Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method
Authors
Keywords
-
Journal
Remote Sensing
Volume 13, Issue 3, Pages 477
Publisher
MDPI AG
Online
2021-01-29
DOI
10.3390/rs13030477
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach
- (2020) Ying Tu et al. Remote Sensing
- A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification
- (2020) Mercedes E. Paoletti et al. Remote Sensing
- Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen
- (2020) Mo Su et al. Remote Sensing
- Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data
- (2020) Amal Chakhar et al. Remote Sensing
- Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines
- (2020) Sotiria Fragou et al. Forests
- Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada
- (2020) Armand LaRocque et al. Remote Sensing
- Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data
- (2020) Jing Sun et al. Remote Sensing
- Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas
- (2020) Sergio Morell-Monzó et al. Remote Sensing
- Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou
- (2020) Leli Zong et al. Remote Sensing
- Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn
- (2020) Hwang Lee et al. Remote Sensing
- Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China
- (2020) Wanliu Mao et al. Remote Sensing
- 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
- (2019) Peng Gong et al. Science Bulletin
- Annual maps of global artificial impervious area (GAIA) between 1985 and 2018
- (2019) Peng Gong et al. REMOTE SENSING OF ENVIRONMENT
- Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018
- (2019) Peng Gong et al. Science Bulletin
- Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data
- (2018) Bin Chen et al. International Journal of Environmental Research and Public Health
- Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics
- (2018) Taïs Grippa et al. ISPRS International Journal of Geo-Information
- Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap
- (2018) Yann Forget et al. Remote Sensing
- Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery
- (2018) Xi Li et al. SENSORS
- Classifying urban land use by integrating remote sensing and social media data
- (2017) Xiaoping Liu et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Spatiotemporal Patterns of the Use of Urban Green Spaces and External Factors Contributing to Their Use in Central Beijing
- (2017) Fangzheng Li et al. International Journal of Environmental Research and Public Health
- Mapping Urban Land Use by Using Landsat Images and Open Social Data
- (2016) Tengyun Hu et al. Remote Sensing
- Development and Applications of a Comprehensive Land Use Classification and Map for the US
- (2014) David M. Theobald PLoS One
- Toward mapping land-use patterns from volunteered geographic information
- (2013) Jamal Jokar Arsanjani et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
- (2012) Peng Gong et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Urbanisation and health in China
- (2012) Peng Gong et al. LANCET
- Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks
- (2011) Julian Hagenauer et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Global Change and the Ecology of Cities
- (2008) N. B. Grimm et al. SCIENCE
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now