Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

Leandro Parente,Evandro Taquary,Ana Silva,Laerte Ferreira,Carlos Souza

doi:10.3390/rs11232881

Leandro Parente, Evandro Taquary + Show 3 more

Open Access

https://doi.org/10.3390/rs11232881

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Journal: Remote Sensing	Publication Date: Dec 3, 2019
Citations: 43	License type: CC BY 4.0

Affiliation: Universidade Federal de Goiás, Imazon

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).

Highlights

The science and technology of remote sensing has reached the era of big data [1]
Aiming to contribute to the generation of land-cover and land-use maps, we evaluated the performance of random forest, long short-term memory (LSTM), and U-Net algorithms, which were applied to a large dataset of PlanetScope satellite imagery, and implemented in the Google Earth Engine and in a local infrastructure using Tensorflow
We focused the implementation of machine learning and deep learning algorithms on the mapping of pasture areas, one of the most complex LULC classes to map [33], using PlanetScope imagery

Summary

Introduction

The science and technology of remote sensing has reached the era of big data [1]. This imposes a paradigm change in the way remote sensing data is processed to extract information for a variety of environmental and societal applications. The challenges involved in this process have led to the emergence of cloud-based platforms, for remote sensing, that can perform planetary-scale analysis of massive amounts of data [4,5,6,7] The combination of these factors, i.e., the high availability of satellite data and adequate analysis platforms, has enabled the emergence of terrestrial coverage mappings on global [8,9,10], continental [11,12,13], and national [14,15,16] scales, which are useful for supporting decision-making and broader policy objectives [17]. These mappings were produced with supervised classifications using conventional machine learning algorithms (e.g., random forest, classification and regression tree-CART) implemented on platforms with thousands of processors capable of handling communication, parallelism, and distributed computing problems [18]

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