Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of sugarcane plantations would help in formulating effective strategies to reduce burning. High-resolution satellite imagery combined with deep-learning technologies can be effective to map sugarcane with high precision. However, land cover mapping using high-resolution data and computationally intensive deep-learning networks can be computationally costly. In this study, we used high-resolution satellite imagery from Planet that has been made available to the public through Norway’s International Climate and Forest Initiative (NICFI). We tested a U-Net deep-learning algorithm with a lightweight MobileNetV2 network as the encoder branch using the Google Earth Engine computational platform. We trained a model using the RGB channels with a pre-trained network (RGBt), an RGB model with randomly initialized weights (RGBr), and a model with randomly initialized weights including the NIR channel (RGBN). We found an F1-score of 0.9550, 0.9262, and 0.9297 for the RGBt, RGBr, and RGBN models, respectively. For an independent model evaluation, we found F1-scores of 0.9141, 0.8681, and 0.8911. We also found a discrepancy in the recall values reported by the model and those from the independent validation. We found that lightweight deep-learning models produce satisfactory results while providing effective means to apply mapping efforts at scale with reduced computational costs. We highlight the importance of central data repositories with labeled data as pre-trained networks were found to be effective in improving the accuracy.
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