Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data. Parente, L., Taquary, E., Silva, A. P., Souza, C., & Ferreira, L. Remote Sensing, 11(23):2881, January, 2019. ![Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8).
@article{parente_next_2019,
title = {Next {Generation} {Mapping}: {Combining} {Deep} {Learning}, {Cloud} {Computing}, and {Big} {Remote} {Sensing} {Data}},
volume = {11},
copyright = {http://creativecommons.org/licenses/by/3.0/},
shorttitle = {Next {Generation} {Mapping}},
url = {https://www.mdpi.com/2072-4292/11/23/2881},
doi = {10.3390/rs11232881},
abstract = {The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94\%, 98.83\%, and 95.53\% in the validation data, and 94.06\%, 87.97\%, and 82.57\% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8).},
language = {en},
number = {23},
urldate = {2019-12-05},
journal = {Remote Sensing},
author = {Parente, Leandro and Taquary, Evandro and Silva, Ana Paula and Souza, Carlos and Ferreira, Laerte},
month = jan,
year = {2019},
keywords = {LSTM, LULC classification, PlanetScope, U-Net, deep learning, machine learning, random forest},
pages = {2881}
}
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Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. 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