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Abstract
Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.
Highlights
Rooftops of buildings have been intensively studied in fields such as sustainable urban development, building energy modeling, and urban planning and design in recent decades[1–3]
Owing to urbanization associated with the digital age, reliable information on rooftops is in increasing demand[4–6]
The main objective is to extract accurate rooftop areas in China using high-resolution open-access remote sensing imagery based on a geospatial artificial intelligence (GeoAI) framework
Summary
Rooftops of buildings have been intensively studied in fields such as sustainable urban development, building energy modeling, and urban planning and design in recent decades[1–3]. Due to the development of image processing algorithms, such as the edge detection and image segmentation, rooftops data can be extracted from high-resolution remote sensing imagery[17,18]. Deep learning-based image semantic segmentation methods have been applied for the extraction of rooftops data[25,26]. The main objective is to extract accurate rooftop areas in China using high-resolution open-access remote sensing imagery based on a geospatial artificial intelligence (GeoAI) framework. The following steps were employed for generating the rooftop area dataset: (1) data preparation through spatial stratified sampling involving geospatial prior knowledge and data processing pipeline to augment the representativeness and number of samples; (2) creation of a deep learning segmentation model, which is based on an ensemble learning strategy and an improved prediction method to improve the rooftop extraction performance. The data was validated on test samples of 180 km[2] across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively
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