Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Limitations of Majority Agreement in Crowdsourced Image Interpretation

Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter-volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer-expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non-cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non-cropland) on 27% of the images, but disagreed strongly (cropland vs. non-cropland) on only 7%. Inter-volunteer disagreement increased significantly with inter-expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis-categorized if only the volunteers’ majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Usability of VGI for validation of land cover maps

Volunteered Geographic Information (VGI) represents a growing source of potentially valuable data for many applications, including land cover map validation. It is still an emerging field and many different approaches can be used to take value from VGI, but also many pros and cons are related to its use. Therefore, since it is timely to get an overview of the subject, the aim of this article is to review the use of VGI as reference data for land cover map validation. The main platforms and types of VGI that are used and that are potentially useful are analysed. Since quality is a fundamental issue in map validation, the quality procedures used by the platforms that collect VGI to increase and control data quality are reviewed and a framework for addressing VGI quality assessment is proposed. A review of cases where VGI was used as an additional data source to assist in map validation is made, as well as cases where only VGI was used, indicating the procedures used to assess VGI quality and fitness for use. A discussion and some conclusions are drawn on best practices, future potential and the challenges of the use of VGI for land cover map validation

Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap

The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there are instances where volunteer mapping is inaccurate. Despite the appeal of using OSM semantic information with remote sensing images, to train deep learning models, the crowdsourced data quality is inconsistent. High-resolution remote sensing image segmentation is a mature application in many fields, such as urban planning, updated mapping, city sensing, and others. Typically, supervised methods trained with annotated data may learn to anticipate the object location, but misclassification may occur due to noise in training data. This article combines Very High Resolution (VHR) remote sensing data with computer vision methods to deal with noisy OSM. This work deals with OSM misalignment ambiguity (positional inaccuracy) concerning satellite imagery and uses a Convolutional Neural Network (CNN) approach to detect missing buildings in OSM. We propose a translating method to align the OSM vector data with the satellite data. This strategy increases the correlation between the imagery and the building vector data to reduce the noise in OSM data. A series of experiments demonstrate that our approach plays a significant role in (1) resolving the misalignment issue, (2) instance-semantic segmentation of buildings with missing building information in OSM (never labeled or constructed in between image acquisitions), and (3) change detection mapping. The good results of precision (0.96) and recall (0.96) demonstrate the viability of high-resolution satellite imagery and OSM for building detection/change detection using a deep learning approach

GREEN SPACES AND PUBLIC HEALTH IN URBAN AREAS

Quality of life in cities depends largely on the availability of attractive and accessible green spaces. It is generally agreed that urban green spaces are essential for the health and well-being of citizens. This paper aims to broach the issue of urban green spaces, emphasizing their importance (embodied in at least three categories of benefits: environmental, economic and social) and focusing on the idea that the existence of green spaces may contribute to obtaining savings or income in a city’s treasury. Therefore, it takes shape the belief that the effects gained from investing in planning and protecting urban green infrastructure, could turn into economic effects. Last decade, researchers have shown a real interest in finding ways to quantify what is called today the economic value brought by the existence of green areas in cities.green spaces, human health, social benefits, hedonic value.

StarBorn : Towards making in-situ land cover data generation fun with a location-based game

University Research Priority Program: Language and Space, University of ZurichPeer reviewedPublisher PD