Department of Infrastructure Engineering,

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

ABSTRACT: Automatic building detection has been a hot topic since the early 1990's. Early approaches were based on a single aerial image. Detecting buildings is a difficult task so it can be more effective when multiple sources of information are obtained and fused. The objective of this paper is to provide a comparative analysis of automatic approaches to building detection from multi-source aerial images. We analysed data related to both urban and suburban areas and took into consideration both object-based and pixel-based ...

ABSTRACT Identifying damaged buildings after natural disasters such as earthquake is important for the planning of recovery actions. We present a segment-based approach to classifying damaged building roofs in aerial laser scanning data. A challenge in the supervised classification of point segments is the generation of training samples, which is difficult because of the complexity of interpreting point clouds. We evaluate the performance of three different classifiers trained with a small set of training samples and show that feature selection improves the training and the accuracy of the resulting classification. When trained with 50 training samples, a linear discriminant classifier using a subset of six features reaches a classification accuracy of 85%.

Abstract A global registration is often insufficient for estimating dendrometric characteristics of trees because individual branches of the same tree may exhibit different positions between two scanning procedures. Therefore, we introduce a localized approach to register point clouds of botanic trees. Given two roughly registered point clouds $ hbox {PC} _ {1} $ and $ hbox {PC} _ {2} $ of a tree, we apply a skeletonization method to both point clouds. Based on these two skeletons, initial correspondences between branch segments of both ...

ABSTRACT Canopy Height Models (CHMs) or normalized Digital Surface Models (nDSM) derived from LiDAR data have been applied to extract relevant forest inventory information. However, generating a CHM by height normalizing the raw LiDAR points is challenging if trees are located on complex terrain. On steep slopes, the raw elevation values located on either the downhill or the uphill part of a tree crown are height-normalized with parts of the digital terrain model that may be much lower or higher than the tree stem base, respectively. In treetop detection, a highest crown return located in the downhill part may prove to be a ''false'' local maximum that is distant from the true treetop. Based on this observation, we theoretically and experimentally quantify the effect of slope on the accuracy of treetop detection. The theoretical model presented a systematic horizontal displacement of treetops that causes tree height to be systematically displaced as a function of terrain slope and tree crown radius. Interestingly, our experimental results showed that the effect of CHM distortion on treetop displacement depends not only on the steep-ness of the slope but more importantly on the crown shape, which is species-dependent. The influence of the systematic error was significant for Scots pine, which has an irregular crown pattern and weak apical dominance, but not for mountain pine, which has a narrow conical crown with a distinct apex. Based on our findings, we suggest that in order to minimize the negative effect of steep slopes on the CHM, especially in heterogeneous forest with multiple species or species which change their morphological characteristics as they mature, it is best to use raw elevation values (i.e., use the un-normalized DSM) and compute the height after treetop detection.