The Role of Citizen Science in Earth Observation

Citizen Science (CS) and crowdsourcing are two potentially valuable sources of data for Earth Observation (EO), which have yet to be fully exploited. Research in this area has increased rapidly during the last two decades, and there are now many examples of CS projects that could provide valuable calibration and validation data for EO, yet are not integrated into operational monitoring systems. A special issue on the role of CS in EO has revealed continued trends in applications, covering a diverse set of fields from disaster response to environmental monitoring (land cover, forests, biodiversity and phenology). These papers touch upon many key challenges of CS including data quality and citizen engagement as well as the added value of CS including lower costs, higher temporal frequency and use of the data for calibration and validation of remotely-sensed imagery. Although still in the early stages of development, CS for EO clearly has a promising role to play in the future

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible

Using OpenStreetMap (OSM) to enhance the classification of local climate zones in the framework of WUDAPT

The World Urban Database and Access Portal Tools (WUDAPT) project has adopted the LocalClimate Zone (LCZ) scheme as a basic and consistent description of form and function of cities atneighbourhood scale. LCZs are classified using crowdsourced training samples, open data andopen source software but the quality of the maps still needs improvement. The aim of this paperis to investigate the use of data from OpenStreetMap (OSM) to enhance the development of LCZs,complement the existing data sources, and improve the accuracy of the maps. Various featureswere derived from the OSM database and combined with seasonal LCZ maps. Therefore amethodology was developed and tested for Hamburg, Germany, using a fuzzy approach and thena weighted combination method was applied to combine the inputs from OSM with each of theseasonal LCZ maps. The results showed that improvements can be achieved for certain classes,either in terms of accuracy, e.g. rectifying the misclassification of agricultural areas as heavyindustry, or representation on the map, e.g. a more detailed water network. The approach developedis flexible and allows for knowledge about which data sources are more reliable as inputsto the combination and weighting process

Mapping and the Citizen Sensor

Maps are a fundamental resource in a diverse array of applications ranging from everyday activities, such as route planning through the legal demarcation of space to scientific studies, such as those seeking to understand biodiversity and inform the design of nature reserves for species conservation. For a map to have value, it should provide an accurate and timely representation of the phenomenon depicted and this can be a challenge in a dynamic world. Fortunately, mapping activities have benefitted greatly from recent advances in geoinformation technologies. Satellite remote sensing, for example, now offers unparalleled data acquisition and authoritative mapping agencies have developed systems for the routine production of maps in accordance with strict standards. Until recently, much mapping activity was in the exclusive realm of authoritative agencies but technological development has also allowed the rise of the amateur mapping community. The proliferation of inexpensive and highly mobile and location aware devices together with Web 2.0 technology have fostered the emergence of the citizen as a source of data. Mapping presently benefits from vast amounts of spatial data as well as people able to provide observations of geographic phenomena, which can inform map production, revision and evaluation. The great potential of these developments is, however, often limited by concerns. The latter span issues from the nature of the citizens through the way data are collected and shared to the quality and trustworthiness of the data. This book reports on some of the key issues connected with the use of citizen sensors in mapping. It arises from a European Co-operation in Science and Technology (COST) Action, which explored issues linked to topics ranging from citizen motivation, data acquisition, data quality and the use of citizen derived data in the production of maps that rival, and sometimes surpass, maps arising from authoritative agencies

Using OpenStreetMap to Create Land Use and Land Cover Maps

OpenStreetMap (OSM) is a bottom up community-driven initiative to create a global map of the world. Yet the application of OSM to land use and land cover (LULC) mapping is still largely unexploited due to problems with inconsistencies in the data and harmonization of LULC nomenclatures with OSM. This chapter outlines an automated methodology for creating LULC maps using the nomenclature of two European LULC products: the Urban Atlas (UA) and CORINE Land Cover (CLC). The method is applied to two regions in London and Paris. The results show that LULC maps with a level of detail similar to UA can be obtained for the urban regions, but that OSM has limitations for conversion into the more detailed non-urban classes of the CLC nomenclature. Future work will concentrate on developing additional rules to improve the accuracy of the transformation and building an online system for processing the data

Impact analysis of accidents on the traffic flow based on massive floating car data

The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow

A METHODOLOGY FOR ASSESSING OPENSTREETMAP DEGREE OF COVERAGE FOR PURPOSES OF LAND COVER MAPPING

The data available in the collaborative project OpenStreetMap (OSM) is in some locations so detailed and complete that it may provide useful data for Land Cover Map creation and validation. However, this degree of detail is not uniform along space. Therefore, one of the first requirements that needs to be assessed to determine if the creation and validation of Land Cover Maps using data available in OSM may be feasible, is the availability of data to provide a relatively complete coverage of the region of interest. To provide a fast and automatic quantitative assessment of this requirement a methodology is presented and tested in this article. Four study areas are considered, all located in Europe. The results show that the four regions presented very different coverages at the time of data download and its spatial distribution was not uniform. This approach enabled the identification of the most problematic regions for land cover mapping, where low levels of data coverage are available. Since the proposed methodology can be automated, it enables a fast identification of the regions that, in a preliminary analysis, may be considered fit for further analysis to assess fitness for use for Land Cover Map creation and/or validation

Assessing the debris around glaciers using remote sensing and random sets

Glacier mapping from satellite multispectral image data is hampered by debris cover on glacier surfaces. Information on the spatial distribution and spatial-temporal dynamics of debris, however, bears various kinds of uncertainties. Debris exhibits the same spectral properties as lateral and terminal moraines and as bedrock outside the glacier margin. Multispectral classification alone is thus not suitable to properly assess its extent. Additional information has to be included, like the low slope angles and curvature characteristics. In this research we propose a random set method for uncertainty modelling of debris-covered glaciers extracted from remote sensed data. Here, we analyse the Fedchenko glacier situated in the Pamir mountains in Central Asia. Clean glacier ice and debris area are represented by random sets. Their statistical mean and median are estimated. The paper combines the advantages of an automated multispectral classification for clean glacier ice and snow with slope information derived from the digital elevation model (DEM). We use an SRTM3 DEM that is resampled to 30m. From a 1999 Landsat ETM+ image the results show that the mean area of clean glacier ice equals 841.87 km2, and 94.39 km2 for debris-covered area. Temporal analysis shows that the mean area of clean ice increased from 1992 to 1999 and is decreasing since 1999, in opposite to the debris covered area. We conclude that this method based on random set theory has the potential to serve as a general framework in uncertainty modelling of debris-covered glaciers and is applicable for mountainous glaciers