Abstract
Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.
Highlights
We developed a method to classify blue-green-gray infrastructure accurately using machine learning algorithms and unmanned aerial vehicle (UAV) image data
Because the resolution of UAV images is on the centimeter scale, this method could identify all types of infrastructure on a sub-meter scale
Evaluating the accuracies with different sampling intervals showed that a sampling interval of 11.6 m ensured that the kappa coefficient and overall accuracy (OA) were in the almost perfect range (>0.8) and that the number of samples was reduced, which increased working efficiency
Summary
Green infrastructure (GI) is a collection of areas that function as natural ecosystems and open spaces (Benedict and McMahon, 2006; Palmer et al, 2015), and it can maintain and improve the quality of air and water and provide multiple benefits for people and wildlife (Palmer et al, 2015; Benedict and McMahon, 2006; Environmental Protection Agency, 2015; Hashad et al, 2021). As an important part of urban ecosystems (Hu et al, 2021), GI provides green spaces for cities, and benefit people’s physical and mental health (Venkataramanan et al, 2019; Zhang et al, 2021). The current related studies only carry out classification and mapping for part of infrastructure. There is a need to perform a more comprehensive classification and mapping of infrastructures
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