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Special Issue "Citizen Science and Earth Observation II"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editors

Guest Editor
Dr. Steffen Fritz

International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Website | E-Mail
Phone: +43 (2236) 807-353
Interests: remote sensing, cropland, crowdsourcing, mapping uncertainty, climate change, agricultural monitoring
Guest Editor
Dr. Cidalia C Fonte

Institute for Systems and Computers Engineering at Coimbra, Department of Mathematics, University of Coimbra, 3001-501 Coimbra, Portugal
E-Mail
Phone: +351-239 4729392
Fax: +351-239 793069
Interests: spatial data validation and quality assessment; land use land cover mapping; volunteered geographic information; spatial data integration; remote sensing

Special Issue Information

Dear Colleagues,

The term citizen science is used when scientific work is performed partially, or completely, by volunteers, which are usually non-experts. During the last decade, citizen science and projects that are based on user-generated content have increased dramatically. Citizen science in the field of Earth observation has started more recently and a number of projects have evolved which involve citizens in monitoring the environment. Furthermore, citizen-based observations can support earth observation in a number of different fields such as climate change, sustainable development, drought monitoring, land cover or land-use change. Moreover, there is the potential to use citizen-based observations in combination with other currently increasing earth observation data from new sensors such as the Sentinel family of satellites and Landsat. In particular, in situ data provided by citizens can be used for calibration and validation activities, as well as the conflation or combined use of satellite and citizen observations.
In 2016, we edited a Special Issue of Remote Sensing that presented a broad view of the state-of-the-art in Citizen Science and Earth Observation; it is now time to revisit the topic. The proposed Special Issue welcomes contributions in the field of Earth observation and its applications with respect to:

  • Methods for citizen-based data collection
  • Innovative use of citizen observations
  • Mobilization of citizens
  • Combined use of satellite and citizen-based observations
  • Contributions of citizen observations to support authoritative data
  • Quality of citizen-based observations
  • Data conflation and data mining
  • Contributions of citizen observations to support the sustainable development goals

Dr. Steffen Fritz
Dr. Cidália Costa Fonte
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • citizen science
  • applications of earth observations
  • crowdsourcing
  • map validation
  • data quality
  • user generated content
  • incentives to mobilize the crowd

Published Papers (6 papers)

