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pybdshadow: a python package for generating, analyzing and visualizing building shadows |
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30 April 2022 |
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Building shadows, as one of the significant elements in urban area, have an impact on a variety of features of the urban environment. Building shadows have been shown to affect local surface temperature in metropolitan environments, which will generate thermal influence to the greenery, water, and impervious structures on the urban heat island[@DAI201977-3; @PARK2021101655-4]. In the field of photovoltaic(PV), building integrated PV systems are expected to disseminate due to effective use of urban space. Researchers also focus on the power output performance affected by the shading of buildings[@WU2021116884-5]. Study of the spatial-temporal distribution of building shadow is conducive in determining the best location for photovoltaic panels to maximize energy generation[@YADAV201811-6]. In addition, building shadows also play a significant role in the field of urban planning[@RAFIEE2014397-12], noise propagation[@bolin2020investigation-9], and post-disaster building rehabilitation[@rs13163297].
With the development of remote sensing, photogrammetry and deep learning technology, researchers are able to obtain city-scale building data with high resolution. These newly emerged building data provides an available data source for generating and analyzing building shadows[@CHEN2020114-8].
pybdshadow is a Python package to generate building shadows from building data and provide corresponding methods to analyze the changing position of shadows. pybdshadow can provide brand new and valuable data source for supporting the field of urban studies.
Existing methods of generating and detecting building shadows can be devided into two major ways: Remote sensing and BIM/GIS analysis.
- Remote sensing: In the field of remote sensing and satellite image processing, researchers examine shadow information from remote sensing images by identifying and distinguishing building shadows from other objects[@rs13152862-7]. Zhou et al. developed a shadow detection method by combining the zero-crossing detection method with the DBM-based geometric method to identify shadow from high-resolution images[@zhou2015integrated-10; @rs12040679-11].
- BIM/GIS analysis: Another way of obtaining building shadow is to transform Building Information Model(BIM) to its corresponding geo-located model[@RAFIEE2014397-12]. The Hillshade function provided in ArcGIS is capable of producing a grayscale 3D representation of the terrain surface, which can be used as a tool for analysing building shadow. Hong et al. analyze the building shadow using Hillshade Analysis and estimate the available rooftop area for PV System[@HONG2016408-13]. Miranda et al. propose an approach that uses the properties of sun movement to track the changing position of shadows within a fixed time interval[@8283638-14].
In Python environment, geospatial analysing package like geopandas, PySAL provide tools to easily implement the spatial analysis of spatial data[@kelsey_jordahl_2021_5573592; @pysal2007]. Nevertheless, there is a lack of an effective tool for generating and analyzing building shadows that is compatible with the Python geospatial data processing framework.
pybdshadow is a python package for generating, analyzing and visualizing building shadows from large scale building geographic data. pybdshadow support generate building shadows from both sun light and point light. pybdshadow provides an efficient and easy-to-use method to generate a new source of geospatial data with great application potential in urban study.
Currently, pybdshadow mainly provides the following methods:
- Generating building shadow from sun light: With given location and time, the function in
pybdshadowuses the properties of sun position obtained fromsuncalc-pyand the building height to generate shadow geometry data(\autoref{fig:fig1}(a)). - Generating building shadow from point light:
pybdshadowcan generate the building shadow with given location and height of the point light, which can be potentially useful for visual area analysis in urban environment(\autoref{fig:fig1}(b)). - Analysis:
pybdshadowintegrated the analysing method based on the properties of sun movement to track the changing position of shadows within a fixed time interval. Based on the grid processing framework provided byTransBigData[@Yu2022],pybdshadowis capable of calculating sunshine time on the ground and on the roof(\autoref{fig:fig2}). - Visualization: Built-in visualization capabilities leverage the visualization package
keplerglto interactively visualize building and shadow data in Jupyter notebooks with simple code.
The target audience of pybdshadow includes data science researchers and data engineers in the field of BIM, GIS, energy, environment, and urban computing.
The latest stable release of the software can be installed via pip and full documentation can be found at https://pybdshadow.readthedocs.io/en/latest/.
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