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Digital Elevation Models (DEMs) are extensively utilized for terrain analysis, representation, and visualization. Various application scenarios require DEMs at different Level of Details (LODs). Although traditional multi-scale landform expression methods primarily target DEMs at small scales, the simplifying Digital Surface Models (DSMs) for urban modeling at large scales remains a significant challenge. By integrating map generalization theory with computer vision techniques, we developed a novel method for urban building simplification in DSMs, termed Building Simplification in Digital Surface Models (BS-DSM). First, the buildings extracted from the DSMs are subjected to morphological analysis and aligned to a specific orientation. Next, the DSMs are divided into rectangular pixel blocks through energy-driven sampling, followed by horizontal simplification of building shapes in a 2D projection plane according to the geometric characteristics of these pixel blocks. Finally, to preserve the average height and total volume of the simplified 3D buildings in the vertical direction, the building heights across different pixel blocks are adjusted and interpolated based on the skeleton lines of building roofs and calculations of adjacent height values. The proposed BS-DSM method was evaluated using the publicly available Vaihingen DSM dataset. The result shows that the BS-DSM method performs better in simplifying building shape and height while meeting basic multi-scale expression constraints compared with traditional filtering methods.

Building simplification involves the process of reducing the complexity of building shapes and details, which is crucial for preserving key features, highlighting essential information, and enhancing map readability. As the map scale decreases, the complex details of buildings need to be effectively simplified. Although various methods exist for simplifying vector or raster data buildings, there is limited research on retaining the original building textures during the simplification process. This study proposes a raster-based method for the simplification and texturing of buildings in remote sensing imagery. The method begins with segmenting preprocessed individual raster data buildings using the superpixels extracted via energy-driven sampling (SEEDS) superpixel segmentation method. Superpixels to be retained are then selected based on the evaluation parameters, corner ratio (CR), and square ratio (SR). Subsequently, the building area texture is extracted, and suitable textures from the texture library are selected through texture feature comparison (TFC). The selected textures are then hue-adjusted to achieve simplified textures that closely resemble the original image. Compared to traditional raster-based building simplification methods, this superpixel-based approach for the simplification and texturing of buildings in remote sensing imagery provides more suitable textures for simplified structures. This enhances the effectiveness of raster-based map generalization, improving both the aesthetic appeal and functionality of maps.

Simplifying building contours involves reducing data volume while preserving the continuity, accuracy, and essential characteristics of building shapes. This presents significant challenges for sequence representation and generation. Traditional methods often rely on complex rule design, feature engineering, and iterative optimization. To overcome these limitations, this study proposes a Transformer-based Polygon Simplification Model (TPSM) for the end-to-end vector simplification of building contours. TPSM processes ordered vertex coordinate sequences of building contours, leveraging the inherent sequence modeling capabilities of the Transformer architecture to directly generate simplified coordinate sequences. To enhance spatial understanding, positional encoding is embedded within the multihead self-attention mechanism, allowing the TPSM to effectively capture relative vertex positions. Additionally, a self-supervised reconstruction mechanism is introduced, where random perturbations are applied to input sequences, and the model learns to reconstruct the original contours. This mechanism enables TPSM to better understand underlying geometric relationships and implicit simplification rules. Experiments were conducted using a 1:10,000 building dataset from Shenzhen, China, targeting a simplification scale of 1:25,000. The results demonstrate that TPSM outperforms five established simplification algorithms in controlling changes to building area, orientation, and shape fidelity, achieving an average intersection over union (IoU) of 0.901 and a complexity-aware IoU (C-IoU) of 0.735.

This study proposes a template matching simplification method from the perspective of shape cognition based on the typical template characteristics of building distributions and representations. The method first formulates a series of templates to abstract the building shape by generalizing their polygons and analyzing their symbolic meanings, then conducts the simplification by searching and matching the most similar template that can be used later to replace the original building. On the premise of satisfying the individual geometric accuracy on a smaller scale, the proposed method can enhance the impression of well-known landmarks and reflect the pattern in mapping areas by the symbolic template. The turning function that describes shape by measuring the changes of the tangent-angle as a function of the arc-length is employed to obtain the similar distance between buildings and template polygons, and the least squares model is used to control the geometry matching of the candidate template. Experiments on real datasets are carried out to assess the usefulness of this method and compare it with two existing methods. The experiments suggest that our method can preserve the main structure of building shapes and geometric accuracy.

