An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory

Rocco Sedona; Claudia Paris; Liang Tian; Morris Riedel; Gabriele Cavallaro

doi:10.1109/IGARSS46834.2022.9883655

An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory

Rocco Sedona, Claudia Paris, Liang Tian, Morris Riedel, Gabriele Cavallaro

Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents an approach that aims to produce a Time-Series (TS) of consistent Land-Cover (LC) maps, typically needed to perform environmental monitoring. First, it creates an annual training set for each TS to be classified, leveraging on publicly available thematic products. These annual training sets are then used to generate a set of preliminary LC maps that allow for the identification of the unchanged areas, i.e., the stable temporal component. Such areas can be used to define an informative and reliable multi-year training set, by selecting samples belonging to the different years for all the classes. The multi-year training set is finally employed to train a unique multi-year Long Short Term Mem-ory (LSTM) model, which enhances the consistency of the annual LC maps. The preliminary results carried out on three TSs of Sentinel 2 images acquired in Italy in 2018,2019 and 2020 demonstrates the capability of the method to improve the consistency of the annual LC maps. The agreement of the obtained maps is approx 78{%}, compared to the approx 74{%} achieved by the LSTM models trained separately.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	203-206
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883655
Publication status	Published - 17 Jul 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Bibliographical note

Funding Information:
Part of this work was performed in the CoE RAISE project which has received funding from the European Union’s Horizon 2020 Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. The authors gratefully acknowledge the computing resources from the DEEP-EST project, which received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 754304.

Publisher Copyright:
© 2022 IEEE.

Other keywords

Deep Learning (DL) Models
Long Short Term Memory (LSTM)
Multi-year training set
Time-Series (TS) of Consistent Land-Cover (LC) Maps

Access to Document

10.1109/IGARSS46834.2022.9883655

Cite this

Sedona, R., Paris, C., Tian, L., Riedel, M., & Cavallaro, G. (2022). An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 203-206). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883655

Sedona, Rocco ; Paris, Claudia ; Tian, Liang et al. / An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 203-206 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory",

abstract = "This paper presents an approach that aims to produce a Time-Series (TS) of consistent Land-Cover (LC) maps, typically needed to perform environmental monitoring. First, it creates an annual training set for each TS to be classified, leveraging on publicly available thematic products. These annual training sets are then used to generate a set of preliminary LC maps that allow for the identification of the unchanged areas, i.e., the stable temporal component. Such areas can be used to define an informative and reliable multi-year training set, by selecting samples belonging to the different years for all the classes. The multi-year training set is finally employed to train a unique multi-year Long Short Term Mem-ory (LSTM) model, which enhances the consistency of the annual LC maps. The preliminary results carried out on three TSs of Sentinel 2 images acquired in Italy in 2018,2019 and 2020 demonstrates the capability of the method to improve the consistency of the annual LC maps. The agreement of the obtained maps is approx 78{%}, compared to the approx 74{%} achieved by the LSTM models trained separately.",

keywords = "Deep Learning (DL) Models, Long Short Term Memory (LSTM), Multi-year training set, Time-Series (TS) of Consistent Land-Cover (LC) Maps",

author = "Rocco Sedona and Claudia Paris and Liang Tian and Morris Riedel and Gabriele Cavallaro",

note = "Funding Information: Part of this work was performed in the CoE RAISE project which has received funding from the European Union{\textquoteright}s Horizon 2020 Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. The authors gratefully acknowledge the computing resources from the DEEP-EST project, which received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the grant agreement no. 754304. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",

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Sedona, R, Paris, C, Tian, L, Riedel, M & Cavallaro, G 2022, An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 203-206, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883655

An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. / Sedona, Rocco; Paris, Claudia; Tian, Liang et al.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 203-206 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Funding Information: Part of this work was performed in the CoE RAISE project which has received funding from the European Union’s Horizon 2020 Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. The authors gratefully acknowledge the computing resources from the DEEP-EST project, which received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 754304. Publisher Copyright: © 2022 IEEE.

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N2 - This paper presents an approach that aims to produce a Time-Series (TS) of consistent Land-Cover (LC) maps, typically needed to perform environmental monitoring. First, it creates an annual training set for each TS to be classified, leveraging on publicly available thematic products. These annual training sets are then used to generate a set of preliminary LC maps that allow for the identification of the unchanged areas, i.e., the stable temporal component. Such areas can be used to define an informative and reliable multi-year training set, by selecting samples belonging to the different years for all the classes. The multi-year training set is finally employed to train a unique multi-year Long Short Term Mem-ory (LSTM) model, which enhances the consistency of the annual LC maps. The preliminary results carried out on three TSs of Sentinel 2 images acquired in Italy in 2018,2019 and 2020 demonstrates the capability of the method to improve the consistency of the annual LC maps. The agreement of the obtained maps is approx 78{%}, compared to the approx 74{%} achieved by the LSTM models trained separately.

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Sedona R, Paris C, Tian L, Riedel M, Cavallaro G. An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 203-206. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883655

An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory

Abstract

Publication series

Conference

Bibliographical note

Other keywords

Access to Document

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Cite this