Image of Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

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Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

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 the 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 pre-trained network (RGBt), a 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.

Ketersediaan
02621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
8 hlm. PDF, 2.575 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
other
Tipe Pembawa
-
Edisi
Vol.1, October 2021
Subjek
Mekong region
High resolution satellite imagery
MobileNetV2
U-net convolutional network
Deep-learning
Artificial intelligence
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine
    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 the 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 pre-trained network (RGBt), a 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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