Scalable Detection of Rural Schools in Africa Using Convolutional Neural Networks and Satellite Imagery

Yazdani, Mehrdad; Nguyen, Manh Duc; Block, Jessica; Crawl, Daniel; Zurutuza, Naroa; Kim, Dohyung; Hanson, Gordon H.; Altıntaş, Ilkay

doi:10.1109/ucc-companion.2018.00048

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…For example, from the historical satellite imagery incorporated in Google Earth, we were able to observe from establishing our sample of 1900 schools, that 113 of the schools had been extended and another 130 schools had been demolished between 2011 and 2019. Therefore, we suggest that EO data can provide a reliable, accurate, and convenient means for assessing classroom areas at the national scale and this has the potential to be automated via Artificial Intelligent/machine learning approaches (see Yazdani et al, [50] for identifying rural schools in Liberia). The advantages of this approach are probably most likely to be realised in the developing world where issues of accessibility to rural schools are especially challenging.…”

Section: Discussionmentioning

confidence: 99%

Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

et al. 2022

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Nigeria is a country with a rapidly growing youthful population and the availability of good quality education for all is a key priority in the sustainable development of the country. An important element of this is the need to improve access to high-quality primary education in rural areas. A key indicator for progress on this is the provision of adequate classroom space for the more than 20 million learners in Nigerian public schools because overpopulated classrooms are known to have a strong negative impact on the performance of both pupils and their teachers. However, it can be challenging to rapidly monitor this indicator for the over 60 thousand primary schools, especially in rural areas. In this research, we used satellite Earth Observation (EO) and Nigerian government data to determine the size of available teaching spaces and evaluate the degree of overcrowding in a sample of 1900 randomly selected rural primary schools across 19 Nigerian states spanning all regions of the country. Our analysis shows that 81.4% of the schools examined were overcrowded according to the minimum standard threshold for school size of at least 1.2 m2 of classroom space per pupil defined by the Federal Government of Nigeria. Such overcrowding can be expected to have a negative impact on educational performance, on achieving universal basic education and UN Sustainable Development Goal (SDG) 4 (Quality Education), and it can lead to poverty. While measuring floor area can be performed manually on site, collecting, and reporting such data for the number of rural primary schools in a large and populous country such as Nigeria is a serious, time-consuming administrative task with considerable potential for errors and data gaps. Satellite EO data are readily available including for remote areas, are reproducible and are easy to update over time. This paper provides a proof-of-concept example of how such EO data can contribute to addressing this socio-economic dimension of the SDGs framework.

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Section: Discussionmentioning

confidence: 99%

Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

et al. 2022

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“…Yazdani et al [88] employed satellite imagery to detect rural schools in Africa. Due to the inaccuracy of the labels in their dataset from their government, the authors have decided to approach their model to do unsupervised learning and used ResNet50 for their CNN model.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Detection of Green Roofs through Satellite Imagery Using Faster R-CNN

Marcos

2024

Preprint

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<p>With the threat of global warming, there is a need to implement sustainable solutions. Due to urbanization, forests and other natural resources that could have helped reduce carbon emissions are being depleted. Thus, cities, like Toronto, experience urban heat island effects, food shortages and increased carbon emissions. To help solve this, Green Roofs are encouraged to be implemented in Toronto to help reduce the mentioned issues. In addition, green roofs are difficult to maintain and COVID-19 making it more difficult for urban farmers to physically go and check the green roofs. Hence, this study proposes to use deep learning in detecting green roofs from satellite imagery. The approach uses NDVI values and Toronto Building data to obtain green roof locations. From here, satellite images were taken from map services such as Google Earth Pro and ArcGIS Pro. The images were preprocessed and fed into Faster R-CNN model to detect green roofs. Since this is a first step in creating a system that can monitor the Green Roof Food-Energy-Water nexus, in the future, the behaviour of Faster R-CNN with Toronto Roof Satellite Images was observed. It was shown that the Faster R-CNN provided a higher validation accuracy and higher confidence score than the Fast R-CNN. Additionally, it is suggested that the current Faster R-CNN model needs to be improved, to precisely detect green roofs in different image resolutions and dimensions.</p>

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“…The work in [386] presents a scalable approach using convolutional neural networks and satellite imagery for detecting rural schools in Africa.…”

Section: Liberiamentioning

confidence: 99%

Machine Intelligence in Africa: a survey

Tembine,

Tapo,

Danioko

et al. 2024

Preprint

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Scalable Detection of Rural Schools in Africa Using Convolutional Neural Networks and Satellite Imagery

Cited by 7 publications

References 5 publications

Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

Assessing Education from Space: Using Satellite Earth Observation to Quantify Overcrowding in Primary Schools in Rural Areas of Nigeria

Detection of Green Roofs through Satellite Imagery Using Faster R-CNN

Machine Intelligence in Africa: a survey

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