Journal of Cleaner Production, Год журнала: 2024, Номер unknown, С. 144301 - 144301
Опубликована: Ноя. 1, 2024
Язык: Английский
Earth system science data, Год журнала: 2024, Номер 16(11), С. 5357 - 5374
Опубликована: Ноя. 25, 2024
Abstract. Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable a variety of applications, including climate modeling, energy consumption analysis, socioeconomic activities. Despite importance this information, previous studies have primarily focused on estimating heights regionally at grid scale, often resulting in with limited coverage or spatial resolution. This limitation hampers comprehensive global analysis ability to generate actionable insights finer scales. In study, we developed height map footprint scale by leveraging Earth Observation (EO) advanced machine learning techniques. Our approach integrated multisource remote-sensing features morphology develop estimation models using extreme gradient boosting (XGBoost) regression method across diverse regions. methodology allowed us estimate individual buildings worldwide, culminating creation three-dimensional (3D) Global Building Footprints (3D-GloBFP) dataset year 2020. evaluation results show that perform exceptionally well R2 values ranging from 0.66 0.96 root-mean-square errors (RMSEs) 1.9 14.6 m 33 subregions. Comparisons other demonstrate 3D-GloBFP closely matches distribution pattern reference heights. derived 3D shows distinct regions, countries, cities, gradually decreasing city center surrounding rural areas. Furthermore, our findings indicate disparities built-up infrastructure (i.e., volume) different countries cities. China country most intensive total (5.28×1011 m3, accounting 23.9 % total), followed USA (3.90×1011 17.6 total). Shanghai has largest volume (2.1×1010 m3) all representative The building-footprint-scale reveals significant heterogeneity environments, providing valuable dynamics climatology. available https://doi.org/10.5281/zenodo.11319912 (Building Americas, Africa, Oceania 3D-GloBFP; Che et al., 2024c), https://doi.org/10.5281/zenodo.11397014 Asia 2024a), https://doi.org/10.5281/zenodo.11391076 Europe 2024b).
Язык: Английский
Процитировано
11
Опубликована: Июнь 24, 2024
Abstract. Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable a variety of applications, including climate modeling, energy consumption analysis, socioeconomic activities. Despite importance this information, previous studies have primarily focused on estimating heights regionally grid scale, often resulting in with limited coverage or spatial resolution. This limitation hampers comprehensive global analyses ability to generate actionable insights finer scales. In study, we developed height map (3D-GloBFP) at footprint scale by leveraging Earth Observation (EO) advanced machine learning techniques. Our approach integrated multisource remote sensing features morphology develop estimation models using eXtreme Gradient Boosting (XGBoost) regression method across diverse regions. methodology allowed us estimate individual buildings worldwide, culminating creation first three-dimensional (3-D) footprints (3D-GloBFP). evaluation results show that perform exceptionally well worldwide R2 ranging from 0.66 0.96 root mean square errors (RMSEs) 1.9 m 14.6 33 subregions. Comparisons other demonstrate our 3D-GloBFP closely matches distribution pattern reference heights. derived 3-D shows distinct regions, countries, cities, gradually decreasing city center surrounding rural areas. Furthermore, findings indicate disparities built-up infrastructure (i.e., volume) different countries cities. China country most intensive total (5.28×1011 m3, accounting 23.9 % total), followed United States (3.90×1011 17.6 total). Shanghai has largest volume (2.1×1010 m3) all representative The building-footprint reveals significant heterogeneity environments, providing valuable dynamics climatology. dataset available https://doi.org/10.5281/zenodo.11319913 (Building Americas, Africa, Oceania 3D-GloBFP) (Che et al., 2024a), https://doi.org/10.5281/zenodo.11397015 Asia 2024b), https://doi.org/10.5281/zenodo.11391077 Europe 2024c).
