Flood events cause substantial damage to urban and rural areas. Monitoring water extent during large-scale flooding is crucial in order to identify the area affected and to evaluate damage. During such events, spatial assessments of floodwater may be derived from satellite or airborne sensing platforms. Meanwhile, an increasing availability of smartphones is leading to documentation of flood events directly by individuals, with information shared in real-time using social media. Topographic data, which can be used to determine where floodwater can accumulate, are now often available from national mapping or governmental repositories. In this work, we present and evaluate a method for rapidly estimating flood inundation extent based on a model that fuses remote sensing, social media and topographic data sources. Using geotagged photographs sourced from social media, optical remote sensing and high-resolution terrain mapping, we develop a Bayesian statistical model to estimate the probability of flood inundation through weights-of-evidence analysis. Our experiments were conducted using data collected during the 2014 UK flood event and focus on the Oxford city and surrounding areas. Using the proposed technique, predictions of inundation were evaluated against ground-truth flood extent. The results report on the quantitative accuracy of the multisource mapping process, which obtained area under receiver operating curve values of 0.95 and 0.93 for model fitting and testing, respectively.

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.

The use of Geographic Information or GI, has grown rapidly in recent years. Previous research has identified the importance of usability and user centred design in enabling the proliferation and exploitation of GI. However, the design and development of usable GI is not simply a matter of applying the tried and tested usability methods that have been developed for software and web design. Dealing with data and specifically GI brings with it a number of issues that change the way usability and user centred design can be applied. This paper describes the outcomes of a workshop held in March 2010 exploring the core issues relating to GI usability. The workshop brought together an international group of twenty experts in both human factors and GI, from a wide range of academic and industrial backgrounds. These experts considered three key issues, the stakeholders in GI, key challenges applying usability to GI and the usability methods that can be successfully applied to GI. The result of this workshop was to identify some areas for future research, such as the production of meaningful metadata and the implications of blurring of the line between data producers and data consumers.

The quality aspects of OpenStreetMap (OSM), as the global representation of crowd-sourced mapping, have always been of priomary concern to academics. While the methodologies for checking its quality against the national maps have been implemented by a number of studies, there are minimal works on how to practically improve the quality of OSM towards being an authoritative map source. This paper presents a method for conflating road attributes, namely the name and reference code, of OSM with the Open Data provided by Ordnance Survey (the British national mapping agency). The added values in the proposed methodology include the daily updates and serving of the conflated maps via open Web Services. More importantly, the OSM crowd correction is facilitated by frequently highlighting and web-serving the individual differences. There are currently over 5,800 differences in matching road names and references between the two datasets. In addition to describing the conflation methodology, the different geographic distribution patterns of the identified differences are discussed. A negative effect of the road density on the ratio of the mismatched features between the two datasets is observable, evidenced by their different geographical distribution over the map. It is shown that the best correspondence between attributes exists in the very dense areas, followed by the very low density areas, and lastly in the middle to large sized cities.

The problems in teaching a course on geographical information systems in higher education are reviewed and a possible syllabus that will be most relevant to needs in the United Kingdom is suggested. The syllabus is divided into six major sections relating to the context, cartographic and spatial analytical concepts, their realization in a computing environment, operations, applications and institutional issues. A particular feature is an attempt to maintain a rigid distribution between underlying theory which is relatively stable and its more volatile operationalization on computers. The paper results from a symposium held in Leicester (U.K.) late in 1988 that brought together nine specialists in the held.