Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI

Vectorization process focus on grouping pixels of a raster image into raw line segments, and forming lines, polylines or poligons. To vectorize massive raster images regarding resource and performane problems, weuse a distributed HIPI image processing interface based on MapReduce approach. Apache Hadoop is placed at the core of the framework. To realize such a system, we first define mapper function, and then its input and output formats. In this paper, mappers convert raster mosaics into vector counterparts. Reduc functions are not needed for vectorization. Vector representations of raster images is expected to give better performance in distributed computations by reducing the negative effects of bandwidth problem and horizontal scalability analysis is done.Comment: In Turkish, Proceedings of International Artificial Intelligence and Data Processing Symposium (IDAP) 201

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results

Advanced considerations in LiDAR technology : application enhancement, inspection workflow implementation and data collection quality management

Bridge inspection is a critical topic in infrastructure management and is facing unprecedented challenges as the public is concerned more about bridge safety after a series of bridge failures. LiDAR based remote sensing is recommended as a way in supplementing the prevailing visual inspection to quantify critical bridge information. In this research, focus will be placed on the advanced considerations of LiDAR technology in bridge inspection, including the application evaluation, inspection workflow implementation, and data collection quality management. Particularly, efforts on improving the computational performance of the original damage detection algorithm have been carried out and the use of reflectivity data is introduced as a new feature to enhance the algorithm’s capability in defect recognition. The specific applications that using LiDAR technology to evaluate bridge deck joint and monitoring simulated slope erosion have been studied. This research further studied the inspection workflow implementation and the sources of errors in the LiDAR bridge inspection. Quality management has also been considered to improve the bridge inspection data quality besides the development of advanced inspection technology. In the end, comparative cost analysis is conducted to determine the savings for implementing LiDAR technology into bridge inspection workflow

Terrestrial LiDAR-based bridge evaluation

Considering the over half million bridges in the US state highway system, more than 70% of which were built before 1935, it is of little wonder that bridge maintenance and management is facing severe challenges and the significant funding scarcity rapidly escalates the problem. Commercial remote sensing techniques have the capability of covering large areas and are suggested to be cost effective methods for bridge inspection. This dissertation introduces several applications of the remote bridge inspection technologies using ground-based LiDAR systems. In particular, the application of terrestrial LiDAR for bridge health monitoring is studied. An automatic bridge condition evaluation system based on terrestrial LiDAR data, LiBE (LiDAR-based Bridge Evaluation), is developed. The research works completed thus far have shown that LiDAR technology has the capability for bridge surface defect detection and quantification, clearance measurement, and displacement measurement during bridge static load testing. Several bridges in Mecklenburg County, NC, and other areas have been evaluated using LiBE and quantitative bridge rating mechanisms are proposed. A cost-benefit analysis has been conducted that demonstrates the relevancy of the technique to current nation-wide bridge management problem, as well as, the potential of reducing the bridge maintenance costs to the stack holders. The results generated from these technologies are valuable for bridge maintenance decision making

An Automatic Bridge Detection Technique for Multispectral Images

Extraction of features from images has been a goal of researchers since the early days of remote sensing. While significant progress has been made in several applications, much remains to be done in the area of accurate identification of high-level features such as buildings and roads. This paper presents an approach for detecting bridges over water bodies from multispectral imagery. The multispectral image is first classified into eight land-cover types using a majority-must-be-granted logic based on the multiseed supervised classification technique. The classified image is then categorized into a trilevel image: water, concrete, and background. Bridges are then recognized in this trilevel image by using a knowledge-based approach that exploits the spatial arrangement of bridges and their surroundings using a five-step approach. A river extraction module identifies the rivers using a recursive scanning technique and geometric constraints. Using a neighborhood operator and the knowledge of the spatial dimensions of a typical bridge, we identify the possible bridge pixels. These potential bridge pixels are then grouped into possible bridge segments based on their connectivity and geometric properties. Finally, these bridge segments are verified on the basis of directional water index along different directions and their connectivity with the road segments. The approach proposed in this paper has been implemented and tested with images from the IRS-1C/1-D satellite that has a spatial resolution of 23.5 × 23.5 m. The results show that this approach is both efficient and effective in extracting bridges