U-Net Based Vegetation Segmentation for Urban Green Space (UGS) Regulation in Yogyakarta City

Urban Green Space (UGS) plays a vital role in maintaining the balance of urban ecosystems. In Yogyakarta City, urban development and an increase in population accompanied by a decrease and insufficiency in the proportion of UGS correlate with rising urban temperatures. Monitoring the availability of UGS can serve as a foundation for decision-making in managing the city’s UGS. With advancements in technology and computing in the field of Computer Vision (CV), it’s possible to recognize UGS from the geometric patterns of satellite images. This study proposes a vegetation segmentation method using satellite images and a Deep Learning (DL) method with the U-Net architecture to estimate the availability of UGS. Evaluation results show that the image dataset without preprocessing with the Red Green Blue (RGB) color channel gives an average Intersection over Union (IoU) of 80.74%. These results are better than data that underwent grayscaling and contrast stretching preprocessing. Based on a case study conducted in three sub-districts in the Yogyakarta region, the constructed U-Net model gave an average IoU evaluation of 60.88%. These results demonstrate the model’s capability to provide a reference in calculating the UGS area at the sub-district level, which can then be utilized by the Yogyakarta City government to analyze the availability of UGS more efficiently. With this analysis, the government can make strategic decisions to manage UGS and create a sustainable urban environment.