Data visualization within urban models

Models of urban environments have many uses for town planning, pre-visualization of new building work and utility service planning. Many of these models are three-dimensional, and increasingly there is a move towards real-time presentation of such large models. In this paper we present an algorithm for generating consistent 3D models from a combination of data sources, including Ordnance Survey ground plans, aerial photography and laser height data. Although there have been several demonstrations of automatic generation of building models from 2D vector map data, in this paper we present a very robust solution that generates models that are suitable for real-time presentation. We then demonstrate a novel pollution visualization that uses these models

Information Rich 3D Computer Modeling of Urban Environments

We are living in an increasingly information rich society. Geographical Information Systems now allow us to precisely tag information to specific features, objects and locations. The Internet is enabling much of this information to be accessed by a whole spectrum of users. At CASA we are attempting to push this technology towards a three-dimensional GIS, that works across the Internet and can represent significant chunks of a large city. We believe that the range of possible uses for such technology is diverse, although we feel that urban planning is an area that can benefit greatly. An opportunity to push this “planning technology” arose when CASA won a tender from Hackney Council to develop a dynamic website for community participation in the process of regenerating the Woodberry Down Estate. This is a run down part of northeast London that is undergoing a major redevelopment. CASA has developed a system that not only informs the local residents about the redevelopment process but it also enables them to use dynamic visualisations of the “before and after effects” of different plans, and then to discuss and vote on the variety of options

Reconstructing historical 3D city models

Historical maps are increasingly used for studying how cities have evolved over time, and their applications are multiple: understanding past outbreaks, urban morphology, economy, etc. However, these maps are usually scans of older paper maps, and they are therefore restricted to two dimensions. We investigate in this paper how historical maps can be ‘augmented’ with the third dimension so that buildings have heights, volumes, and roof shapes. The resulting 3D city models, also known as digital twins, have several benefits in practice since it is known that some spatial analyses are only possible in 3D: visibility studies, wind flow analyses, population estimation, etc. At this moment, reconstructing historical models is (mostly) a manual and very time-consuming operation, and it is plagued by inaccuracies in the 2D maps. In this paper, we present a new methodology to reconstruct 3D buildings from historical maps, we developed it with the aim of automating the process as much as possible, and we discuss the engineering decisions we made when implementing it. Our methodology uses extra datasets for height extraction, reuses the 3D models of buildings that still exist, and infers other buildings with procedural modelling. We have implemented and tested our methodology with real-world historical maps of European cities for different times between 1700 and 2000

Building extraction for 3D city modelling using airborne laser scanning data and high-resolution aerial photo

Light detection and ranging (LiDAR) technology has become a standard tool for three-dimensional mapping because it offers fast rate of data acquisition with unprecedented level of accuracy. This study presents an approach to accurately extract and model building in three-dimensional space from airborne laser scanning data acquired over Universiti Putra Malaysia in 2015. First, the point cloud was classified into ground and non-ground xyz points. The ground points was used to generate digital terrain model (DTM) while digital surface model (DSM) was  produced from the entire point cloud. From DSM and DTM, we obtained normalise DSM (nDSM) representing the height of features above the terrain surface.  Thereafter, the DSM, DTM, nDSM, laser intensity image and orthophoto were  combined as a single data file by layer stacking. After integrating the data, it was segmented into image objects using Object Based Image Analysis (OBIA) and subsequently, the resulting image object classified into four land cover classes: building, road, waterbody and pavement. Assessment of the classification accuracy produced overall accuracy and Kappa coefficient of 94.02% and 0.88 respectively. Then the extracted building footprints from the building class were further processed to generate 3D model. The model provides 3D visual perception of the spatial pattern of the buildings which is useful for simulating disaster scenario for  emergency management

