3D Geodata Research Articles

Use of Tree-Based Machine Learning Methods for Stratigraphic Classification in 3D Geological Modelling

The need for underground development has been expanded due to rapid urbanization and to reduce the negative impact on city living. In recent years, 3D geological modelling has been widely used by engineers and geo-scientists for desk study and has shown its capability of integrating geological and geotechnical information for better usage in building and civil engineering projects. To provide techniques and instruction(?), a 3D Geo-data Modelling and Management System (GeM2S) has been established to better understand the subsurface conditions in Singapore. However, the geological stratum between the existing boreholes is often not investigated, which brings the possibility of vital errors in underground design due to inaccurate interpretation of the ground conditions. It is desirable to utilize advanced machine learning methods to predict and update the 3D geo-models. Machine learning is regarded as a subset in the field of artificial intelligence, which has shown its rapid development recently. However, the accuracy of the model is one of the major concerns when the techniques are applied. In this study, four machine learning models are proposed to provide the solutions for stratigraphic classification, namely, Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (Catboost). The borehole data are located in the Bishan planning region in Singapore. The voxel-based input data for the models are spatial coordinates (X, Y, Z) and ground surface elevation. The prediction results demonstrate that LightGBM with optimization have produced the highest performance in this multi-classification problems. Finally, based on the prediction results of LightGBM, the voxel-based 3D geological models and the selected cross-sections are built for further analysis and comparison.

WHICH 3D DATA REPRESENTATION DOES THE CROWD LIKE BEST? CROWD-BASED ACTIVE LEARNING FOR COUPLED SEMANTIC SEGMENTATION OF POINT CLOUDS AND TEXTURED MESHES

Abstract. Semantic interpretation of multi-modal datasets is of great importance in many domains of geospatial data analysis. However, when training models for automated semantic segmentation, labeled training data is required and in case of multi-modality for each representation form of the scene. To completely avoid the time-consuming and cost-intensive involvement of an expert in the annotation procedure, we propose an Active Learning (AL) pipeline where a Random Forest classifier selects a subset of points sufficient for training and where necessary labels are received from the crowd. In this AL loop, we aim on coupled semantic segmentation of an Airborne Laser Scanning (ALS) point cloud and the corresponding 3D textured mesh generated from LiDAR data and imagery in a hybrid manner. Within this work we pursue two main objectives: i) We evaluate the performance of the AL pipeline applied to an ultra-high resolution ALS point cloud and a derived textured mesh (both benchmark datasets are available at https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx). ii) We investigate the capabilities of the crowd regarding interpretation of 3D geodata and observed that the crowd performs about 3 percentage points better when labeling meshes compared to point clouds. We additionally demonstrate that labels received solely by the crowd can power a machine learning system only differing in Overall Accuracy by less than 2 percentage points for the point cloud and less than 3 percentage points for the mesh, compared to using the completely labeled training pool. For deriving this sparse training set, we ask the crowd to label 0.25 % of available training points, resulting in costs of 190 $.

Statistical Interpolation of Spatially Varying but Sparsely Measured 3D Geo-Data Using Compressive Sensing and Variational Bayesian Inference

Real geo-data are three-dimensional (3D) and spatially varied, but measurements are often sparse due to time, resource, and/or technical constraints. In these cases, the quantities of interest at locations where measurements are missing must be interpolated from the available data. Several powerful methods have been developed to address this problem in real-world applications over the past several decades, such as two-point geo-statistical methods (e.g., kriging or Gaussian process regression, GPR) and multiple-point statistics (MPS). However, spatial interpolation remains challenging when the number of measurements is small because a suitable covariance function is difficult to select and the parameters are challenging to estimate from a small number of measurements. Note that a covariance function form and its parameters are key inputs for some methods (e.g., kriging or GPR). MPS is a non-parametric simulation method that combines training images as prior knowledge for sparse measurements. However, the selection of a suitable training image for continuous geo-quantities (e.g., soil or rock properties) faces certain difficulties and may become increasingly complicated when the geo-data to be interpolated are high-dimensional (e.g., 3D) and exhibit non-stationary (e.g., with unknown trends or non-stationary covariance structure) and/or anisotropic characteristics. This paper proposes a non-parametric approach that systematically combines compressive sensing and variational Bayesian inference for statistical interpolation of 3D geo-data. The method uses sparse measurements and their locations as the input and provides interpolated values at unsampled locations with quantified interpolation uncertainty as the output. The proposed method is illustrated using a series of numerical 3D examples, and the results indicate a reasonably good performance.

