In this paper, different methods for the evaluation of building detection algorithms are compared. Whereas pixel-based evaluation gives estimates of the area that is correctly classified, the results are distorted by errors at the building outlines. These distortions are potentially in an order of 30%. Object-based evaluation techniques are less affected by such errors. However, the performance metrics thus delivered are sometimes considered to be less objective, because the definition of a "correct detection" is not unique. Based on a critical review of existing performance metrics, selected methods for the evaluation of building detection results are presented. These methods are used to evaluate the results of two different building detection algorithms in two test sites. A comparison of the evaluation techniques shows that they highlight different properties of the building detection results. As a consequence, a comprehensive evaluation strategy involving quality metrics derived by different methods is proposed.
ABSTRACT:For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.
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