Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 200 - 200

Published: Jan. 8, 2025

Anthropogenic heat is the generated by human activities such as industry, construction, transport, and metabolism. Accurate estimates of anthropogenic are essential for studying impacts on climate atmospheric environment. Commonly applied methods estimating include inventory method, energy balance equation building model simulation method. In recent years, rapid development computer technology availability massive data have made machine learning a powerful tool fluxes assessing its effects. Multi-source remote sensing also been widely used to obtain more details spatial temporal distribution characteristics heat. This paper reviews main approaches emissions. The typical algorithms abovementioned three introduced, their advantages limitations evaluated. Moreover, progress in application discussed well. Based big techniques, research feature engineering fusion will bring about major changes analysis modeling More in-depth this issue recommended provide important support curbing global warming, mitigating air pollution, achieving national goals carbon peak neutrality strategy.

Language: Английский

---

High-resolution mapping of GDP using multi-scale feature fusion by integrating remote sensing and POI data

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 129, P. 103812 - 103812

Published: April 9, 2024

High-resolution spatial distribution maps of GDP are essential for accurately analyzing economic development, industrial layout, and urbanization processes. However, the currently accessible gridded datasets limited in number resolution. Furthermore, high-resolution mapping remains a challenge due to complex sectoral structure GDP, which encompasses agriculture, industry, services. Meanwhile, multi-source data with high resolution can effectively reflect level regional development. Therefore, we propose multi-scale fusion residual network (Res-FuseNet) designed estimate grid density by integrating remote sensing POI data. Specifically, Res-FuseNet extracts features relevant different sectors. It constructs joint representation through mechanism estimates three sectors using connections. Subsequently, obtained correcting overlaying each sector county-level statistical The 100-meter map urban agglomeration middle reaches Yangtze River 2020 was successfully generated this method. experimental results confirm that outperforms machine learning models baseline model significantly training across at town-level. R2 values 0.69, 0.91, 0.99, respectively, while town-level evaluation also exhibit accuracy (R2=0.75). provides an innovative method, reveal characteristics structures fine-scale disparities within cities, offering robust support sustainable

Language: Английский

---

A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 4, 2025

Language: Английский

---

STARS: A novel gap-filling method for SDGSAT-1 nighttime light imagery using spatiotemporal and spectral synergy

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 322, P. 114720 - 114720

Published: March 27, 2025

Language: Английский

---

Towards building floor-level nighttime light exposure assessment using SDGSAT-1 GLI data

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 223, P. 375 - 397

Published: March 27, 2025

Language: Английский

---

Enhancing nighttime light remote Sensing: Introducing the nighttime light background value (NLBV) for urban applications

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 126, P. 103626 - 103626

Published: Dec. 22, 2023

Artificial light at night, as captured by nighttime (NTL) remote sensing, typically consists of two components: static urban lighting facilities and dynamic outdoor human activities. Separating these components can improve our understanding the mechanism underlying NTL sensing broaden its applications. In this paper, we introduce concept Nighttime Light Background Value (NLBV) to represent emitted solely facilities, excluding influence By utilizing a random forest method, derived pixel-level NLBV for Shanghai from data. Comparative analysis demonstrates that exhibits stronger correlation with building density road compared original Our empirical findings demonstrate definition application significantly enhance NTL-based applications extracting physical attributes estimating socioeconomic variables. Firstly, built-up area extracted based on outperforms data, especially in highly urbanized. Secondly, separating activity enables more accurate estimation variables different contributions. Moreover, results highlight significant potential incorporating across various disciplines. Overall, study significance improving accuracy applicability opening up new opportunities research practical domains.

Language: Английский

---

Evaluating the Potential of SDGSAT-1 Glimmer Imagery for Urban Road Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 18, P. 785 - 794

Published: Nov. 19, 2024

Language: Английский

---