Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.

Extracting Rectified Building Footprints from Traditional Orthophotos: A New Workflow

Deep learning techniques such as convolutional neural networks have largely improved the performance of building segmentation from remote sensing images. However, the images for building segmentation are often in the form of traditional orthophotos, where the relief displacement would cause non-negligible misalignment between the roof outline and the footprint of a building; such misalignment poses considerable challenges for extracting accurate building footprints, especially for high-rise buildings. Aiming at alleviating this problem, a new workflow is proposed for generating rectified building footprints from traditional orthophotos. We first use the facade labels, which are prepared efficiently at low cost, along with the roof labels to train a semantic segmentation network. Then, the well-trained network, which employs the state-of-the-art version of EfficientNet as backbone, extracts the roof segments and the facade segments of buildings from the input image. Finally, after clustering the classified pixels into instance-level building objects and tracing out the roof outlines, an energy function is proposed to drive the roof outline to maximally align with the building footprint; thus, the rectified footprints can be generated. The experiments on the aerial orthophotos covering a high-density residential area in Shanghai demonstrate that the proposed workflow can generate obviously more accurate building footprints than the baseline methods, especially for high-rise buildings.

Upliving - Student Accommodation

<p>This research proposal looks into the future at an increase in student population, an increase in basic living costs, the looming issues with densification of Wellington City, and its development to the transport infrastructure. The project aims to create accommodation for students going through university while providing a simple and cohesive mode of transport linking the CBD. The biggest motivator for Upliving is Wellington’s great potential for infill buildings in spaces such as cracks between buildings; above roads; atop buildings; underutilised areas within an urban context; temporarily empty sites awaiting future development; and car parks (particularly with the development of transport infrastructure, eliminating the need for as many vehicles within the city). The development would be funded by universities in collaboration with the city to help compensate for the rising education costs and rising living costs. It will provide better transport within the city, opening up more potential for development with less infrastructure to facilitate the currently high vehicle usage. The goals of the architecture are to accommodate students, maintain a minimal building footprint, effectively use circulation spaces to minimise an inclination to damage of property, create flexible spaces, maintain a simple structure for rapid construction, provide active communal spaces, establish connections to university campuses, generally link the Wellington’s CBD, use underutilised spaces, and maintain a contemporary identity that blends into the urban context. There are many aspects involved in this research portfolio, including the notion of research by design through the use of an in-depth iterative process, precedent investigations, client/occupant research, detailed design explorations of large and small scales, an outcome, and a critical reflection highlighting possible changes and a direction for further development.</p>

Upliving - Student Accommodation

<p>This research proposal looks into the future at an increase in student population, an increase in basic living costs, the looming issues with densification of Wellington City, and its development to the transport infrastructure. The project aims to create accommodation for students going through university while providing a simple and cohesive mode of transport linking the CBD. The biggest motivator for Upliving is Wellington’s great potential for infill buildings in spaces such as cracks between buildings; above roads; atop buildings; underutilised areas within an urban context; temporarily empty sites awaiting future development; and car parks (particularly with the development of transport infrastructure, eliminating the need for as many vehicles within the city). The development would be funded by universities in collaboration with the city to help compensate for the rising education costs and rising living costs. It will provide better transport within the city, opening up more potential for development with less infrastructure to facilitate the currently high vehicle usage. The goals of the architecture are to accommodate students, maintain a minimal building footprint, effectively use circulation spaces to minimise an inclination to damage of property, create flexible spaces, maintain a simple structure for rapid construction, provide active communal spaces, establish connections to university campuses, generally link the Wellington’s CBD, use underutilised spaces, and maintain a contemporary identity that blends into the urban context. There are many aspects involved in this research portfolio, including the notion of research by design through the use of an in-depth iterative process, precedent investigations, client/occupant research, detailed design explorations of large and small scales, an outcome, and a critical reflection highlighting possible changes and a direction for further development.</p>

Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines.

Application of GIS in Rainwater Harvesting Research: A Scoping Review

The spatial and temporal variability of quantity and quality of water are important aspects of water resources management. Water demand has been increasing across the globe, but the fresh water supply is limited. Rainwater harvesting (RWH) systems are increasingly being embraced as an alternative freshwater source. This study reviews the dynamics of global research on RWH that utilises geographic information systems (GIS). It is found that the interest and use of RWH utilising GIS have increased over the recent years. However, the full potential of GIS in large scale RWH is yet to be untapped. We make recommendations for future research on RWH based on GIS. This includes new software and model development that links RWH with GIS to plan and design large scale RWH and automated building footprint extraction for estimating RWH potential. GIS can play a bigger role in achieving Sustainable Development Goals (SDGs) by incorporating GIS with RWH since GIS can handle large spatial data efficiently, which can help in locating areas that are suitable for rainwater harvesting.

Developing a Method to Extract Building 3D Information from GF-7 Data

The three-dimensional (3D) information of buildings can describe the horizontal and vertical development of a city. The GaoFen-7 (GF-7) stereo-mapping satellite can provide multi-view and multi-spectral satellite images, which can clearly describe the fine spatial details within urban areas, while the feasibility of extracting building 3D information from GF-7 image remains understudied. This article establishes an automated method for extracting building footprints and height information from GF-7 satellite imagery. First, we propose a multi-stage attention U-Net (MSAU-Net) architecture for building footprint extraction from multi-spectral images. Then, we generate the point cloud from the multi-view image and construct normalized digital surface model (nDSM) to represent the height of off-terrain objects. Finally, the building height is extracted from the nDSM and combined with the results of building footprints to obtain building 3D information. We select Beijing as the study area to test the proposed method, and in order to verify the building extraction ability of MSAU-Net, we choose GF-7 self-annotated building dataset and a public dataset (WuHan University (WHU) Building Dataset) for model testing, while the accuracy is evaluated in detail through comparison with other models. The results are summarized as follows: (1) In terms of building footprint extraction, our method can achieve intersection-over-union indicators of 89.31% and 80.27% for the WHU Dataset and GF-7 self-annotated datasets, respectively; these values are higher than the results of other models. (2) The root mean square between the extracted building height and the reference building height is 5.41 m, and the mean absolute error is 3.39 m. In summary, our method could be useful for accurate and automatic 3D building information extraction from GF-7 satellite images, and have good application potential.