CENTIMETRE-ACCURACY IN FORESTS AND URBAN CANYONS – COMBINING A HIGH-PERFORMANCE IMAGE-BASED MOBILE MAPPING BACKPACK WITH NEW GEOREFERENCING METHODS

Abstract. Advances in digitalization technologies lead to rapid and massive changes in infrastructure management. New collaborative processes and workflows require detailed, accurate and up-to-date 3D geodata. Image-based web services with 3D measurement functionality, for example, transfer dangerous and costly inspection and measurement tasks from the field to the office workplace. In this contribution, we introduced an image-based backpack mobile mapping system and new georeferencing methods for capture previously inaccessible outdoor locations. We carried out large-scale performance investigations at two different test sites located in a city centre and in a forest area. We compared the performance of direct, SLAM-based and image-based georeferencing under demanding real-world conditions. Both test sites include areas with restricted GNSS reception, poor illumination, and uniform or ambiguous geometry, which create major challenges for reliable and accurate georeferencing. In our comparison of georeferencing methods, image-based georeferencing improved the median precision of coordinate measurement over direct georeferencing by a factor of 10–15 to 3 mm. Image-based georeferencing also showed a superior performance in terms of absolute accuracies with results in the range from 4.3 cm to 13.2 cm. Our investigations showed a great potential for complementing 3D image-based geospatial web-services of cities as well as for creating such web services for forest applications. In addition, such accurately georeferenced 3D imagery has an enormous potential for future visual localization and augmented reality applications.

USAGE OF GEODATA AND VIRTUAL REALITY IN THE MODERN SITUATION VISUALIZATION

Abstract. When a crisis arises, geographically referenced data is helpful in dealing with the situation. A map can provide a link between available data and stakeholders. It allows all participants to obtain an understandable model of the situation. For crisis management, hawse have developed an integrated system with an emphasis on ergonomics and data fusion. Visualization and interaction are tailored towards specific responsibilities as well as towards different devices such as smartphones, desktops and large displays. In addition to commonly available 2D geodata, highly accurate and up-to-date 3D geodata becomes more readily available. 3D geodata displayed on normal display devices, however, naturally lacks the third dimension. In order to gain a better understanding of the situation, we have added a virtual reality variant to its crisis management system. The VR environment allows meeting as a team, connecting local and remote participants, in the VR environment for a more efficient and natural discussion. In this VR meeting, all participants can work with the same geodata and annotations, providing a consistent view of the situation. To assess the additional value of 3D geodata and virtual reality visualization in this context, a user study was conducted comparing the performance using 2D geodata, 3D geodata and visualization in VR. The study provides insights into the task-specific value of 3D geodata and VR visualization.

EXTENSION AND CONTEXTUALISATION FOR LINKED SEMANTIC 3D GEODATA

Abstract. The application of the linked data principles provide a simple and effective way to publish and share data on the Web. The publication of geodata on the Web bas been studied and developed for a long time, leading to the creation of geo vocabularies and ontologies as well as geographic query languages. This is also true for 3D geodata, for which multiple RDF vocabularies and OWL ontologies exist (at the building, city, or territory level). In this paper we show that at least two issues remain to be addressed to reach a satisfactory publication framework for 3D linked geodata. First, the semantic model interconnection issue is not magically solved by the creation of ontologies, it requires the careful design of interconnection and extension schemes and their management. Second, the representation of additional dimensions, such as time, level of detail, confidence level, provenance, etc. cannot be directly handled in RDF. For these two issues we propose methodologies and design patterns and show how they can actually be used to create a semantically rich representation of 3D or nD linked geodata.