Several algorithms have been developed to simplify buildings based on their local structure in past decades. However, different local structures are defined for certain purposes, and no algorithm can appropriately simplify all buildings. We propose a combined building simplification approach based on local structure classification and backtracking strategy. In this approach, local structures are classified and their based operations are defined by considering the buildings’ orthogonal and non-orthogonal features. Each building is simplified to target scale with a selected local-structure-based operation progressively scale-by-scale. Rules are built to support the selection of local-structure-based operations with a binary decision tree, and a backtracking strategy is used when an invalid operation is applied. When the building is too small or the evaluation shows that it cannot be simplified based on local structures, template matching or enlargement algorithms are applied to simplify the building. A dataset (1:10k) collected from the Ordnance Survey was used for the experiment and simplified scale of 1:25k. Results satisfied legibility constraints and the change in area, orientation and position of simplified buildings are controlled within certain range by comparing with the results generated based on other four simplification algorithms. Possible use of our approach for continuous scale transformation of buildings is also discussed.

Building simplification is an important research area in automatic map generalization. Up to now, many approaches have been proposed by scholars. However, in the continuous transformation of scales for buildings, keeping the main shape characteristics, area, and orthogonality of buildings are always the key and difficult points. Therefore, this paper proposes a method of progressive simplification for buildings based on structural subdivision. In this paper, iterative simplification is adopted, which transforms the problem of building simplification into the simplification of the minimum details of building outlines. Firstly, a top priority structure (TPS) is determined, which represents the smallest detail in the outline of the building. Then, according to the orthogonality and concave–convex characteristics, the TPS are classified as 62 subdivisions, which cover the local structure of the building polygon. Then, the subdivisions are divided into four simplification types. The building is simplified to eliminate the TPS continuously, retaining the right-angle characteristics and area as much as possible, until the results satisfy the constraints and rules of simplification. A topographic dataset (1:1 K) collected from Kadaster was used for our experiments. In order to evaluate the algorithm, many tests were undertaken, including tests of multi-scale simplification and simplification of typical buildings, which indicate that this method can realize multi-scale presentation of buildings. Compared with the existing simplification methods, the comparison results show that the proposed method can simplify buildings effectively, which has certain advantages in keeping shape characteristics, area, and rectangularity.

建筑物化简是地图制图领域关注的热点问题之一。集成不同算法构建形状特征自适应的化简模型是应对建筑物多样化形态的有效策略，但当前相关研究主要从局部结构模式或化简结果评价展开，缺乏对形状结构的整体分析视角和深层次认知。本文提出一种深度学习支持下的形状自适应建筑物化简方法。首先，利用图卷积自编码网络对建筑物形状进行深度认知，提取隐含在边界节点分布中的形状特征并进行编码表达；然后，通过监督学习方法建立形状编码与化简算法之间的映射关系，从而实现依据输入建筑物的形状特征选择适宜化简算法的自适应机制。试验表明，本文方法的化简结果在位置、方向、面积和形状保持指标上总体优于单一算法，具备较好的理论与应用价值。

P208; 针对轮廓模糊建筑物多边形的化简问题,提出一种基于特征边重构的建筑物化简方法.该方法定义了建筑物的主方向和控制其整体结构的特征边,以保持建筑物的规则形态.首先利用统计加权方法计算建筑物的主方向,基于主方向对建筑物执行直角化操作.然后按照特征边的定义检测直角化建筑物的特征边,将特征边组合并抽象出几种局部结构,建立重组规则.最后通过判别特征边组合的空间关系,选择合适的结构重组规则来化简建筑物.结合真实数据进行多组试验,结果表明该方法有效还原了建筑物的直角特征,面积和形状保持良好,适用性强.

Since Urban Building Energy Modeling and Multi-Objective Optimization require large computational resources, both could benefit of simplification techniques able also to make them more affordable to professionals. In this work, the capabilities of a simplification algorithm for urban scale application are assessed in the context of the optimization of energy efficiency measures for buildings. A group of buildings from different districts were selected along with a set of energy efficiency measures and four objectives, such as heating and cooling needs, thermal comfort, and costs. The performance of the simplification was assessed by using precision, recall and F1-score as metrics to check whether the simplified models could obtain the same optimal solutions as the detailed ones. Overall, the simplification produced adequate results with an F1-score greater than 0.8 for 85 % of the cases considered in all climates, allowing the simulation time to be reduced up to 15.7 times.

The BIM-based building energy simulation plays an important role in sustainable design on the track of achieving the net-zero carbon building stock by 2050. However, the issues on BIM-BEM interoperability make the design process inefficient and less automatic. The insufficient semantic information may lead to results inaccurate while the error-prone geometry will terminate the simulation engine. Defective models and authoring tools lagging behind the standard often cause failures in creating a clean geometry that is acceptable to the simulation engine. This project aims to develop a workflow that helps with the documentation of a lightweight geometry in gbXML format. The implemented workflow bypasses the modeling inaccuracies and irrelevant details by reconstructing the model based on extrusions on patched floor plans. Compared with other gbXML files exported by BIM authoring tools, the resulting gbXML is more lightweight with airtight space boundaries. The gbXML has been further tested against EnergyPlus to demonstrate its capability in aiding a seamless geometry exchange between BIM and BEM.