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Research

Open AccessArticle
Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning
Remote Sens. 2019, 11(15), 1799; https://doi.org/10.3390/rs11151799
Received: 26 June 2019 / Revised: 22 July 2019 / Accepted: 26 July 2019 / Published: 31 July 2019
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Abstract
Reliable techniques to generate accurate data sets of human built-up areas at national, regional, and global scales are a key factor to monitor the implementation progress of the Sustainable Development Goals as defined by the United Nations. However, the scarce availability of accurate [...] Read more.
Reliable techniques to generate accurate data sets of human built-up areas at national, regional, and global scales are a key factor to monitor the implementation progress of the Sustainable Development Goals as defined by the United Nations. However, the scarce availability of accurate and up-to-date human settlement data remains a major challenge, e.g., for humanitarian organizations. In this paper, we investigated the complementary value of crowdsourcing and deep learning to fill the data gaps of existing earth observation-based (EO) products. To this end, we propose a novel workflow to combine deep learning (DeepVGI) and crowdsourcing (MapSwipe). Our strategy for allocating classification tasks to deep learning or crowdsourcing is based on confidence of the derived binary classification. We conducted case studies in three different sites located in Guatemala, Laos, and Malawi to evaluate the proposed workflow. Our study reveals that crowdsourcing and deep learning outperform existing EO-based approaches and products such as the Global Urban Footprint. Compared to a crowdsourcing-only approach, the combination increased the quality (measured by Matthew’s correlation coefficient) of the generated human settlement maps by 3 to 5 percentage points. At the same time, it reduced the volunteer efforts needed by at least 80 percentage points for all study sites. The study suggests that for the efficient creation of human settlement maps, we should rely on human skills when needed and rely on automated approaches when possible. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Open AccessArticle
Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas
Remote Sens. 2019, 11(1), 88; https://doi.org/10.3390/rs11010088
Received: 8 November 2018 / Revised: 25 December 2018 / Accepted: 28 December 2018 / Published: 7 January 2019
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Abstract
Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for [...] Read more.
Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics. Moreover, a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area. Experiments were conducted on the data captured over Wuhan city, and three state-of-the-art approaches were adopted for comparison. Results show that the proposed approach obtains satisfactory results and outperforms the other comparative approaches. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Open AccessArticle
Methodology for Participatory GIS Risk Mapping and Citizen Science for Solotvyno Salt Mines
Remote Sens. 2018, 10(11), 1828; https://doi.org/10.3390/rs10111828
Received: 28 September 2018 / Revised: 23 October 2018 / Accepted: 14 November 2018 / Published: 19 November 2018
Cited by 4 | PDF Full-text (6711 KB) | HTML Full-text | XML Full-text
Abstract
The Horizon 2020 interim evaluation (2017) indicates a steep increase in citizen engagement in European Union Citizen Science (CS) projects, with less than 1% in budgetary terms and minimal influence. Research findings attribute weak CS influence to the restriction of citizen actions to [...] Read more.
The Horizon 2020 interim evaluation (2017) indicates a steep increase in citizen engagement in European Union Citizen Science (CS) projects, with less than 1% in budgetary terms and minimal influence. Research findings attribute weak CS influence to the restriction of citizen actions to data collection, with minimal or no engagement in co-design, co-creation, data analysis, and elucidation of results. We design a participatory GIS and CS methodology aimed at engaging the citizens in the entire Earth Observation (EO) project cycle. The methodology also seeks to address previous CS project challenges related to data quality, data interoperability, citizen-motivation, and participation. We draw the high-level requirements from the SENDAI framework of action and the three pillars of active citizen engagement, as enshrined in Principle 10 of the Rio Declaration and the Aarhus Convention. The primary input of the methodology is the Haklay (2018) approach for participatory mapping and CS, and the Reed (2009) stakeholder analysis framework. The proposed methodology comprises of three main parts: system analysis, stakeholder analysis, and a six-step methodology. We designed the six-step methodology using an iterative and flexible approach, to take account of unforeseen changes. Future research will focus on implementing the methodology and evaluating its effectiveness in the Solotvyno Saltmine case study in Ukraine. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Open AccessArticle
Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap
Remote Sens. 2018, 10(7), 1145; https://doi.org/10.3390/rs10071145
Received: 19 June 2018 / Revised: 12 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
Cited by 1 | PDF Full-text (14504 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban [...] Read more.
The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban environments. However, at a large scale, the process of collecting training samples becomes a huge project in itself. This leads to a growing interest from the remote sensing community toward Volunteered Geographic Information (VGI) projects such as OpenStreetMap (OSM). Despite the spatial heterogeneity of its contribution patterns, OSM provides an increasing amount of information on the earth’s surface. More interestingly, the community has moved beyond street mapping to collect a wider range of spatial data such as building footprints, land use, or points of interest. In this paper, we propose a classification method that makes use of OSM to automatically collect training samples for supervised learning of built-up areas. To take into account a wide range of potential issues, the approach is assessed in ten Sub-Saharan African urban areas from various demographic profiles and climates. The obtained results are compared with: (1) existing high resolution global urban maps such as the Global Human Settlement Layer (GHSL) or the Human Built-up and Settlements Extent (HBASE); and (2) a supervised classification based on manually digitized training samples. The results suggest that automated supervised classifications based on OSM can provide performances similar to manual approaches, provided that OSM training samples are sufficiently available and correctly pre-processed. Moreover, the proposed method could reach better results in the near future, given the increasing amount and variety of information in the OSM database. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Graphical abstract

Open AccessArticle
Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach
Remote Sens. 2018, 10(7), 1134; https://doi.org/10.3390/rs10071134
Received: 23 April 2018 / Revised: 5 July 2018 / Accepted: 15 July 2018 / Published: 18 July 2018
Cited by 1 | PDF Full-text (10061 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To [...] Read more.
This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F1 score (F1) = 0.918), and a fair positional accuracy (root mean square error(RMSE) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Graphical abstract

Open AccessArticle
Is Ocean Reflectance Acquired by Citizen Scientists Robust for Science Applications?
Remote Sens. 2018, 10(6), 835; https://doi.org/10.3390/rs10060835
Received: 2 May 2018 / Revised: 18 May 2018 / Accepted: 24 May 2018 / Published: 26 May 2018
Cited by 2 | PDF Full-text (7638 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring the dynamics of the productivity of ocean water and how it affects fisheries is essential for management. It requires data on proper spatial and temporal scales, which can be provided by operational ocean colour satellites. However, accurate productivity data from ocean colour [...] Read more.
Monitoring the dynamics of the productivity of ocean water and how it affects fisheries is essential for management. It requires data on proper spatial and temporal scales, which can be provided by operational ocean colour satellites. However, accurate productivity data from ocean colour imagery is only possible with proper validation of, for instance, the atmospheric correction applied to the images. In situ water reflectance data are of great value due to the requirements for validation and reflectance is traditionally measured with the Surface Acquisition System (SAS) solar tracker system. Recently, an application for mobile devices, “HydroColor”, was developed to acquire water reflectance data. We examined the accuracy of the water reflectance measures acquired by HydroColor with the help of both trained and untrained citizens, under different environmental conditions. We used water reflectance data acquired by SAS solar tracker and by HydroColor onboard the BC ferry Queen of Oak Bay from July to September 2016. Monte Carlo permutation F tests were used to assess whether the differences between measurements collected by SAS solar tracker and HydroColor with citizens were significant. Results showed that citizen HydroColor measurements were accurate in red, green, and blue bands, as well as red/green and red/blue ratios under different environmental conditions. In addition, we found that a trained citizen obtained higher quality HydroColor data especially under clear skies at noon. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Graphical abstract

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