Ordnance Survey

About: Ordnance Survey is a government organization based out in Southampton, Hampshire, United Kingdom. It is known for research contribution in the topics: Geospatial analysis & Photogrammetry. The organization has 172 authors who have published 255 publications receiving 6302 citations. The organization is also known as: OS & UK Ordnance Survey.

Web mapping 2.0: The neogeography of the GeoWeb

Abstract: The landscape of Internet mapping technologies has changed dramatically since 2005. New techniques are being used and new terms have been invented and entered the lexicon such as: mash-ups, crowdsourcing, neogeography and geostack. A whole range of websites and communities from the commercial Google Maps to the grassroots OpenStreetMap, and applications such as Platial, also have emerged. In their totality, these new applications represent a step change in the evolution of the area of Internet geographic applications (which some have termed the GeoWeb). The nature of this change warrants an explanation and an overview, as it has implications both for geographers and the public notion of Geography. This article provides a critical review of this newly emerging landscape, starting with an introduction to the concepts, technologies and structures that have emerged over the short period of intense innovation. It introduces the non-technical reader to them, suggests reasons for the neologism, explains the terminology, and provides a perspective on the current trends. Case studies are used to demonstrate this Web Mapping 2.0 era, and differentiate it from the previous generation of Internet mapping. Finally, the implications of these new techniques and the challenges they pose to geographic information science, geography and society at large are considered.

Satellite remote sensing of forest resources: three decades of research development

Abstract: Three decades have passed since the launch of the first international satellite sensor programme designed for monitoring Earth’s resources. Over this period, forest resources have come under increasing pressure, thus their management and use should be underpinned by information on their properties at a number of levels. This paper provides a comprehensive review of how satellite remote sensing has been used in forest resource assessment since the launch of the first Earth resources satellite sensor (ERTS) in 1972. The use of remote sensing in forest resource assessment provides three levels of information; namely (1) the spatial extent of forest cover, which can be used to assess the spatial dynamics of forest cover; (2) forest type and (3) biophysical and biochemical properties of forests. The assessment of forest information over time enables the comprehensive monitoring of forest resources. This paper provides a comprehensive review of how satellite remote sensing has been used to date and, building on...

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

Abstract: The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.