Lutz Plümer

CityGML: Interoperable Access to 3D City Models

Virtual 3D city models provide important information for different aspects of disaster management. In this context, up-to-dateness of and flexible access to 3D city models are of utmost importance. Spatial Data Infrastructures (SDI) provide the appropriate framework to cover both aspects, integrating distributed data sources on demand. In this paper we present CityGML, a multi-purpose and multi-scale representation for the storage of and interoperable access to 3D city models in SDIs. CityGML is based on the standard GML3 of the Open Geospatial Consortium and covers the geometrical, topological, and semantic aspects of 3D city models. The class taxonomy distinguishes between buildings and other man-made artifacts, vegetation objects, waterbodies, and transportation facilities like streets and railways. Spatial as well as semantic properties are structured in five consecutive levels of detail. Throughout the paper, special focus is on the utilization of model concepts with respect to different tasks in disaster management.

Extracting Buildings from Aerial Images Using Hierarchical Aggregation in 2D and 3D

We propose a model-based approach to automated 3D extraction of buildings from aerial images. We focus on a reconstruction strategy that is not restricted to a small class of buildings. Therefore, we employ a generic modeling approach which relies on the well-defined combination of building part models. Building parts are classified by their roof type. Starting from low-level image features we combine data-driven and model-driven processes within a multilevel aggregation hierarchy, thereby using a tight coupling of 2D image and 3D object modeling and processing, ending up in complex 3D building estimations of shape and location. Due to the explicit representation of well-defined processing states in terms of model-based 2D and 3D descriptions at all levels of modeling and data aggregation, our approach reveals a great potential for reliable building extraction.

Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping

Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor is able to replace an expensive laser scanner in many plant phenotyping scenarios.

Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis

Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisation technique, simplex volume maximisation (SiVM), to hyperspectral data. It is an unsupervised classification approach, optimised for fast computation of massive datasets. It allows calculation of how similar each spectrum is to observed typical spectra. This provides the means to express how likely it is that one plant is suffering from stress. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter experiment and to agricultural corn plots subjected to a two factorial field setup altering water and nutrient availability. Both experiments were conducted on the canopy level. SiVM was significantly better than using a combination of established vegetation indices. In the corn plots, SiVM clearly separated the different treatments, even though the effects on leaf and canopy traits were subtle.

How to achieve consistency for 3D city models

Consistency is a crucial prerequisite for a large number of relevant applications of 3D city models, which have become more and more important in GIS. Users need efficient and reliable consistency checking tools in order to be able to assess the suitability of spatial data for their applications. In this paper we provide the theoretical foundations for such tools by defining an axiomatic characterization of 3D city models. These axioms are effective and efficiently supported by recent spatial database management systems and methods of Computational Geometry or Computer Graphics. They are equivalent to the topological concept of the 3D city model presented in this paper, thereby guaranteeing the reliability of the method. Hence, each error is detected by the axioms, and each violation of the axioms is in fact an error. This property, which is proven formally, is not guaranteed by existing approaches. The efficiency of the method stems from its locality: in most cases, consistency checks can safely be restricted to single components, which are defined topologically. We show how a 3D city model can be decomposed into such components which are either topologically equivalent to a disk, a sphere, or a torus, enabling the modeling of the terrain, of buildings and other constructions, and of bridges and tunnels, which are handles from a mathematical point of view. This enables a modular design of the axioms by defining axioms for each topological component and for the aggregation of the components. Finally, a sound, consistent concept for aggregating features, i.e. semantical objects like buildings or rooms, to complex features is presented.

Models for photogrammetric building reconstruction

Abstract The paper discusses the modeling necessary for recovering man made objects—in this case buildings—in complex scenes from digital imagery. The approach addresses all levels of image analysis for deriving semantically meaningful descriptions of the scene from the image, via the geometrical/physical model of the objects and their counterparts in the image. The central link between raster image and scene are network-like organized aspects of parts of the objects. This is achieved by generically modeling the objects using parametrized volume primitives together with the application-specific constraints, which seems to be adequate for many types of buildings. The paper sketches the various interrelationships between the different models and their use for feature extraction, hypothesis generation, and verification.

A review of advanced machine learning methods for the detection of biotic stress in precision crop protection

Effective crop protection requires early and accurate detection of biotic stress. In recent years, remarkable results have been achieved in the early detection of weeds, plant diseases and insect pests in crops. These achievements are related both to the development of non-invasive, high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. Several methods of machine learning have been utilized for precision agriculture such as support vector machines and neural networks for classification (supervised learning); k-means and self-organizing maps for clustering (unsupervised learning). These methods are able to calculate both linear and non-linear models, require few statistical assumptions and adapt flexibly to a wide range of data characteristics. Successful applications include the early detection of plant diseases based on spectral features and weed detection based on shape descriptors with supervised or unsupervised learning methods. This review gives a short introduction into machine learning, analyses its potential for precision crop protection and provides an overview of instructive examples from different fields of precision agriculture.

Achieving Integrity in Geographic Information Systems—Maps and Nested Maps

This article provides a formal data model which allows to establish geometrical-topological integrity of areal objects in a geographical information system (GIS). The data model leads to an automatic tool able to check consistency of a given set of data and to avoid inconsistencies caused by updates of the database. To this end we start from the mathematical notion of a map which provides an irregular tessellation, i.e., a partition of the plane which is non-overlapping and covering. From another perspective, a map is a plane graph with an explicit representation of faces as its atomic areal components. The concept of nested maps extends this standard notion by the specification of a hierarchical structure which aggregates the set of faces. Such aggregations are common in political and administrative structures. Whereas the mathematical notion of a map is familiar in GIS and the base for many tools supporting topological editing, there was a lack of effectively checkable integrity constraints which are correct and complete, i.e., equivalent, for maps. This article provides an axiomatic, effectively checkable characterization of maps which is equivalent to the standard mathematical one, extends it to nested maps and discusses how to use them in order to achieve and maintain integrity in a GIS.

How to Get 3-D for the Price of 2-D--Topology and Consistency of 3-D Urban GIS

This article deals with topological concepts and models which are necessary to represent three-dimensional urban objects in a geographical information system (GIS). Depending on the shape and the representation of features, several classes with increasing topological complexity are identified and described. This complexity has strong impacts on the models and tools which are required to represent, manage and edit the data. One specific model we call ‘2.8-D map’ is identified, which covers many 3-D applications in GIS. It is a slight extension of a 2-D or 2.5-D model and preserves the algorithmic and conceptual simplicity of the 2-D case as much as possible. The model is described in a formal way. Integrity axioms are given, which detect errors in corresponding data sets safely and guarantee the consistency of 2.8-D maps in a mathematically sound and provable way. These axioms are effectively and efficiently checkable by automatic procedures. The model extends digital terrain models (2.5-D) by allowing for vertical walls and projections like balconies or ledges. The conceptual simplicity is due to the two-dimensional topology of the model. Thus bridges and tunnels are special cases; it is shown how to detect and handle these cases efficiently. Based on this model, thematic objects and their aggregation structures are defined in a consistent way.

Automatic classification of building types in 3D city models

This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data.