagery is an important source for road network extraction for urban road database creation, refinement and updating. However due to complexity of the scene in an urban environment, automated extraction of such features using various line and edge detection algorithms is limited. In this paper we present an integrated approach to extract road network from high resoluti...

Using vehicle cameras to automatically assess road weather conditions requires that the road surface first be identified and segmented from the imagery. This is a challenging problem for uncalibrated cameras such as removable dash cams or cell phone cameras, where the location of the road in the image may vary considerably from image to image. Here we show that combining a spatial prior with va...

Modern driver assistance systems such as collision avoidance or intersection assistance need reliable information on the current environment. Extracting such information from camera-based systems is a complex and challenging task for inner city traffic scenarios. This paper presents an approach to combined scene segmentation and object detection using stereo and color information. The extracted...

We describe the vision system for Alvin, the Autonomous Land Vehicle, addressing in particuly the task of road-following. The system builds symbolic descriptions of the road and obstacle boundaries using both video and range sensors. Road segmentation methods are described for video-based road-following, along with approaches to boundary extraction and the transformation of boundaries in the im...

The availability of highly accurate maps has become crucial due to the increasing importance of location-based mobile applications as well as autonomous vehicles. However, mapping roads is currently an expensive and humanintensive process. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to det...

We present an approach to image-based road segmentation for autonomous driving in which an appearance model is adaptively learned from laser range-finder data. By tracking linear configurations of ladar obstacles as putative road edges and backprojecting into the image, a coarse partition of pixels into high-confidence on-road and off-road regions, as well as unlabeled bands of uncertainty betw...

Recently, convolutional neural networks (CNNs) trained with strong human supervision have shown to achieve state of the art performance for both road detection and semantic segmentation. However, collecting strongly labeled data for both require detailed per-pixel annotations from humans which renders data annotation highly costly and time consuming. Therefore, in this work we propose methods t...

Traffic conditions in Indian urban and sub urban roads are in many ways not ideal for driving. This is due to faded and unmaintained lane markings. Therefore driving sometimes becomes difficult. Due to inappropriate markings of the roads, it is difficult to track the lane marking using conventional lane marking algorithms. Therefore the issue of Lane tracking with road boundary detection and ot...

Remote sensing is extensively used in cartography. As transportation networks expand, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic Aperture Radar (SAR) satellites can provide high resolution topographical maps. However roads are difficult to identify in SAR images as they look visually similar to other objects like rivers and railways. Deep ...