30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine

no code implementations • 7 May 2018 • Tengfei Long, Zhaoming Zhang, Guojin He, Weili Jiao, Chao Tang, Bingfang Wu, Xiaomei Zhang, Guizhou Wang, Ranyu Yin

Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution.

Fire Detection Time Series +1

Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method

no code implementations • 18 Mar 2018 • Xin Zhang, Bingfang Wu, Liang Zhu, Fuyou Tian, Miao Zhang, Yuanzeng

In this paper, we first test the state of the art semantic segmentation deep learning classifiers for LUCC mapping with 7 categories in the TGRA area with rapideye 5m resolution data.

Semantic Segmentation