Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.
|Original language||English (US)|
|Title of host publication||Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|State||Published - 2019|
|Event||28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China|
Duration: Aug 10 2019 → Aug 16 2019
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Conference||28th International Joint Conference on Artificial Intelligence, IJCAI 2019|
|Period||8/10/19 → 8/16/19|
Bibliographical noteFunding Information:
This work was funded by the NSF awards 1838159 and 1739191. Access to computing facilities was provided by Minnesota Supercomputing Institute.
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