Mobile low-cost 3D camera maize crop height measurements under field conditions

To tackle global challenges such as food supply and renewable energy provision, the improvement of efficiency and productivity in agriculture is of high importance. Site-specific information about crop height plays an important role in reaching these goals. Crop height can be derived with a variety of approaches including the analysis of three-dimensional (3D) geodata. Crop height values derived from 3D geodata of maize (1.88 and 2.35 m average height) captured with a low-cost 3D camera were examined. Data were collected with a unique measurement setup including the mobile mounting of the 3D camera, and data acquisition under field conditions including wind and sunlight. Furthermore, the data were located in a global co-ordinate system with a straightforward approach, which can strongly reduce computational efforts and which can subsequently support near real-time data processing in the field. Based upon a comparison between crop height values derived from 3D geodata captured with the low-cost approach, and high-end terrestrial laser scanning reference data, minimum RMS and standard deviation values of 0.13 m (6.91% of average crop height), and maximum R2 values of 0.79 were achieved. It can be concluded that the crop height measurements derived from data captured with the introduced setup can provide valuable input for tasks such as biomass estimation. Overall, the setup is considered to be a valuable extension for agricultural machines which will provide complementary crop height measurements for various agricultural applications.

Modeling Turkey National 2D Geo-Data Model as a CityGML Application Domain Extension in UML

This paper presents the
 generation of the 3D national geo-data model of Turkey, which is compatible
 with the international OGC CityGML Encoding Standard. We prepare an ADE named CityGML-TRKBIS
 that is produced by extending existing thematic modules of CityGML according to
 TRKBIS needs. All thematic data groups in TRKBIS geo-data model have been
 remodeled in order to generate the national large scale 3D geo-data model for
 Turkey. Within the concept of reference model-driven framework for modelling
 CityGML ADEs in UML, the first step is conceptual mapping between CityGML and
 national information model. To test all stages of the framework for Urban GIS
 Turkey, Building data theme is mapped with CityGML-Building thematic model. All
 classes, attributes, code lists and code list values of the TRKBIS is tried to
 be mapped with related CityGML concept. New classes, attributes and code list
 values that will be added to CityGML Building model are determined based on
 this conceptual mapping. Finally the new model for CityGML-TRKBIS.BI is
 established in UML with subclassing of the related CityGML classes. This study
 provides new insights into 3D applications in Turkey. The generated 3D geo-data
 model for building thematic class will be used as a common exchange format that
 meets 2D, 2.5D and 3D implementation needs at national level. 
 

Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements.

BackgroundIn agriculture, information about the spatial distribution of crop height is valuable for applications such as biomass and yield estimation, or increasing field work efficiency in terms of fertilizing, applying pesticides, irrigation, etc. Established methods for capturing crop height often comprise restrictions in terms of cost and time efficiency, flexibility, and temporal and spatial resolution of measurements. Furthermore, crop height is mostly derived from a measurement of the bare terrain prior to plant growth and measurements of the crop surface when plants are growing, resulting in the need of multiple field campaigns. In our study, we examine a method to derive crop heights directly from data of a plot of full grown maize plants captured in a single field campaign. We assess continuous raster crop height models (CHMs) and individual plant heights derived from data collected with the low-cost 3D camera Microsoft® Kinect® for Xbox One™ based on a comprehensive comparison to terrestrial laser scanning (TLS) reference data.ResultsWe examine single measurements captured with the 3D camera and a combination of the single measurements, i.e. a combination of multiple perspectives. The quality of both CHMs, and individual plant heights is improved by combining the measurements. R2 of CHMs derived from single measurements range from 0.48 to 0.88, combining all measurements leads to an R2 of 0.89. In case of individual plant heights, an R2 of 0.98 is achieved for the combined measures (with R2 = 0.44 for the single measurements). The crop heights derived from the 3D camera measurements comprise an average underestimation of 0.06 m compared to TLS reference values.ConclusionWe recommend the combination of multiple low-cost 3D camera measurements, removal of measurement artefacts, and the inclusion of correction functions to improve the quality of crop height measurements. Operating low-cost 3D cameras under field conditions on agricultural machines or on autonomous platforms can offer time and cost efficient tools for capturing the spatial distribution of crop heights directly in the field and subsequently to advance agricultural efficiency and productivity. More general, all processes which include the 3D geometry of natural objects can profit from low-cost methods producing 3D geodata.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-016-0150-6) contains supplementary material, which is available to authorized users.

