A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning

Remote Sensing of Environment, Год журнала: 2023, Номер 291, С. 113578 - 113578

Опубликована: Апрель 10, 2023

Язык: Английский

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Mapping local climate zones for cities: A large review

Remote Sensing of Environment, Год журнала: 2023, Номер 292, С. 113573 - 113573

Опубликована: Апрель 14, 2023

Язык: Английский

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Global maps of 3D built-up patterns for urban morphological analysis

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2022, Номер 114, С. 103048 - 103048

Опубликована: Окт. 7, 2022

Horizontal and vertical patterns of built-up land are essential to analyse a range environmental change impacts, such as exposure natural hazards, urban heat islands, trapping air pollution, well for decision making in this context. However, while data on horizontal abundant, they relatively rare patterns. Here, we present global maps 3D at 1-km2 resolution the nominal year 2015. These estimated using random forest models, fed with wide spatial trained reference from all continents except Antarctica. Independent testing indicates that R2 values models footprint, height, volume equal 0.89, 0.73, 0.84, respectively. Our results show buildings worldwide 6.16-m high average, total building is 1645 km3, which equivalent solid cube 12 km each side. Yet, find large variations patterns, both within across world regions. In particular, floor space per person exceeds 200 m2 Oceania North America, it only 29 South Asia 38 Sub-Saharan Africa. provide novel insights into distribution offer new opportunities assessments impacts. The height can be downloaded https://doi.org/10.34894/4QAGYL.

Язык: Английский

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Mapping fine-scale building heights in urban agglomeration with spaceborne lidar

Remote Sensing of Environment, Год журнала: 2022, Номер 285, С. 113392 - 113392

Опубликована: Дек. 6, 2022

Язык: Английский

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Estimation of urban-scale photovoltaic potential: A deep learning-based approach for constructing three-dimensional building models from optical remote sensing imagery

Sustainable Cities and Society, Год журнала: 2023, Номер 93, С. 104515 - 104515

Опубликована: Март 9, 2023

Язык: Английский

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Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010

Scientific Data, Год журнала: 2023, Номер 10(1)

Опубликована: Май 26, 2023

Abstract Understanding the spatiotemporal dynamics of global 3D urban expansion over time is becoming increasingly crucial for achieving long-term development goals. In this study, we generated a dataset annual (1990–2010) using World Settlement Footprint 2015 data, GAIA and ALOS AW3D30 data with three-step technical framework: (1) extracting constructed land to generate research area, (2) neighborhood analysis calculate original normalized DSM slope height each pixel in study (3) correction areas greater than 10° improve accuracy estimated building heights. The cross-validation results indicate that our reliable United States(R 2 = 0.821), Europe(R 0.863), China(R 0.796), across world(R 0.811). As know, first 30-meter globe, which can give unique information understand address implications urbanization on food security, biodiversity, climate change, public well-being health.

Язык: Английский

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Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities

Remote Sensing of Environment, Год журнала: 2023, Номер 298, С. 113802 - 113802

Опубликована: Сен. 12, 2023

Язык: Английский

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Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 6514 - 6528

Опубликована: Янв. 1, 2024

Extracting building heights from single-view remote sensing images greatly enhances the application of data. While methods for extracting height shadow have been widely studied, it remains a challenging task. The main reasons are as follows: (1) traditional method information exhibits low accuracy. (2) use only to extract results in limited scenarios. To solve above problems, this paper introduces side and complement each other, proposes extraction high-resolution using information. Firstly, we propose RMU-Net method, which utilizes multi-scale features This aims address issues related pixel detail loss imprecise edge segmentation, result significant scale differences within segmentation targets. Additionally, employ area threshold optimize results, specifically tackle small stray patches holes, enhancing overall integrity accuracy extraction. Secondly, that integrates based on an enhanced proportional coefficient model. measuring lengths is improved by incorporating fishing net informed our analysis geometric relationships among buildings. Finally, establish dataset containing images, select multiple areas experimental analysis. demonstrate 91.03% 90.29%. average absolute error (MAE) 1.22, while root mean square (RMSE) 1.21. Furthermore, proposed method's validity scalability affirmed through analyses applicability anti-interference performance extensive areas.

Язык: Английский

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City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China

Applied Energy, Год журнала: 2024, Номер 359, С. 122720 - 122720

Опубликована: Фев. 1, 2024

Язык: Английский

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A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Remote Sensing of Environment, Год журнала: 2024, Номер 310, С. 114241 - 114241

Опубликована: Июнь 4, 2024

Язык: Английский

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