No abstract

No 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.

<p><strong>Abstract.</strong> People detection in 2D laser range data is widely used in many application, such as robotics, smart cities or regions, and intelligent driving. For most current methods on people detection based on a single laser range finder are actually leg detectors as the sensor are always established below the knee height. Current state-of-the-art methods share similar steps including segmentation, feature extraction and a machine learning-based classification, but use different features which have good performance on their own experimental data. For researchers, it is important and desirable to know which features are more robust. In this paper, taking advantage of the fact that effective features can be selected by AdaBoost and assembled into a strong classifier, a set of features presented in state-of-the-art methods is combined with a set of features presented by us to train a leg detector by the AdaBoost algorithm. This detector is assembling by effective features and can classify segments into leg and non-leg. Three open source data sets including simple and complex scenarios are used for the experiments to test the features and extracted the important ones. To reduce the effect of segmentation on the final results, three segmentation methods are simultaneously used for experiments and analysis to ensure the reliability and credibility of our conclusion. Finally, 10 robust features for leg detection in 2D laser range data are presented based on the results.</p>