Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization

This paper extends recent research into the usefulness of volunteered photos for land cover extraction, and investigates whether this usefulness can be automatically assessed by an easily accessible, off-the-shelf neural network pre-trained on a variety of scene characteristics. Geo-tagged photographs are sometimes presented to volunteers as part of a game which requires them to extract relevant facts about land use. The challenge is to select the most relevant photographs in order to most efficiently extract the useful information while maintaining the engagement and interests of volunteers. By repurposing an existing network which had been trained on an extensive library of potentially relevant features, we can quickly carry out initial assessments of the general value of this approach, pick out especially salient features, and identify focus areas for future neural network training and development. We compare two approaches to extract land cover information from the network: a simple post hoc weighting approach accessible to non-technical audiences and a more complex decision tree approach that involves training on domain-specific features of interest. Both approaches had reasonable success in characterizing human influence within a scene when identifying the land use types (as classified by Urban Atlas) present within a buffer around the photograph’s location. This work identifies important limitations and opportunities for using volunteered photographs as follows: (1) the false precision of a photograph’s location is less useful for identifying on-the-spot land cover than the information it can give on neighbouring combinations of land cover; (2) ground-acquired photographs, interpreted by a neural network, can supplement plan view imagery by identifying features which will never be discernible from above; (3) when dealing with contexts where there are very few exemplars of particular classes, an independent a posteriori weighting of existing scene attributes and categories can buffer against over-specificity

Enhancing the role of citizen sensors in mapping: COST Action TD1202

This article introduces a strategic initiative, COST Action TD1202, focused on the role of citizen sensors in mapping. It outlines the Action's scope, aims and current status. In particular, the article outlines the potential of citizen science in mapping activities and indicates the scope of current work undertaken by the Action's four working groups. It is stressed that the Action is at an early stage and that it is open to new members

Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information

There is much interest in being able to combine crowdsourced data. One of the critical issues in information sciences is how to combine data or information that are discordant or inconsistent in some way. Many previous approaches have taken a majority rules approach under the assumption that most people are correct most of the time. This paper analyses crowdsourced land cover data generated by the Geo-Wiki initiative in order to infer the land cover present at locations on a 50 km grid. It compares four evidence combination approaches (Dempster Shafer, Bayes, Fuzzy Sets and Possibility) applied under a geographically weighted kernel with the geographically weighted average approach applied in many current Geo-Wiki analyses. A geographically weighted approach uses a moving kernel under which local analyses are undertaken. The contribution (or salience) of each data point to the analysis is weighted by its distance to the kernel centre, reflecting Tobler’s 1st law of geography. A series of analyses were undertaken using different kernel sizes (or bandwidths). Each of the geographically weighted evidence combination methods generated spatially distributed measures of belief in hypotheses associated with the presence of individual land cover classes at each location on the grid. These were compared with GlobCover, a global land cover product. The results from the geographically weighted average approach in general had higher correspondence with the reference data and this increased with bandwidth. However, for some classes other evidence combination approaches had higher correspondences possibly because of greater ambiguity over class conceptualisations and / or lower densities of crowdsourced data. The outputs also allowed the beliefs in each class to be mapped. The differences in the soft and the crisp maps are clearly associated with the logics of each evidence combination approach and of course the different questions that they ask of the data. The results show that discordant data can be combined (rather than being removed from analysis) and that data integrated in this way can be parameterised by different measures of belief uncertainty. The discussion highlights a number of critical areas for future research

Utilização de Informação Geográfica Voluntária para a Criação/Validação de Mapas de Cobertura e Uso do Solo

Atualmente está disponível uma grande variedade de Informação Geográfica Voluntária (IGV), que vai desde fotografias e descrições georreferenciadas até mapas vetoriais. A partir de algumas dessas fontes de dados é possível extrair informação sobre a cobertura e uso do solo, que pode ser útil de várias formas para a produção de mapas de cobertura e uso do solo, por exemplo, através da identificação de áreas de treino para a classificação de imagens de satélite, construção de bases de dados de referência para a validação dos mapas ou até mesmo a criação destes mapas diretamente a partir da IGV. No entanto, várias questões são levantadas pelo uso deste tipo de dados. Neste artigo é explicado o potencial e as limitações do uso da IGV para auxiliar na criação de mapas de cobertura e uso do solo. São identificadas iniciativas que fornecem dados potencialmente úteis, são apresentados exemplos de aplicação de IGV para a criação e validação de mapas de cobertura e uso do solo e são identificados algumas questões que requerem mais investigação

On the Contribution of Volunteered Geographic Information to Land Monitoring Efforts

Land-related inventories are important sources of geoinformation for environmentalists, researchers, policy-makers, practitioners, and ecologists. Traditionally, a considerable amount of energy, time, and money have been dedicated to map global/regional/local land use datasets. While remote sensing images and techniques along with field surveying have been the main sources of data for determining land use features, field measurements of ground truth have always amplified the required time and money, as well as information credibility. Nowadays, volunteered geographic information (VGI) has shown its great contributions to different scientific disciplines. This was made possible thanks to Web 2.0 technologies and GPS-enabled devices, which have advanced citizens knowledge-based projects and made them user-friendly for volunteered citizens to collect and share their knowledge about geographical objects. OpenStreetMap as one of those leading VGI projects has shown its great potential for collecting and providing land use information. The collaboratively collected land use features from diverse citizens could greatly back up the challenging element of land use mapping, which is in-field data gathering. Hence, in this literature we will look at the completeness, thematic accuracy and fitness for use of OpenStreetMap features for land mapping purposes over European countries. The empirical findings reveal that the degree of completeness varies widely ranging from 2% to 96% and overall and per-class thematic accuracies goes up to 80% and 96%, respectively compared to the European GMESUA datasets. Furthermore, more than 50% of land use features of eight European countries are mapped. This messages that the harnessing citizens’ knowledge can play a great role in land mapping as an alternative and complementary data source