Dr. Carlos H. Caldas Profile

Dr. Carlos H. Caldas

Assistant Professor at Univ of Texas at Austin

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SPIE Journal Paper | 7 April 2020

Framework for network-level pavement condition assessment using remote sensing data mining

Stefanos Politis, Zhanmin Zhang, Sareh Kouchaki, Carlos Caldas

JARS, Vol. 14, Issue 02, 024504, (April 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.024504

KEYWORDS: Image segmentation, Remote sensing, Data modeling, Roads, Multispectral imaging, Data mining, Satellites, Inspection, Sensors, Reflectivity

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Pavement condition monitoring is fundamental for the efficient allocation of resources in transportation asset management. However, data collection involves laborious and costly procedures. Our study intends to investigate the usage of remote sensing data for network-level pavement condition assessment that offers a more cost-effective alternative and a rapid infrastructure assessment tool that can be used in the aftermath of natural disasters. Based on an extensive literature review, a data mining framework was established to train models that predict the pavement condition of different road segments. The framework exploits the inherent information of multispectral images by generating spectral related attributes. To identify pavement sampling areas, an automated procedure using image segmentation replaces manual surface digitizing. Unlike previous research, different classification models were used to approximate the mapping function from spectral information to pavement conditions. A preliminary case study was conducted with data provided by the City of Dallas and multispectral images acquired from the Texas Natural Resources Information System. The mean-shift segmentation algorithm was used to locate noise introducing areas on the pavement surface. Four different classification models were trained using k-nearest neighbors, naïve Bayes, support vector machines, and a multilayer perceptron. The developed models were employed to predict the road surface condition class of a test set not included in the training procedure. The multilayer perceptron presented the highest accuracy level of 71%, showing that the framework might have the potential for future implementation.

Proceedings Article | 11 April 2006 Paper

Modeling job sites in real time to improve safety during equipment operation

Carlos Caldas, Carl Haas, Katherine Liapi, Jochen Teizer

Proceedings Volume 6174, 61743K (2006) https://doi.org/10.1117/12.657939

KEYWORDS: 3D modeling, LIDAR, Clouds, Instrument modeling, Safety, Data acquisition, Sensors, Data modeling, 3D acquisition, Safety equipment

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Real-time three-dimensional (3D) modeling of work zones has received an increasing interest to perform equipment operation faster, safer and more precisely. In addition, hazardous job site environment like they exist on construction sites ask for new devices which can rapidly and actively model static and dynamic objects. Flash LADAR (Laser Detection and Ranging) cameras are one of the recent technology developments which allow rapid spatial data acquisition of scenes. Algorithms that can process and interpret the output of such enabling technologies into threedimensional models have the potential to significantly improve work processes. One particular important application is modeling the location and path of objects in the trajectory of heavy construction equipment navigation. Detecting and mapping people, materials and equipment into a three-dimensional computer model allows analyzing the location, path, and can limit or restrict access to hazardous areas. This paper presents experiments and results of a real-time three-dimensional modeling technique to detect static and moving objects within the field of view of a high-frame update rate laser range scanning device. Applications related to heavy equipment operations on transportation and construction job sites are specified.

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