URBAN TREE CLASSIFICATION USING FULL-WAVEFORM AIRBORNE LASER SCANNING

Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

URBAN TREE CLASSIFICATION USING FULL-WAVEFORM AIRBORNE LASER SCANNING

Abstract. Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

Concepts of 3D terrain modeling and geomorphometric analysis in mining

This paper describes the concepts of 3D terrain models and their application in mining. The methods of terrain (relief) by spots elevation and contour lines are commonly applied on geodetic plans and topographic maps. Appearance of the technologies has changed the way of modeling and analysis of geospatial data, i.e., applying the concept of digital terrain models (DTM). This approach emphasizes the importance of geomorphometric analyses and monitoring changes on the field in time. Application of 3D geodata models in digital format (raster or vector) becomes increasingly used in mining. Therefore, it is important to describe the concepts and features of 3D geodata models and potential applications and monitoring changes in the field in time and space.

Methods of laser scanning point clouds integration in precise 3D building modelling

3D city models are built on the basis of photogrammetric data, especially laser scanning (LS) data. Due to the increasing levels of accuracy required of 3D city models, terrestrial and airborne laser scanning are the most frequently used technologies. The models are often fragmentary, based on only one type of geodata, e.g. aerial photography or airborne laser scanning (ALS) data. This paper presents various aspects of integrating data from different laser scanning technologies (terrestrial and airborne), which are at the moment considered to be the most detailed and accurate systems for the direct acquiring of 3D geodata. The main issue discussed in the paper is the integration of data in the form of point clouds acquiring from different laser scanning systems. Integration will be understood here as an appropriate processing of both sets of data for orientation into a uniform spatial coordinate system. Thus, the problem of extracting characteristic points common for both sets, in a way which will allow for obtaining a basis for building a complete and accurate 3D city model, will be fundamental. The main challenge when performing this task is to correctly and accurately specify the homologous point pairs used in 3D transformation when both data sets characterise different accuracy, density and content. In the present paper, manual (direct), semi-automatic (indirect determination of characteristic points by approximation) and automatic (3D model) methods for extracting characteristic points are presented, along with the results of an accuracy assessment. Furthermore different transformation methods (isometric, conformal and affine) are also verified, compared and analysed.

Image-based styling

The same data can be visualized using various visual styles that each is suitable for specific requirements, e.g., 3D geodata visualized using photorealistic, cartographic, or illustrative styles. In contrast to feature-based styling, image-based styling performed in image space at image resolution allows decoupling styling from image generation and output-sensitive, expressive styling. However, leveraging image-based styling is still impeded. No previous approach allows specifying image-based styling expressively with an extensive inventory of composable operators, while providing styling functionality in a service-oriented, interoperable manner. In this article, we present an interactive system for specifying and providing the functionality of image-based styling. As key characteristics, it separates concerns of styling from image generation and facilitates specifying styling as algebraic compositions of high-level operators using a unified 3D model representation. We propose a generalized visualization model, an image-based styling algebra, two declarative DSLs, an operator taxonomy, an operational model, and a standards-based service interface. The approach facilitates expressive specifications of image-based styling for design, description, and analysis and leveraging the functionality of image-based styling in a service-oriented, interoperable, reusable, and composable manner.

