Power to the people: Applying citizen science and computer vision to home mapping for rural energy access

Leonard, Alycia; Wheeler, Scot; McCulloch, Malcolm

doi:10.1016/j.jag.2022.102748

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…It also had a forum called "Talk" where citizen scientists could discuss images which caused them difficulty, ask questions, and chat. Further details about the technical implementation of the project are provided in (Leonard, Wheeler, and Mcculloch 2022).…”

Section: About Power To the Peoplementioning

confidence: 99%

“…The annotations were analyzed for accuracy based on precision, recall, and F1 score (i.e., the harmonic mean of precision and recall), and visualized for intuitive understanding. More details on the technical specifications of data and a full analysis are available in (Leonard, Wheeler, and Mcculloch 2022).…”

Section: Project Datamentioning

confidence: 99%

“…This paper investigates the impacts of an online remote mapping citizen science project and explores best practices for mutual benefit to contributors and professional researchers. We evaluate PTTP, an online citizen science project that mapped homes in rural Uganda, Kenya, and Sierra Leone for electrical system planning (Leonard, Wheeler, and Mcculloch 2022) and created a training dataset for computer vision McCulloch 2022a, 2022b). 1 Through analysis of beta feedback, discussion board posts, an evaluation survey, and project data, we study the community composition of PTTP contributors, their learnings, their motivations, and their experiences.…”

Section: Introductionmentioning

confidence: 99%

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Does Citizen Science Bring “Power to the People”? Evaluating a Remote Mapping Project to Identify Best Practices for Positive Impact on Volunteers

Leonard

Wheeler

McCulloch

2023

CSTP

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A core tenet of citizen science is mutual benefit to the professional researcher and the citizen scientist. While the impacts on the citizen scientist are often implicitly assumed to be positive, this is infrequently studied directly. Here, we evaluate the impacts of the Power to the People remote mapping citizen science project on volunteers to explore best practices for positive impact. We analyze beta feedback collected before project launch, discussion board posts made during the project, an end-of-project evaluation survey, and mapping data generated during the project. We found that this project attracted a diverse global community who were motivated to contribute to research with the potential to create real-world impact. 87% of respondents had a "good" or "excellent" experience with the project, and 66% learned something by participating. Best-practices identified through this evaluation are to: (1) account for the intersectionality of contributor demographics; (2) emphasize project interdisciplinarity and real-world impact potential;(3) provide learning opportunities at multiple levels of depth; (4) remember that the most vocal contributors do not represent the entire community; and (5) evaluate data quality regularly to identify silent issues.

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Section: About Power To the Peoplementioning

confidence: 99%

Section: Project Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Does Citizen Science Bring “Power to the People”? Evaluating a Remote Mapping Project to Identify Best Practices for Positive Impact on Volunteers

Leonard

Wheeler

McCulloch

2023

CSTP

Self Cite

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“…Additionally, the Convolutional Neural Network (CNN) architecture used, and the type of satellite data chosen to make it infeasible to map large spatial extensions. In the research done by Leonard et al 12 , they applied citizen science through a project called "Power to the People", paired with satellite imagery and computer vision to map remote off-grid homes for electrical system design. This research shows citizen science and computer vision to be a promising pipeline for rural home-level mapping.…”

Section: Introductionmentioning

confidence: 99%

Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks

Arrechea-Castillo,

Solano-Correa,

Muñoz-Ordóñez

et al. 2023

Geospatial Informatics XIII

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The Sustainable Development Goal (SDG) number 11 aims at making cities and human settlements more inclusive, safe, resilient, and sustainable. Complying with SDG 11 is a difficult task, especially when considering rural settlements where: (i) population settles in a dispersed manner; and (ii) geography complexity and social dynamics of the area make it difficult to monitor and capture data. One example of such areas can be found in the South-West of Colombia, in the Las Piedras River sub-basin. The National Administrative Department of Statistics in Colombia (DANE in Spanish) aims at mapping the population and houses in dispersed and difficult-to-access rural settlements in an accurate and continuous way. Nevertheless, there are several difficulties (derived from the in-situ way of collecting the data) that prevent such data from being generated. This research presents a methodology to carry out an updated mapping of rural areas with high spatial resolution data coming from PlanetScope (3 m). Such a mapping considers the dynamics of housing growth, focusing on dispersed and difficult-to-access rural settlements. To this aim, Convolutional Neural Networks (CNNs) are used together with PlanetScope data, allowing to account for average houses size (≥12 𝑚2) in the study area. Preliminary results show a detection accuracy above 95%, in average, according to geography complexity.

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“…This dataset was produced through a citizen science project called “Power to the People”, which mapped rural homes for electrical infrastructure planning and computer-vision-based mapping. Additional details on this work are presented in “Power to the People: Applying citizen science to home-level mapping for rural energy access” [1] . 578,010 home annotations were made on approximately 1,267 km 2 of imagery over 179 days by over 6,000 volunteers.…”

mentioning

confidence: 99%

Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery

2022

Self Cite

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Power to the people: Applying citizen science and computer vision to home mapping for rural energy access

Cited by 6 publications

References 20 publications

Does Citizen Science Bring “Power to the People”? Evaluating a Remote Mapping Project to Identify Best Practices for Positive Impact on Volunteers

Does Citizen Science Bring “Power to the People”? Evaluating a Remote Mapping Project to Identify Best Practices for Positive Impact on Volunteers

Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks

Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery

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