Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery
The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators fro...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/11/2067 |
| _version_ | 1827691612579299328 |
|---|---|
| author | Haoyu Liu Xianwen He Yanbing Bai Xing Liu Yilin Wu Yanyun Zhao Hanfang Yang |
| author_facet | Haoyu Liu Xianwen He Yanbing Bai Xing Liu Yilin Wu Yanyun Zhao Hanfang Yang |
| author_sort | Haoyu Liu |
| collection | DOAJ |
| description | The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research. |
| first_indexed | 2024-03-10T11:05:07Z |
| format | Article |
| id | doaj.art-25d5497d5f834f1385c9930792c81316 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:05:07Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-25d5497d5f834f1385c9930792c813162023-11-21T21:12:03ZengMDPI AGRemote Sensing2072-42922021-05-011311206710.3390/rs13112067Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite ImageryHaoyu Liu0Xianwen He1Yanbing Bai2Xing Liu3Yilin Wu4Yanyun Zhao5Hanfang Yang6Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaGraduate School of Information Sciences, Tohoku University, Sendai 980-8579, JapanCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, ChinaThe official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.https://www.mdpi.com/2072-4292/13/11/2067attention-augmented CNNnightlightfine-grained GDP estimationdaytime satellite imageryarbitrary area representation |
| spellingShingle | Haoyu Liu Xianwen He Yanbing Bai Xing Liu Yilin Wu Yanyun Zhao Hanfang Yang Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery Remote Sensing attention-augmented CNN nightlight fine-grained GDP estimation daytime satellite imagery arbitrary area representation |
| title | Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery |
| title_full | Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery |
| title_fullStr | Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery |
| title_full_unstemmed | Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery |
| title_short | Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery |
| title_sort | nightlight as a proxy of economic indicators fine grained gdp inference around chinese mainland via attention augmented cnn from daytime satellite imagery |
| topic | attention-augmented CNN nightlight fine-grained GDP estimation daytime satellite imagery arbitrary area representation |
| url | https://www.mdpi.com/2072-4292/13/11/2067 |
| work_keys_str_mv | AT haoyuliu nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery AT xianwenhe nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery AT yanbingbai nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery AT xingliu nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery AT yilinwu nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery AT yanyunzhao nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery AT hanfangyang nightlightasaproxyofeconomicindicatorsfinegrainedgdpinferencearoundchinesemainlandviaattentionaugmentedcnnfromdaytimesatelliteimagery |