Data quality in citizen science urban tree inventories

Roman, Lara A.; Scharenbroch, Bryant C.; Östberg, Johan; Mueller, Lee S.; Henning, Jason G.; Koeser, Andrew K.; Sanders, Jessica R.; Betz, Daniel R.; Jordan, Rebecca

doi:10.1016/j.ufug.2017.02.001

Cited by 81 publications

(97 citation statements)

References 42 publications

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“…This approach delivers the correct result in > 90% of the cases for three different change categories unchanged, removed, and new tree planted. We compare the system's performance with a recent study on manual collection of inventories of Roman et al (2017). The authors found that citizen scientists, i.e.…”

Section: Discussionmentioning

confidence: 99%

From Google Maps to a fine-grained catalog of street trees

Branson

Wegner

Hall

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

129

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Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google Maps TM . These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect > 70% of the street trees, assign correct species to > 80% for 40 different species, and correctly detect and classify changes in > 90% of the cases.

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Section: Discussionmentioning

confidence: 99%

From Google Maps to a fine-grained catalog of street trees

Branson

Wegner

Hall

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

129

View full text Add to dashboard Cite

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“…Moreover, to monitor tree resources temporally, the quality component of the timeliness of VGI has to be screened in the crowdsourced database (for more details [90]). Only four previous independent studies on VGI quality in tree inventories are recognized in the literature (for more details see [14,[91][92][93]). These previous studies found that while some of the tree attributes (variables) were measured with lower accuracy by volunteers, generally, there was a high potential to use in situ crowdsourced data, particularly for tree inventory projects, where slightly lower quality levels are acceptable (for more details see [14,[91][92][93]).…”

Section: Crowdsourcingmentioning

confidence: 99%

“…Only four previous independent studies on VGI quality in tree inventories are recognized in the literature (for more details see [14,[91][92][93]). These previous studies found that while some of the tree attributes (variables) were measured with lower accuracy by volunteers, generally, there was a high potential to use in situ crowdsourced data, particularly for tree inventory projects, where slightly lower quality levels are acceptable (for more details see [14,[91][92][93]). It is notable that all of the previous studies focused on the quality assessment of crowdsourced data gathered through the outdoor mapping approach, while none have investigated the quality of data gathered through a remote mapping crowdsourcing approach, despite the popularity of the remote mapping approach for collecting individual tree inventory data.…”

Section: Crowdsourcingmentioning

confidence: 99%

“…The ex-post category includes all the VGI quality improvement approaches in which the data quality improvement task is conducted after the submission of VGI [151]. The providing of training courses and auxiliary learning materials for the volunteers [14,152,153], using gamification [154][155][156], running BioBlitz [157] events, employing expert-based/community-based VGI quality screening methods (and removing/correcting the errors) [29,158], and using VGI quality screening methods (and removing/correcting the errors) based on intrinsic or extrinsic VGI quality indicators for detecting errors [30,90,150,158,159] are examples of ex-ante and ex-post approaches for VGI quality improvement. Nevertheless, before adopting any ex-ante or ex-post VGI quality improvement approaches, it is necessary to understand the initial impacts (positive and negative) of different factors on the quality level of the VGI, recognize their affecting mechanisms, and control them as best as possible to the achieve an acceptable quality level for conducting the supplementary quality improvement processes.…”

Section: The Feasibility Of Large-scale Remote Mapping Of Urban Orchamentioning

confidence: 99%

“…Four major types of mapping techniques exist for inventory of trees at the individual tree level, namely, (1) field surveys; (2) terrestrial scanning and close-range photogrammetry; (3) aerial and satellite remote sensing; and (4) crowdsourcing [12][13][14] (for more details, see Section 2.1). Depending on the nature of the adopted technique and the characteristics of the deployed sensor for mapping of the tree crowns, each technique has some strong points and limitations for capturing the information about the tree crowns (for more details, see Section 2.1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2018

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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 F 1 score (F 1 ) = 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.

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Citizens and cities: Leveraging citizen science and big data for sustainable urban development

2021

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Citizen science (CS), that is, the involvement of citizens in data collection or analysis for research projects, is becoming more widespread. This is due to the increasing digitalization of the general public and due to the increasing number of grand challenges that society is facing. Thanks to the contributions of common citizens in data collection and data analysis conducted through technology‐mediated interactions, CS can produce a number of benefits for researchers, public organizations, policymakers, citizens, and society as a whole. Given the high density of socio‐economic activities in cities, CS can be implemented in a particularly effective way in urban environments to help tackle many “grand challenges”, namely, the pressing environmental and social issues that societies are facing at present. However, CS still has untapped potential to be explored. Indeed, we contend that even though CS involves citizens for precisely defined scientific objectives, the interaction that occurs can also be leveraged to collect data beyond the original aim, thereby producing big data (BD). Through a multiple case studies analysis, we highlight how CS can be used to collect BD as well, which can be a valuable resource for researchers, public organizations, and policymakers. With this aim in mind, this study proposes the definition of a citizen‐sourcing framework that jointly employs CS and BD, and it highlights which processes can be implemented to favor the sustainable development of urban environments. Moreover, we also discuss the looming dangers associated with citizen‐sourcing as a result of technology‐mediated interactions and the use of digital technologies, and we highlight possible future developments.

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Data quality in citizen science urban tree inventories

Cited by 81 publications

References 42 publications

From Google Maps to a fine-grained catalog of street trees

From Google Maps to a fine-grained catalog of street trees

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

Citizens and cities: Leveraging citizen science and big data for sustainable urban development

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