Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery

This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. This dataset was produced through a citizen science project called “Power to the People”, which mapped rural homes for electrical infrastructure planni...
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Autor*in:	Alycia Leonard [verfasserIn] Scot Wheeler [verfasserIn] Malcolm McCulloch [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision

Übergeordnetes Werk:	In: Data in Brief - Elsevier, 2015, 42(2022), Seite 108262-
Übergeordnetes Werk:	volume:42 ; year:2022 ; pages:108262-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.dib.2022.108262

Katalog-ID:	DOAJ027711579

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allfields	10.1016/j.dib.2022.108262 doi (DE-627)DOAJ027711579 (DE-599)DOAJdc0e73c8cefe4e76999a1752a5853024 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Alycia Leonard verfasserin aut Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health. Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision Computer applications to medicine. Medical informatics Science (General) Scot Wheeler verfasserin aut Malcolm McCulloch verfasserin aut In Data in Brief Elsevier, 2015 42(2022), Seite 108262- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:42 year:2022 pages:108262- https://doi.org/10.1016/j.dib.2022.108262 kostenfrei https://doaj.org/article/dc0e73c8cefe4e76999a1752a5853024 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340922004644 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 108262-
spelling	10.1016/j.dib.2022.108262 doi (DE-627)DOAJ027711579 (DE-599)DOAJdc0e73c8cefe4e76999a1752a5853024 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Alycia Leonard verfasserin aut Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health. Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision Computer applications to medicine. Medical informatics Science (General) Scot Wheeler verfasserin aut Malcolm McCulloch verfasserin aut In Data in Brief Elsevier, 2015 42(2022), Seite 108262- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:42 year:2022 pages:108262- https://doi.org/10.1016/j.dib.2022.108262 kostenfrei https://doaj.org/article/dc0e73c8cefe4e76999a1752a5853024 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340922004644 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 108262-
allfields_unstemmed	10.1016/j.dib.2022.108262 doi (DE-627)DOAJ027711579 (DE-599)DOAJdc0e73c8cefe4e76999a1752a5853024 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Alycia Leonard verfasserin aut Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health. Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision Computer applications to medicine. Medical informatics Science (General) Scot Wheeler verfasserin aut Malcolm McCulloch verfasserin aut In Data in Brief Elsevier, 2015 42(2022), Seite 108262- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:42 year:2022 pages:108262- https://doi.org/10.1016/j.dib.2022.108262 kostenfrei https://doaj.org/article/dc0e73c8cefe4e76999a1752a5853024 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340922004644 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 108262-
allfieldsGer	10.1016/j.dib.2022.108262 doi (DE-627)DOAJ027711579 (DE-599)DOAJdc0e73c8cefe4e76999a1752a5853024 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Alycia Leonard verfasserin aut Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health. Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision Computer applications to medicine. Medical informatics Science (General) Scot Wheeler verfasserin aut Malcolm McCulloch verfasserin aut In Data in Brief Elsevier, 2015 42(2022), Seite 108262- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:42 year:2022 pages:108262- https://doi.org/10.1016/j.dib.2022.108262 kostenfrei https://doaj.org/article/dc0e73c8cefe4e76999a1752a5853024 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340922004644 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 108262-
allfieldsSound	10.1016/j.dib.2022.108262 doi (DE-627)DOAJ027711579 (DE-599)DOAJdc0e73c8cefe4e76999a1752a5853024 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Alycia Leonard verfasserin aut Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health. Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision Computer applications to medicine. Medical informatics Science (General) Scot Wheeler verfasserin aut Malcolm McCulloch verfasserin aut In Data in Brief Elsevier, 2015 42(2022), Seite 108262- (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:42 year:2022 pages:108262- https://doi.org/10.1016/j.dib.2022.108262 kostenfrei https://doaj.org/article/dc0e73c8cefe4e76999a1752a5853024 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340922004644 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 108262-
language	English
source	In Data in Brief 42(2022), Seite 108262- volume:42 year:2022 pages:108262-
sourceStr	In Data in Brief 42(2022), Seite 108262- volume:42 year:2022 pages:108262-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Citizen science Remote sensing Geographic information systems Online participation Satellite mapping Computer vision Computer applications to medicine. Medical informatics Science (General)
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container_title	Data in Brief
authorswithroles_txt_mv	Alycia Leonard @@aut@@ Scot Wheeler @@aut@@ Malcolm McCulloch @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	797838937
id	DOAJ027711579
language_de	englisch
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callnumber-first	R - Medicine
author	Alycia Leonard
spellingShingle	Alycia Leonard misc R858-859.7 misc Q1-390 misc Citizen science misc Remote sensing misc Geographic information systems misc Online participation misc Satellite mapping misc Computer vision misc Computer applications to medicine. Medical informatics misc Science (General) Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery
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topic	misc R858-859.7 misc Q1-390 misc Citizen science misc Remote sensing misc Geographic information systems misc Online participation misc Satellite mapping misc Computer vision misc Computer applications to medicine. Medical informatics misc Science (General)
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title	Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery
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title_full	Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery
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title_sort	rural home annotation dataset mapped by citizen scientists in satellite imagery
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title_auth	Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery
abstract	This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health.
abstractGer	This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health.
abstract_unstemmed	This article presents a geolocated dataset of rural home annotations on very high resolution satellite imagery from Uganda, Kenya, and Sierra Leone. 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 km2 of imagery over 179 days by over 6,000 volunteers. The bounding-box annotations produced in this work have been anonymized and georeferenced. These raw annotations were found to have a precision of 49% and recall of 93% compared to a researcher-generated set of gold standard annotations. Data on roof colour and shape were also collected and are provided. Metadata about the sensors used to capture the original images and the annotation process are also attached to data records. While this dataset was collected for electrical infrastructure planning research, it can be useful in diverse sectors, including humanitarian assistance and public health.
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title_short	Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery
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code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">42</subfield><subfield code="j">2022</subfield><subfield code="h">108262-</subfield></datafield></record></collection>
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Rural Home Annotation Dataset Mapped by Citizen Scientists in Satellite Imagery

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