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https://doi.org/10.2139/ssrn.6110888
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/2017jd027359openalex-extracted
Large Uncertainties in Urban‐Scale Carbon Emissions
inferredcites →https://doi.org/10.1002/joc.2390openalex-extracted
High‐resolution (space, time) anthropogenic heat emissions: London 1970–2025
inferredcites →https://doi.org/10.1007/s11227-024-06790-7openalex-extracted
Optimizing feature selection and remote sensing classification with an enhanced machine learning method
inferredcites →https://doi.org/10.1007/s11356-023-31817-6openalex-extracted
Spatiotemporal analysis of national carbon emission and regional carbon simulation in China
inferredcites →https://doi.org/10.1007/s12665-023-10770-0openalex-extracted
Exploring the eco-efficiency of cultivated land utilization and its influencing factors in black soil region of Northeast China under the goal of reducing non-point pollution and net carbon emission
inferredcites →https://doi.org/10.1007/s41748-022-00332-yopenalex-extracted
Modeling the Impacts of Residential, Commercial, and Industrial Land use on Carbon Footprints Using Kernel Density Function in an Urban Setting
inferredcites →https://doi.org/10.1016/0034-4257(79)90013-0openalex-extracted
Red and photographic infrared linear combinations for monitoring vegetation
inferredcites →https://doi.org/10.1016/0034-4257(91)90048-bopenalex-extracted
A review of assessing the accuracy of classifications of remotely sensed data
inferredcites →https://doi.org/10.1016/j.apenergy.2022.120410openalex-extracted
Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings
inferredcites →https://doi.org/10.1016/j.apenergy.2024.124258openalex-extracted
Carbon emission drivers of China's power sector and its transformation for global decarbonization contribution
inferredcites →https://doi.org/10.1016/j.apenergy.2025.125865openalex-extracted
Local energy zone for urban carbon emissions studies – Accounting, characterization and prediction
inferredcites →https://doi.org/10.1016/j.buildenv.2023.111007openalex-extracted
Local carbon emission zone construction in the highly urbanized regions: Application of residential and transport CO2 emissions in Shanghai, China
inferredcites →https://doi.org/10.1016/j.eiar.2023.107320openalex-extracted
Integrated effects of land use and land cover change on carbon metabolism: Based on ecological network analysis
inferredcites →https://doi.org/10.1016/j.enbuild.2008.01.005openalex-extracted
Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles
inferredcites →https://doi.org/10.1016/j.enbuild.2022.111835openalex-extracted
Approach to local climate zone based energy consumption assessment in an Indian city
inferredcites →https://doi.org/10.1016/j.enbuild.2022.112335openalex-extracted
Relationships between building characteristics, urban form and building energy use in different local climate zone contexts: An empirical study in Seoul
inferredcites →https://doi.org/10.1016/j.enbuild.2024.115243openalex-extracted
Nonlinear and threshold effects of urban building form on carbon emissions
inferredcites →https://doi.org/10.1016/j.enbuild.2025.115832openalex-extracted
Carbon emission estimation at the urban functional zone scale: Integrating multi-source data and machine learning approach
inferredcites →https://doi.org/10.1016/j.isprsjprs.2014.06.011openalex-extracted
An effective thin cloud removal procedure for visible remote sensing images
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.01.011openalex-extracted
Random forest in remote sensing: A review of applications and future directions
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.11.004openalex-extracted
Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative
inferredcites →https://doi.org/10.1016/j.isprsjprs.2018.07.017openalex-extracted
A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.06.022openalex-extracted
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine
inferredcites →https://doi.org/10.1016/j.isprsjprs.2023.05.028openalex-extracted
Nighttime light remote sensing for urban applications: Progress, challenges, and prospects
inferredcites →https://doi.org/10.1016/j.jclepro.2017.06.075openalex-extracted
Methodology and applications of city level CO2 emission accounts in China
inferredcites →https://doi.org/10.1016/j.jclepro.2024.144318openalex-extracted
Estimating the marginal effects of plot-scale socio-economic and spatial morphological factors on carbon emissions from non-linear perspective
inferredcites →https://doi.org/10.1016/j.landusepol.2024.107209openalex-extracted
Spatial heterogeneity and driving mechanisms of carbon emissions in urban expansion areas: A research framework coupled with patterns and functions
inferredcites →https://doi.org/10.1016/j.rse.2009.08.016openalex-extracted
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
inferredcites →https://doi.org/10.1016/j.rse.2011.06.025openalex-extracted
Improving the estimation of urban surface emissivity based on sub-pixel classification of high resolution satellite imagery
inferredcites →https://doi.org/10.1016/j.rse.2011.07.020openalex-extracted
Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data
inferredcites →https://doi.org/10.1016/j.rse.2011.12.003openalex-extracted
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
inferredcites →https://doi.org/10.1016/j.rse.2013.08.014openalex-extracted
Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)
inferredcites →https://doi.org/10.1016/j.rse.2016.02.028openalex-extracted
A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
inferredcites →https://doi.org/10.1016/j.rse.2016.10.010openalex-extracted
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
inferredcites →https://doi.org/10.1016/j.rse.2017.06.031openalex-extracted