Visualisation of the 3D Geodata Models and Their Application

This paper describes the 3D geodata models and their application. On geodetic plans and topographic maps commonly applied methods of terrain (relief) by spots elevation and contour lines. However, with the advent of new technologies the way of the visualisation is changing and highlights the concept 3D geodata model. Namely, there are different concepts: digital elevation model (DEM), digital terrain model (DTM), digital surface model (DSP) and so on. Infrastructure and 3D geodata models are standardized, while the visualization and details of information change and adjust the needs and aspect ratio display. Application of 3D geodata models in digital format (raster or vector) is becoming increasingly topical over the internet. Therefore, it is important to distinguish between certain concepts and features of 3D geodata models and the possibility of their application.

Effects of reduced terrestrial LiDAR point density on high-resolution grain crop surface models in precision agriculture.

3D geodata play an increasingly important role in precision agriculture, e.g., for modeling in-field variations of grain crop features such as height or biomass. A common data capturing method is LiDAR, which often requires expensive equipment and produces large datasets. This study contributes to the improvement of 3D geodata capturing efficiency by assessing the effect of reduced scanning resolution on crop surface models (CSMs). The analysis is based on high-end LiDAR point clouds of grain crop fields of different varieties (rye and wheat) and nitrogen fertilization stages (100%, 50%, 10%). Lower scanning resolutions are simulated by keeping every n-th laser beam with increasing step widths n. For each iteration step, high-resolution CSMs (0.01 m2 cells) are derived and assessed regarding their coverage relative to a seamless CSM derived from the original point cloud, standard deviation of elevation and mean elevation. Reducing the resolution to, e.g., 25% still leads to a coverage of >90% and a mean CSM elevation of >96% of measured crop height. CSM types (maximum elevation or 90th-percentile elevation) react differently to reduced scanning resolutions in different crops (variety, density). The results can help to assess the trade-off between CSM quality and minimum requirements regarding equipment and capturing set-up.

Performance study of various spatial indexes on 3D geo-data in Geo-RDBMS

In the emerging era of information and communication technologies, geotechnology is one of the fastest growing fields. Geo-RDBMS is very important and evolving aspect for GIS, as it can manage large volume of spatial data inside RDBMS. The utilization of RDBMS for geospatial data was one of the important focuses of GIS professionals in last decades to store and manage 2D geo-data. However, the support for 3D geo-data inside RDBMS is still limited and is a challenging task for RDBMS providers. In this study, data organization and performance assessment of 3D geo-data inside RDBMS are carried out. In this process, various file-based 3D data models such as CityGML, COLLADA and KML are migrated to geo-RDBMS to bring entire 3D geo-data in common platform. Various spatial indexing techniques viz. R-Tree, B-Tree, GiST, etc. are applied on these 3D data models and best indexing techniques are studied for 3D GIS operations.

Towards a 3D Spatial Urban Energy Modelling Approach

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

A Standard-based Model for the Exchange of 3D Geological Data

A wide range of methodologies and software applications have been developed to generate 3-Dimensional (3D) geological model data. One of the main open issues is the exchange of these heterogeneous data from different sources, which influences the sharing and integration of 3D geodata. It is also a necessity to take into consideration the semantics of 3D geological data to enable its full exchange. A standard based geologic data model (sGDM) is proposed to offer an xml-based data exchange of 3D geological models. sGDM totally reuses the standard of GeoSciML declared by the International Union of Geological Sciences (IUGS) as the core module for defining geological feature objects. Then geological features can be integrated into a geological model within a top-to-down architecture of geological model – feature group – geological feature. The Symbology Encoding (SE) standard from Open Geospatial Consortium (OGC) is utilized and extended by sGDM to define 3D visualization parameters of geological features. Besides, sGDM has benefited from several other existing standards, such as X3D proposed by Web 3D Consortium, WMS, SLD, CityGML etc. that proposed by OGC. To evaluate the sGDM, we also presented a high level prototype architecture for the integrated, xml-based storage and management of 3D geological data. In this architecture, data user and provider can exchange information through a communication method using sGDM-defined xml streams.

TOWARDS A 3D SPATIAL URBAN ENERGY MODELLING APPROACH

Abstract. Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.