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.

Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of 100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

No abstract

In recent times mobile laser scanning (MLS) has been used to acquire massive 3D point clouds in urban areas and along road corridors for the collection of detailed data for 3D city modelling, building façade reconstruction and capture of vegetation and road features for inventories. The objectives of this paper are the extraction of tree features from such data‐sets and the modelling of trees for the purpose of visualisation in 3D city models. After the detection of high vegetation the point cloud is reduced using a 3D alpha shape approach. Then the required model parameters such as crown and stem height, crown and stem diameter, and crown shape are derived and the trees are modelled individually in a realistic manner. The tree model so generated correctly represents the overall appearance of the tree. However, the inner structure such as the branching of the tree crown is parameterised. The workflow reduces the point cloud by means of a step‐by‐step process, which eases the handling of the massive MLS data‐sets. The thinning using 3D alpha shapes reduces the amount of data to be processed by about 95%. It is shown that the model parameters are not influenced by the thinning procedure employed. This proves the robustness of the data reduction method and the tree modelling approach.