Google Earth Engine: Planetary-scale geospatial analysis for everyone
inferredcites →https://doi.org/10.1016/j.rse.2018.10.013openalex-extracted
Integrating cloud-based workflows in continental-scale cropland extent classification
inferredcites →https://doi.org/10.1016/j.rse.2025.114824openalex-extracted
Mapping the first dataset of global urban land uses with Sentinel-2 imagery and POI prompt
inferredcites →https://doi.org/10.1016/j.rser.2016.09.059openalex-extracted
Do greenhouse gas emission calculations from energy crop cultivation reflect actual agricultural management practices? – A review of carbon footprint calculators
inferredcites →https://doi.org/10.1016/j.scib.2019.03.002openalex-extracted
Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.161677openalex-extracted
LCZ method is more effective than traditional LUCC method in interpreting the relationship between urban landscape and atmospheric particles
inferredcites →https://doi.org/10.1038/s41558-019-0659-6openalex-extracted
Carbon dioxide emissions continue to grow amidst slowly emerging climate policies
inferredcites →https://doi.org/10.1038/s41586-020-2686-xopenalex-extracted
Mapping carbon accumulation potential from global natural forest regrowth
inferredcites →https://doi.org/10.1038/s41597-020-00605-zopenalex-extracted
Combining expert and crowd-sourced training data to map urban form and functions for the continental US
inferredcites →https://doi.org/10.1038/s41597-020-00736-3openalex-extracted
County-level CO2 emissions and sequestration in China during 1997–2017
inferredcites →https://doi.org/10.1038/s41597-024-04195-yopenalex-extracted
Global urban and rural settlement dataset from 2000 to 2020
inferredcites →https://doi.org/10.1038/s41597-025-04806-2openalex-extracted
Global up-to date emissions using the EDGAR Fast-Track methodology
inferredcites →https://doi.org/10.1038/s41597-025-05047-zopenalex-extracted
MSLU-100K: A Large Multi-Source Dataset for Land Use Analysis in Major Chinese Cities
inferredcites →https://doi.org/10.1038/s43017-021-00244-xopenalex-extracted
Challenges and opportunities for carbon neutrality in China
inferredcites →https://doi.org/10.1038/s43017-025-00658-xopenalex-extracted
Global carbon emissions and decarbonization in 2024
inferredcites →https://doi.org/10.1038/s43247-024-01951-1openalex-extracted
Carbon dioxide emissions from industrial processes and product use are a non-ignorable factor in China’ s mitigation
inferredcites →https://doi.org/10.1038/s44284-024-00069-xopenalex-extracted
Assessing global urban CO2 removal
inferredcites →https://doi.org/10.1038/s44284-025-00233-xopenalex-extracted
Declining short-term emission control opportunity for major events in Chinese cities
inferredcites →https://doi.org/10.1080/01431160304987openalex-extracted
Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
inferredcites →https://doi.org/10.1080/01431160600589179openalex-extracted
Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery
inferredcites →https://doi.org/10.1080/20964471.2019.1625528openalex-extracted
Automated global delineation of human settlements from 40 years of Landsat satellite data archives
inferredcites →https://doi.org/10.1088/1748-9326/ac7c2aopenalex-extracted
Urban and rural carbon footprints in developing countries
inferredcites →https://doi.org/10.1109/lgrs.2022.3161578openalex-extracted
Multisource Remote Sensing Classification for Coastal Wetland Using Feature Intersecting Learning
inferredcites →https://doi.org/10.1109/mgrs.2017.2762307openalex-extracted
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
inferredcites →https://doi.org/10.1109/tgrs.2008.916201openalex-extracted
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
inferredcites →https://doi.org/10.1109/tgrs.2015.2435801openalex-extracted
Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery
inferredcites →https://doi.org/10.1111/tgis.12182openalex-extracted
Towards an Automated Comparison of OpenStreetMap with Authoritative Road Datasets
inferredcites →https://doi.org/10.1111/tgis.13272openalex-extracted
Quantitative Identification of Mixed Urban Functions: A Probabilistic Approach Based on Physical and Social Sensing Data
inferredcites →https://doi.org/10.3389/fenvs.2021.637455openalex-extracted
LCZ Generator: A Web Application to Create Local Climate Zone Maps
inferredcites →https://doi.org/10.4236/jgis.2022.141001openalex-extracted
Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review
inferredcites →https://doi.org/10.5194/essd-10-87-2018openalex-extracted
The Open-source Data Inventory for Anthropogenic CO 2 , version 2016 (ODIAC2016): a global monthly fossil fuel CO 2 gridded emissions data product for tracer transport simulations and surface flux inversions
inferredcites →https://doi.org/10.5194/essd-13-2753-2021openalex-extracted
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
inferredcites →https://doi.org/10.5194/essd-14-1377-2022openalex-extracted
TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning
inferredcites →https://doi.org/10.5194/essd-14-3743-2022openalex-extracted
Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine
inferredcites →https://doi.org/10.5194/essd-16-2297-2024openalex-extracted
A 30 m annual cropland dataset of China from 1986 to 2021
inferredcites →https://doi.org/10.5194/isprs-annals-x-2-2024-137-2024openalex-extracted
Algorithm, Progresses, Datasets and Validation of GLC_FCS30D: the first global 30 m land-cover dynamic product with fine classification system from 1985 to 2022
inferredcites →https://doi.org/10.5721/eujrs20144723openalex-extracted
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.buildenv.2026.114653openalex-snapshot
Mapping local energy zones in China to support urban-scale energy-carbon research
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