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Creating road maps is essential for applications such as autonomous driving and city planning. Most approaches in industry focus on leveraging expensive sensors mounted on top of a fleet of cars. This results in very accurate estimates when exploiting a user in the loop. However, these solutions are very expensive and have small coverage. In contrast, in this paper we propose an approach that directly...
This paper proposes a novel approach for segmenting and space partitioning data of sparse 3D LiDAR point clouds for autonomous driving tasks in urban environments. Our main focus is building a compact data representation which provides enough information for an accurate segmentation algorithm. We propose the use of an extension of elevation maps for automotive driving perception tasks which is capable...
In this paper, a structural conditional random field framework (SCRF) is proposed to detect the detailed change information from high spatial resolution (HSR) remote sensing imagery. Traditional random field based methods encounter the over-smoothing problem when deal with HSR images and the boundary of changed objects cannot be preserved well. To solve this problem, in SCRF, fuzzy c means (FCM) is...
This paper reports on classification methods applied and tested for land use classification in a semi-arid environment. Our study, conducted on two irrigated sites located in the Kairouan region, the largest irrigated region in Tunisia, compared Support Vector Machine (SVM) and Maximum Likelihood classification of SPOT-7 data. To produce a per-field classification a Mean-Shift Segmentation has been...
The paper discusses some properties of photogrammetric imagery of urban areas, which allows you to create a fairly simple method for the detection of structures without altitude information using. The algorithm of step-by-step selection of buildings “by contradiction” is offered. The resultant algorithm partially copes with the task, but has some problem places, in particular segments with an earth...
Available big geoscientific data and modern powerful computation hardware have laid a solid foundation for the prevailing deep learning models in the field of image classification, detection and segmentation. In these models, fully convolutional networks achieve unprecedented success in image segmentation tasks [6]. In this paper, we apply the contemporary image segmentation models in the context...
Geographic object-based image analysis (GEOBIA) is a useful method for image classification. This study aimed to find optimal parameters combination for extracting buildings using object-based image analysis. The images taken from two satellites Quickbird-2 and Gaofen (GF-1) were used to extract information of buildings in an urban area. The most important step in image segmentation is to determine...
Semantic segmentation is a process that recognizes objects and their regions in images and is a significant challenge in image recognition. Many conventional methods have been proposed, and these studies are expected to be used for many applications such as image retrieval, robot vision for autonomous mobile robots, an automatic driving system for motor vehicles. However, semantic segmentation is...
To deal with the problem of urban ground object information extraction, the paper proposes an object-oriented classification method using aerial image and LiDAR data. Firstly, we select the optimal segmentation scales of different ground objects and synthesize them to get accurate object boundaries. Then, we use ReliefF algorithm to select the optimal feature combination and eliminate the Hughes phenomenon...
The urbanization process changed the urban ecological land and consequently affected the quality of urban residents' environment, and it was very important to obtain urban ecological land cover information. In this paper, an object-oriented method was proposed to extract urban ecological land cover from the multiple-channel images acquired by Chinese Gaofen-1 (GF-1) satellite. Taking Beijing City...
Many cities in developing countries lack detailed information on the emergence and growth of highly dynamic slum developments. Available statistical data are often aggregated to large administrative units that are heterogeneous and geographically rather meaningless in terms of pro-poor policy development. Such general base information neither allows a spatially disaggregated analysis of deprivations...
We consider the problem of estimating the relative depth of a scene from a monocular image. The dark channel prior, used as a statistical observation of haze free images, has been previously leveraged for haze removal and relative depth estimation tasks. However, as a local measure, it fails to account for higher order semantic relationship among scene elements. We propose a dual channel prior used...
A new image inpainting technique is developed to fit perfectly for special categories of images that contain mainly buildings. This technique handles the need to obtain an image of building free from parking cars along sides of the roads. To do that, one needs to carefully inpaint the roads and the missing parts of images. This can be done by combining vanishing points detections and image segmentation...
In the paper, we present an approach of road extraction in urban area by combining the Hough transform and region growing. In this case, we use Digital Surface Mode (DSM) data, which is based on the elevation of land surface, building, and so on to overcome the disadvantage of aerial photo image. The main problem in extracting the road in urban area from an aerial photo is the shadow cast by the buildings...
Detecting the spatial objects is an important research agenda for geospatial information science. Appling object-oriented image classification to extract GIS features, which fulfills the needs of updating the geospatial databases with remote sensing imagery will greatly enhance the ongoing digital city construction and national condition monitoring. This paper describes the key technology for high...
Accurate segmentation of sidewalks from satellite images can be required in various applications, for example giving walking directions to pedestrians and robot navigation. We propose a framework to construct sidewalk and crosswalk maps from satellite images. This is a challenging task, since typically sidewalks in satellite images are highly occluded by trees and their shadows and also there can...
In this paper we proposed the method for road extraction. The road extraction involves the two main steps: the detection of road that might have the other non road parts like buildings and parking lots followed by morphological operations to remove the non road parts based on their features. We used the K-Means clustering to detect the road area and may be some non road area. Morphological operations...
In this paper, we provide a method for image labeling by combining the local features and contextual cues in a multiple segmentation framework. Our main insight is to weight the classification results of each image region in different levels, which are obtained by a series of learned discriminative models based on bag of features. The contextual cues are implicitly embedded as feature selection in...
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