Abstract
Plantation mapping is important for understanding deforestation and climate change. While most existing plantation products are created manually, in this paper we study an ensemble learning based framework for automatically mapping plantations in southern Kalimantan on a yearly scale using remote sensing data. We study the effectiveness of several components in this framework, including class aggregation, data sampling, learning model selection and post-processing, by comparing with multiple baselines. In addition, we analyze the quality of our plantation mapping product by visual examination of high resolution images. We also compare our method to existing manually labeled plantation datasets and show that our method can achieve a better balance of precision (i.e., user's accuracy) and recall (i.e., producer's accuracy).
Original language | English (US) |
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Article number | 46 |
Journal | Frontiers in Big Data |
Volume | 2 |
DOIs | |
State | Published - Dec 6 2019 |
Bibliographical note
Funding Information:Funding. This work was funded by the NSF Award 1029711 and NSF Big Data Award 1838159. JG and PW were partially supported by the Belmont Forum/FACCE-JPI funded DEVIL project (NE/M021327/1). Access to computing facilities was provided by Minnesota Supercomputing Institute.
Publisher Copyright:
Copyright © 2019 Jia, Khandelwal, Carlson, Gerber, West and Kumar.
Keywords
- deep learning
- deforestation
- ensemble learning
- plantation
- remote sensing