Land Use and Land Cover Classification for Bangladesh 2005 on Google Earth Engine. Yu, Z., Di, L., Tang, J., Zhang, C., Lin, L., Yu, E. G., Rahman, M. S., Gaigalas, J., & Sun, Z. In 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics), pages 1–5, August, 2018.
doi  abstract   bibtex   
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%.
@inproceedings{yu_land_2018,
	title = {Land {Use} and {Land} {Cover} {Classification} for {Bangladesh} 2005 on {Google} {Earth} {Engine}},
	doi = {10.1109/Agro-Geoinformatics.2018.8475976},
	abstract = {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\%.},
	booktitle = {2018 7th {International} {Conference} on {Agro}-geoinformatics ({Agro}-geoinformatics)},
	author = {Yu, Z. and Di, L. and Tang, J. and Zhang, C. and Lin, L. and Yu, E. G. and Rahman, M. S. and Gaigalas, J. and Sun, Z.},
	month = aug,
	year = {2018},
	pages = {1--5},
	file = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/AE49K6JH/8475976.html:text/html}
}
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