Application of Machine Learning in Urban Greenery Land Cover Extraction

Abstract

ABSTRACT:Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging work to extract the urban greenery information from remote sensing image as the trees and grassland are mixed with city built-ups. In this paper, we propose a new automatic pixel-based greenery extraction method using multispectral remote sensing images. The method includes three main steps. First, a small part of the images is manually interpreted to provide prior knowledge. Secondly, a five-layer neural network is trained and optimised with the manual extraction results, which are divided to serve as training samples, verification samples and testing samples. Lastly, the well-trained neural network will be applied to the unlabelled data to perform the greenery extraction. The GF-2 and GJ-1 high resolution multispectral remote sensing images were used to extract greenery coverage information in the built-up areas of city X. It shows a favourable performance in the 619 square kilometers areas. Also, when comparing with the traditional NDVI method, the proposed method gives a more accurate delineation of the greenery region. Due to the advantage of low computational load and high accuracy, it has a great potential for large area greenery auto extraction, which saves a lot of manpower and resources.

A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

“…When it comes to urban ecological research using (fused) LiDAR data in China, a non-negligible factor is relatively strict air traffic control. Under such restraints, on the one hand, Chinese high-resolution remote sensing satellites should be noted, such as Gaofen-1 with 2 m spatial resolution [94], and SuperView-101/102 with 0.5 m resolution [95]. To the best of our knowledge, there are few studies using remote sensing data from Chinese satellites in urban ecosystems.…”