Detection of heavy-polluting enterprises from optical satellite remote sensing images

Zhibao Wang; Xi Zhao; Lu Bai; Mei Wang; Man Zhao; Meng Fan; Jinhua Tao; Liangfu Chen

doi:10.1109/igarss52108.2023.10281808

Detection of heavy-polluting enterprises from optical satellite remote sensing images

Zhibao Wang, Xi Zhao, Lu Bai, Mei Wang, Man Zhao, Meng Fan, Jinhua Tao, Liangfu Chen

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

Abstract

Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry’s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.

Original language	English
Title of host publication	Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Electronic)	9798350320107
ISBN (Print)	9798350331745
DOIs	https://doi.org/10.1109/igarss52108.2023.10281808
Publication status	Published - 20 Oct 2023
Externally published	Yes
Event	IEEE International Geoscience and Remote Sensing Symposium 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023 https://doi.org/10.1109/IGARSS52108.2023

Publication series

Name	IEEE International Geoscience and Remote Sensing Symposium: Proceedings
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium 2023
Abbreviated title	IGARSS 2023
Country/Territory	United States
City	Pasadena
Period	16/07/2023 → 21/07/2023
Internet address	https://doi.org/10.1109/IGARSS52108.2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/igarss52108.2023.10281808Licence: Unspecified

Cite this

Wang, Z., Zhao, X., Bai, L., Wang, M., Zhao, M., Fan, M., Tao, J., & Chen, L. (2023). Detection of heavy-polluting enterprises from optical satellite remote sensing images. In Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023 (IEEE International Geoscience and Remote Sensing Symposium: Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/igarss52108.2023.10281808

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title = "Detection of heavy-polluting enterprises from optical satellite remote sensing images",

abstract = "Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry{\textquoteright}s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.",

author = "Zhibao Wang and Xi Zhao and Lu Bai and Mei Wang and Man Zhao and Meng Fan and Jinhua Tao and Liangfu Chen",

year = "2023",

month = oct,

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doi = "10.1109/igarss52108.2023.10281808",

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series = "IEEE International Geoscience and Remote Sensing Symposium: Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023",

address = "United States",

note = "IEEE International Geoscience and Remote Sensing Symposium 2023, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",

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Wang, Z, Zhao, X, Bai, L, Wang, M, Zhao, M, Fan, M, Tao, J & Chen, L 2023, Detection of heavy-polluting enterprises from optical satellite remote sensing images. in Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023. IEEE International Geoscience and Remote Sensing Symposium: Proceedings, Institute of Electrical and Electronics Engineers Inc., IEEE International Geoscience and Remote Sensing Symposium 2023, Pasadena, California, United States, 16/07/2023. https://doi.org/10.1109/igarss52108.2023.10281808

Detection of heavy-polluting enterprises from optical satellite remote sensing images. / Wang, Zhibao; Zhao, Xi; Bai, Lu et al.
Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE International Geoscience and Remote Sensing Symposium: Proceedings).

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

TY - GEN

T1 - Detection of heavy-polluting enterprises from optical satellite remote sensing images

AU - Wang, Zhibao

AU - Zhao, Xi

AU - Bai, Lu

AU - Wang, Mei

AU - Zhao, Man

AU - Fan, Meng

AU - Tao, Jinhua

AU - Chen, Liangfu

PY - 2023/10/20

Y1 - 2023/10/20

N2 - Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry’s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.

AB - Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry’s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.

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DO - 10.1109/igarss52108.2023.10281808

M3 - Conference contribution

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T3 - IEEE International Geoscience and Remote Sensing Symposium: Proceedings

BT - Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023

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Wang Z, Zhao X, Bai L, Wang M, Zhao M, Fan M et al. Detection of heavy-polluting enterprises from optical satellite remote sensing images. In Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, IGARSS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (IEEE International Geoscience and Remote Sensing Symposium: Proceedings). doi: 10.1109/igarss52108.2023.10281808

Detection of heavy-polluting enterprises from optical satellite remote sensing images

Abstract

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UN SDGs

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