Developing a three-dimensional city modeling with the absence of elevation data

The past few decades have witnessed steady innovations in remote sensing technologies; however, elevation data needed for creating 3D city models are not reachable for several regions in all over the world. Many developed states still without proper nationwide elevation measurements dataset for developing sufficient 3D city models. The current paper addresses the possibility of producing 3D models for areas without elevation data but with footprints, measurements collected from government departments and volunteered individuals. The study aims to investigate and evaluate a different approach to create three-dimensional city models based on data that existed in open-source maps when elevation measurements are not available. The proposed approach can be divided into two stages: footprint and shadow data collection, and height estimation. At first, the footprint information and shadow area are manually gathered from satellite images, then the building height is predicted based on rooftop and shadow data. SketchUp, a 3D design software, is employed as an efficient tool for creating the 3D virtual city model. To develop such a model, the software utilizes procedural modeling in addition to an image-based approach. The developed model can produce a satisfactory and realistic virtual scene within a short time and for a large area. The 3D city modeling resulted from estimated heights is considered as a rational provisional solution at areas where elevation data are not available or are out-dated

Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability

Points for Energy Renovation (PointER): A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics

Rapid renovation of Europe's inefficient buildings is required to reduce climate change. However, analyzing and evaluating buildings at scale is challenging because every building is unique. In current practice, the energy performance of buildings is assessed during on-site visits, which are slow, costly, and local. This paper presents a building point cloud dataset that promotes a data-driven, large-scale understanding of the 3D representation of buildings and their energy characteristics. We generate building point clouds by intersecting building footprints with geo-referenced LiDAR data and link them with attributes from UK's energy performance database via the Unique Property Reference Number (UPRN). To achieve a representative sample, we select one million buildings from a range of rural and urban regions across England, of which half a million are linked to energy characteristics. Building point clouds in new regions can be generated with the open-source code published alongside the paper. The dataset enables novel research in building energy modeling and can be easily expanded to other research fields by adding building features via the UPRN or geo-location.Comment: The PointER dataset can be downloaded from https://doi.org/10.14459/2023mp1713501. The code used for generating building point clouds is available at https://github.com/kdmayer/PointE

Spatiotemporal Modelling of Rooftop Rainwater Harvesting with LiDAR Data in the Taita Hills, Kenya

The puzzling thing about water is that, while it is very abundant in our planet – earth, millions of people globally face water scarcity. Some places, however, do not. In other places, it is not really that there is no water at all, but it is not available all year round, in most cases. This underscores the importance of putting into cognizance, the spatial and temporal context of water scarcity, hence, the basis for this project. Developing countries, especially have had worse situations with water scarcity due to population explosion and the lack of the technological advancement to harness, purify, transport, store, deliver and reuse water. One of such countries is Kenya, where many do not have access to potable water. Many solutions have been proffered without adequately addressing the issue itself. Rooftop rainwater harvesting is a potential solution to ameliorate this problem. In this thesis, I took a holistic approach to evaluate the potential of Rooftop Rainwater Harvesting (RRWH) in meeting the domestic water needs of the Taita People, Kenya. Importantly, contrary to other RRWH studies, I attempt to introduce and synergize the temporal aspect with the spatial context, in order to deeply understand the monthly dynamics of RRWH. This is crucial in answering the ‘where’ and ‘when’ questions of RRWH. This aims to provide a decision support for stakeholders, by presenting the results visually and quantifiably. The project is mainly divided into three parts. The first part involves the validation and utilization of a Light and Range Detection (LiDAR) data, for automatically generating the footprints of roofs in Taita. Herein, I compared the accuracies of LiDAR datasets from same area but different years. The second part utilizes the roofs’ polygons generated from the LiDAR data to estimate the Rooftop Rainwater Harvesting Potential in the region, by integrating it with Climatologies at high resolution for the earth’s land surface areas (CHELSA) and a strategically chosen universal roof coefficient. Lastly, household survey was carried out in the study area to understand the social context and integrate the data into my model. The result shows that there is a clear temporal trend to RRWHP in the area, and a single annual RRWHP model might be too generalized to give sufficient insight into understanding how much the system can mitigate water problem in the area. It also logically incorporates the survey data into the model to provide information about measurable monthly and annual values, as to percentage of the households that RRWH can fulfill their needs