TY - GEN
T1 - Land use and land cover classification for Bangladesh 2005 on google earth engine
AU - Yu, Zhiqi
AU - Di, Liping
AU - Tang, Junmei
AU - Zhang, Chen
AU - Lin, Li
AU - Yu, Eugene Genong
AU - Rahman, Md Shahinoor
AU - Gaigalas, Juozas
AU - Sun, Ziheng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/27
Y1 - 2018/9/27
N2 - Land use and land cover maps are essential to study how the earth surface change over time and how human activities interact with environments. The growing amount of available remote sensing images, especially the well archived Landsat images with 30 meters resolution, have been used to conduct supervised classification for land use and land cover maps. However, to achieve high classification accuracy, ground truth samples with fine quality and large quantity are required. Collecting ground truth samples is both time-consuming and expensive and sometimes even unviable when ground truth samples are needed for the past years. In this paper, we provided a way of using the GlobeLand30 (GLC30) 2000 and 2010 products as ground truth instead of manually labeling ground truth samples to produce land use and land cover maps for 2005 in our study area, Bangladesh country on Google Earth Engine (GEE) platform. The accuracy assessment is conducted on randomly generated samples from GlobeLand30 products, and the overall accuracy is around 84.8%.
AB - Land use and land cover maps are essential to study how the earth surface change over time and how human activities interact with environments. The growing amount of available remote sensing images, especially the well archived Landsat images with 30 meters resolution, have been used to conduct supervised classification for land use and land cover maps. However, to achieve high classification accuracy, ground truth samples with fine quality and large quantity are required. Collecting ground truth samples is both time-consuming and expensive and sometimes even unviable when ground truth samples are needed for the past years. In this paper, we provided a way of using the GlobeLand30 (GLC30) 2000 and 2010 products as ground truth instead of manually labeling ground truth samples to produce land use and land cover maps for 2005 in our study area, Bangladesh country on Google Earth Engine (GEE) platform. The accuracy assessment is conducted on randomly generated samples from GlobeLand30 products, and the overall accuracy is around 84.8%.
KW - GlobeLand30
KW - Google Earth Engine
KW - Land use and Land cover
KW - Supervised Classification
UR - http://www.scopus.com/inward/record.url?scp=85055874067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055874067&partnerID=8YFLogxK
U2 - 10.1109/Agro-Geoinformatics.2018.8475976
DO - 10.1109/Agro-Geoinformatics.2018.8475976
M3 - Conference contribution
AN - SCOPUS:85055874067
T3 - 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
BT - 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
Y2 - 6 August 2018 through 9 August 2018
ER -
