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O. Veselý
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Despite being relatively novel, generative adversarial networks (GAN) have already been appropriated for application to several problems within the field of architectural and urban generative design. However, the preceding GAN based models for building massing generation make use of only simplified and two dimensional representation of the built environment.
This work improves upon the existing deep-learning-based methods for generation of building massings and building group layouts, by fusing high accuracy three-dimensional building models with site context derived from cadastral and topographic data, sourced from openly available datasets in the Netherlands. Pix2pixGAN implementation in PyTorch, trained on existing massing data encoded into images as heightmaps, is used to generate building massing geometry. Two methods for geometry extraction from heightmaps are introduced, voxelization and vectorization. The goal for the model is to maximize similarity of morphological traits of configurations generated by the model to the ground truth training data. The effects of multiple proposed training configurations on the resulting massings generated by the model are evaluated, together with visual assessment, using their Spacematrix mappings.
Three distinct models with specific goals are presented - parcel infill model, street block infill model, and urban fabric infill model. All three models show a capability to learn spatial traits of existing building configurations and transfer them into new situations not encountered in the training data, which is confirmed by the distribution of Spacematrix mapping of the generated results being similar to the distributions of the ground truth data.
The proposed methodology represents a novel approach to generating building massing configurations by autonomously inferring the rules of their composition from existing urban areas. The resulting models could be used to provide initial states in optimization-driven design approaches, or as smart massing suggestion engines, assisting architects and city planners during the early building design process. ...
This work improves upon the existing deep-learning-based methods for generation of building massings and building group layouts, by fusing high accuracy three-dimensional building models with site context derived from cadastral and topographic data, sourced from openly available datasets in the Netherlands. Pix2pixGAN implementation in PyTorch, trained on existing massing data encoded into images as heightmaps, is used to generate building massing geometry. Two methods for geometry extraction from heightmaps are introduced, voxelization and vectorization. The goal for the model is to maximize similarity of morphological traits of configurations generated by the model to the ground truth training data. The effects of multiple proposed training configurations on the resulting massings generated by the model are evaluated, together with visual assessment, using their Spacematrix mappings.
Three distinct models with specific goals are presented - parcel infill model, street block infill model, and urban fabric infill model. All three models show a capability to learn spatial traits of existing building configurations and transfer them into new situations not encountered in the training data, which is confirmed by the distribution of Spacematrix mapping of the generated results being similar to the distributions of the ground truth data.
The proposed methodology represents a novel approach to generating building massing configurations by autonomously inferring the rules of their composition from existing urban areas. The resulting models could be used to provide initial states in optimization-driven design approaches, or as smart massing suggestion engines, assisting architects and city planners during the early building design process. ...
Despite being relatively novel, generative adversarial networks (GAN) have already been appropriated for application to several problems within the field of architectural and urban generative design. However, the preceding GAN based models for building massing generation make use of only simplified and two dimensional representation of the built environment.
This work improves upon the existing deep-learning-based methods for generation of building massings and building group layouts, by fusing high accuracy three-dimensional building models with site context derived from cadastral and topographic data, sourced from openly available datasets in the Netherlands. Pix2pixGAN implementation in PyTorch, trained on existing massing data encoded into images as heightmaps, is used to generate building massing geometry. Two methods for geometry extraction from heightmaps are introduced, voxelization and vectorization. The goal for the model is to maximize similarity of morphological traits of configurations generated by the model to the ground truth training data. The effects of multiple proposed training configurations on the resulting massings generated by the model are evaluated, together with visual assessment, using their Spacematrix mappings.
Three distinct models with specific goals are presented - parcel infill model, street block infill model, and urban fabric infill model. All three models show a capability to learn spatial traits of existing building configurations and transfer them into new situations not encountered in the training data, which is confirmed by the distribution of Spacematrix mapping of the generated results being similar to the distributions of the ground truth data.
The proposed methodology represents a novel approach to generating building massing configurations by autonomously inferring the rules of their composition from existing urban areas. The resulting models could be used to provide initial states in optimization-driven design approaches, or as smart massing suggestion engines, assisting architects and city planners during the early building design process.
