Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimization of DNNs. Neuroevolution is a term, which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for wider application within real-world deep learning problems. This article presents a comprehensive survey, discussion, and evaluation of the state-of-the-art in using EAs for architectural configuration and training of DNNs. This article highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions. Impact Statement—The concept of deep learning originated from the study of artificial neural networks (ANNs). ANNs have achieved extraordinary results in a variety of diverse application areas. Numerous methods have been applied to the architectural configuration and learning or training of artificial DNN and these methods play a crucial role in the success or failure of the DNN for most problems and applications. Recently, EAs have been gaining momentum as a computationally feasible method (called neuroevolution) for the automated configuration and learning or training of DNNs. This article reviews over 170 recent scientific papers describing how major EAs paradigms are being applied by researchers to the configuration and optimization of multiple DNNs. By articulating a clear understanding of the context, state-of-the-art, and feasibility of Neuroevolution, researchers in AI, EAs, and DNN will benefit from this article. The impact of this article comes from contributing toward enhancing research capacity, knowledge, and skills for researchers currently working in neuroevolution and actively engaging those considering becoming involved in this area.

Volunteered Geographic Information (VGI) is currently a\ud “hot topic” in the GIS community. The OpenStreetMap\ud (OSM) project is one of the most popular and well supported\ud examples of VGI. Traditional measures of spatial data quality\ud are often not applicable to OSM as in many cases it\ud is not possible to access ground-truth spatial data for all\ud regions mapped by OSM. We investigate to develop measures\ud of quality for OSM which operate in an unsupervised\ud manner without reference to a “trusted” source of groundtruth\ud data. We provide results of analysis of OSM data\ud from several European countries. The results highlight specific\ud quality issues in OSM. Results of comparing OSM with\ud ground-truth data for Ireland are also presented

This paper describes the results of an analysis of the OpenStreetMap (OSM) database for the United Kingdom (UK) and Ireland (correct to April 2011). 15, 640 OSM ways (polygons and polylines), resulting in 316, 949 unique versions of these objects, were extracted and analysed from the OSM database for the UK and Ireland. In our analysis we only considered “heavily edited” objects in OSM: objects which have been edited 15 or more times. Our results show that there is no strong relationship between increasing numbers of contributors to a given object and the number of tags (metadata) assigned to it. 87% of contributions/edits to these objects are performed by 11% of the total 4128 contributors. In 79% of edits additional spatial data (nodes) are added to objects. The results in this paper do not attempt to evaluate the OSM data as good/poor quality but rather informs potential consumers of OSM data that the data itself is changing over time. In developing a better understanding of the characteristics of “heavily edited” objects there may be opportunities to use historical analysis in working towards quality indicators for OSM in the future.

(2019). Crowdsourced geospatial data quality: challenges and future directions. International Journal of Geographical Information Science. Ahead of Print.