Язык: Английский
Процитировано
9
Geographical Journal, Год журнала: 2025, Номер unknown
Опубликована: Фев. 1, 2025
Abstract Urbanisation is transitioning from disorderly sprawl to compact intensification, accompanied by functional differentiation and morphological changes spatially. This study addresses the relationship between urban functions morphologies at block scale in Hangzhou. Leveraging geo‐big data, we adopt a points of interest (POI) weighting method map four essential functions—residential, commercial, public service, industrial—at traffic analysis zones (TAZ) scale. Additionally, estimate indices using building footprint data volume data. Our investigation reveals intriguing patterns: residential, service exhibit central concentration trend diminishing towards periphery, whereas industrial demonstrate multi‐hotspot distribution. Morphological like patch density mean while size shape index, presenting pronounced peripheral distribution trend. Significantly, nuanced associations were elucidated. Residential tend display dense small patches, commercial areas showcase larger volumes, complex shapes. Furthermore, construction intensity‐based heterogeneity unveils dynamics morphologies, particularly high‐density areas. These findings underscore importance integrating considerations into planning, offering fresh perspective for zoning planning.
Язык: Английский
Процитировано
1
Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105684 - 105684
Опубликована: Июль 18, 2024
Язык: Английский
Процитировано
7
Sustainable Cities and Society, Год журнала: 2024, Номер 106, С. 105382 - 105382
Опубликована: Март 26, 2024
Язык: Английский
Процитировано
4
Remote Sensing, Год журнала: 2025, Номер 17(1), С. 155 - 155
Опубликована: Янв. 5, 2025
The intertidal ecosystem serves as a critical transitional zone between terrestrial and marine environments, supporting diverse biodiversity essential ecological functions. However, these systems are increasingly threatened by climate change, rising sea levels, anthropogenic impacts. Accurately mapping ecosystems differentiating mangroves, salt marshes, tidal flats remains challenge due to inconsistencies in classification frameworks. Here, we present high-precision approach for using multi-source satellite data, including Sentinel-1, Sentinel-2, Landsat 8/9, integrated with the Google Earth Engine (GEE) platform, enable detailed of zones across China–ASEAN. Our findings indicate total area 73,461 km2 China–ASEAN, an average width 1.16 km. Analyses patch area, abundance, perimeter relationships reveal power-law distribution scaling exponent 1.52, suggesting self-organizing characteristics shaped both natural human pressures. offer foundational data guide conservation management strategies region’s novel perspective propel research on global coastal ecosystems.
Язык: Английский
Процитировано
0
Scientific Data, Год журнала: 2025, Номер 12(1)
Опубликована: Янв. 30, 2025
Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex morphologies. In 2012, Local Climate Zone (LCZ) classification was proposed address this issue has since widely adopted in climate studies globally. Despite its prevalence, literature on dynamic mapping morphology remains sparse, making it difficult delve into study renewal year by year. study, we compared different training scales, producing mappings with a spatial resolution 100 meters spanning from 2000 2022 major Chinese cities, based LCZ scheme. The results demonstrate strong inter-year consistency, accuracy change is overall higher than 70%. Additionally, our exhibit good alignment other datasets, more suitable for current development situation China, effectively discriminate between building heights densities across types. This dataset holds significant potential enhancing monitoring advancing research.
Язык: Английский
Процитировано
0
International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104404 - 104404
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0
Scientific Data, Год журнала: 2025, Номер 12(1)
Опубликована: Март 12, 2025
Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well indicative function, quality, age, is essential for accurate urban analysis, simulations, policy updates. Current datasets suffer from incomplete coverage of multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, 23.6 billion m² rooftops an F1-Score 89.93% in OCRNet-based extraction, totaling 363 m³ stock. We trained bootstrap aggregated XGBoost models city administrative classifications, incorporating morphology, location, function features. Using multi-source billions remote sensing images 60 street view (SVIs), we generated height, structure, style, quality each machine learning large multimodal models. Accuracy was validated through model benchmarks, existing similar products, manual SVI validation, mostly above 80%. Our results are crucial global SDGs planning.
Язык: Английский
Процитировано
0
Land Use Policy, Год журнала: 2025, Номер 153, С. 107542 - 107542
Опубликована: Март 23, 2025
Язык: Английский
Процитировано
0