• Corpus ID: 245353876

Deep Learning and Earth Observation to Support the Sustainable Development Goals

@article{Persello2021DeepLA,
  title={Deep Learning and Earth Observation to Support the Sustainable Development Goals},
  author={Claudio Persello and Jan Dirk Wegner and Ronny H{\"a}nsch and Devis Tuia and Pedram Ghamisi and Mila Koeva and G. Camps-Valls},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.11367}
}
This is the pre-acceptance version of the paper. The final version will appear in the IEEE Geoscience and Remote Sensing Magazine. The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the living planet challenges. This paper reviews current deep learning approaches for Earth observation data, along… 

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References

SHOWING 1-10 OF 279 REFERENCES

Deep learning in environmental remote sensing: Achievements and challenges

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.

Deep learning and process understanding for data-driven Earth system science

It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.

A deep learning approach for early wildfire detection from hyperspectral satellite images

A novel wildfire detection method that utilises satellite images in an advanced deep learning architecture for locating wildfires at pixel level is proposed and empirical evaluations show the superiorperformance of the approach over the baselines with 94% F1- score and 1.5 times faster detections.

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

An introduction to the most relevant deep learning models and concepts is provided, point out possible pitfalls by analyzing special characteristics of SAR data, review the state of the art of deep learning applied to SAR, summarize available benchmarks, and recommend some important future research directions.

Understanding deep learning in land use classification based on Sentinel-2 time series

This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network.

The Sustainable Development Goals need geoscience

  • M. Scown
  • Environmental Science
    Nature Geoscience
  • 2020
To the Editor — The United Nations 2030 Agenda and its 17 Sustainable Development Goals (SDGs) represent the global strategy for achieving a better future for all. Yet, the Earth subsystems required

Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

This study attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019, and found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure.

The challenges of a Big Data Earth

A digital Earth is also capable of being represented mathematically as a digitally networked phenomenon, analogous to an analogue computer, and should be an important target for a Big Earth Data Journal.
...