Texton Based Segmentation for Road Defect Detection from Aerial Imagery

Spatio-temporal Road Detection from Aerial Imagery using CNNs

The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image...

Road Extraction from Digital Aerial Imagery

Automatic Pavement Crack Detection Based on Aerial Imagery

Road health information is an important indicator for assessing the status of the road in management systems. Identifying the abandonment of surfaces is an important process in maintaining roads and traffic safety, which is traditionally conducted on the basis of field surveys. Today, remote sensing methods, especially photogrammetric imaging, are presented. In this article, based on by UAVs im...

Vehicle Detection From Aerial Imagery

This paper presents a new technique for vehicle detection from aerial imagery. The proposed technique is simple but more effective than some of the existing techniques reported earlier. This research involves an overall cascade system that concentrates on vehicle detection mainly in rural and dense environments. This research uses and compares between certain feature extraction techniques and s...

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

A new convolution neural network (CNN) architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN) design being structured into encoding and decoding stages. By taking advantage of recent advances in CNN designs, we use the composed inception module to replace common convolutional layers,...