- Computer Science
conference paper description
Plantation mapping is important for understanding deforestation and climate change. Most existing plantation products rely heavily on visual interpretation of satellite imagery, which results in both false positives and false negatives. In this paper we aim to design an automatic framework that map plantations in large regions. Conventional classification methods cannot be directly applied due to the lack of ground-truth data. To this end, we propose a novel method that learns from multiple imperfect annotators. Since each annotator's labeling accuracy varies across different land covers due to his expertise and reference imagery, we model the annotator's reliability level to be associated with different types of locations. On the other hand, the temporal variation of land covers also greatly impacts the performance of conventional learning model. Therefore we utilize the remote sensing data which are available at multiple periods of a year and extend our proposed method by incorporating multi-instance learning. Finally, we show the superiority of the proposed method over multiple baselines in both synthetic dataset and real-world dataset. In addition, through several case studies we demonstrate that our method can achieve a better balance of precision and recall than the existing plantation products.