This work improves upon the existing deep-learning-based methods for generation of building massings and building group layouts, by fusing high accuracy three-dimensional building models with site context derived from cadastral and topographic data, sourced from openly available datasets in the Netherlands. Pix2pixGAN implementation in PyTorch, trained on existing massing data encoded into images as heightmaps, is used to generate building massing geometry. Two methods for geometry extraction from heightmaps are introduced, voxelization and vectorization. The goal for the model is to maximize similarity of morphological traits of configurations generated by the model to the ground truth training data. The effects of multiple proposed training configurations on the resulting massings generated by the model are evaluated, together with visual assessment, using their Spacematrix mappings.
Three distinct models with specific goals are presented - parcel infill model, street block infill model, and urban fabric infill model. All three models show a capability to learn spatial traits of existing building configurations and transfer them into new situations not encountered in the training data, which is confirmed by the distribution of Spacematrix mapping of the generated results being similar to the distributions of the ground truth data.
The proposed methodology represents a novel approach to generating building massing configurations by autonomously inferring the rules of their composition from existing urban areas. The resulting models could be used to provide initial states in optimization-driven design approaches, or as smart massing suggestion engines, assisting architects and city planners during the early building design process.
Led in cooperation with the company Brink, who provides management and consultation services for construction and real estate sectors, this Synthesis Project aims at automatically deriving meaningful information about buildings. More precisely, the focus is to automatically detect roof obstacles - such as dormers, chimneys, and solar panels - to be able to determine the available roof surface for new solar panel installation, and therefore to perform more accurate solar potential analysis. For this purpose, three different methods are developed and implemented to increase the results’ accuracy, which are geometry-based, unsupervised, and supervised classification. While AHN3 point cloud and 3D BAG Level of Detail (LoD) 2.2 building models are used for the geometry-based classification, the input data of the unsupervised image classification consists of aerial images and BAG footprints. Finally, supervised image classification method makes use of the aerial images as well as the BAG footprints and a dataset of manually labelled solar panel polygons. The results show that the accuracy of individual methods is not sufficient; therefore, the outputs of all three methods are merged together into one pipeline, with the aim of obtaining one final end product. The latter is the 3D BAG LoD2.2 building model in CityJSON format, enhanced with three new attributes per building: the obstacle area on the roof, the available area for installing solar panels, and a Boolean value showing whether the building has existing solar panels or not. Additionally, an enhanced point cloud for future use is generated, with a new attribute per point indicating its distance to the 3D model and therefore its potential for being an obstacle or not. The assessment of the results with the ground truth illustrates that the algorithm gives promising results; however, the scope of the project can be broadened, and improvements can be made to increase the accuracy as well as the efficiency.
...
Led in cooperation with the company Brink, who provides management and consultation services for construction and real estate sectors, this Synthesis Project aims at automatically deriving meaningful information about buildings. More precisely, the focus is to automatically detect roof obstacles - such as dormers, chimneys, and solar panels - to be able to determine the available roof surface for new solar panel installation, and therefore to perform more accurate solar potential analysis. For this purpose, three different methods are developed and implemented to increase the results’ accuracy, which are geometry-based, unsupervised, and supervised classification. While AHN3 point cloud and 3D BAG Level of Detail (LoD) 2.2 building models are used for the geometry-based classification, the input data of the unsupervised image classification consists of aerial images and BAG footprints. Finally, supervised image classification method makes use of the aerial images as well as the BAG footprints and a dataset of manually labelled solar panel polygons. The results show that the accuracy of individual methods is not sufficient; therefore, the outputs of all three methods are merged together into one pipeline, with the aim of obtaining one final end product. The latter is the 3D BAG LoD2.2 building model in CityJSON format, enhanced with three new attributes per building: the obstacle area on the roof, the available area for installing solar panels, and a Boolean value showing whether the building has existing solar panels or not. Additionally, an enhanced point cloud for future use is generated, with a new attribute per point indicating its distance to the 3D model and therefore its potential for being an obstacle or not. The assessment of the results with the ground truth illustrates that the algorithm gives promising results; however, the scope of the project can be broadened, and improvements can be made to increase the accuracy as well as the efficiency.