Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model
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Summary
This paper investigates a faster and more automated method that does not require remotely sensed images for land cover classification and achieves an overall accuracy of approximately 80%, providing evidence that CGI with textual information has a great potential forLand cover classification.
- Type
- article
- Published
- 2017-06-13
- Cited by
- 23
- References
- 35
- Access
- Open access
- OpenAlex
- https://openalex.org/W2625043886
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11570228
Keywords
Land cover, Crowdsourcing, Computer science, Cover (algebra), Cluster analysis
References
- Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts
- Concave hull: A k-nearest neighbours approach for the computation of the region occupied by a set of points
- Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice
- Extracting and understanding urban areas of interest using geotagged photos
- Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter)
- Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data
- Spectral clustering for sensing urban land use using Twitter activity
- Global land cover mapping at 30 m resolution: A POK-based operational approach
- Using Volunteered Data in Land Cover Map Validation: Mapping West African Forests
- Usability of VGI for validation of land cover maps
- Warming goal: clear link to emissions
- Land cover classification using geo-referenced photos
- A web-based system for supporting global land cover data production
- Exploratory analysis of OpenStreetMap for land use classification
- Building a hybrid land cover map with crowdsourcing and geographically weighted regression
- Exploring Geotagged images for land-use classification
- Social Sensing: A New Approach to Understanding Our Socioeconomic Environments
- Status of land cover classification accuracy assessment
- Some methods for classification and analysis of multivariate observations
- Normalized cuts and image segmentation
Cited by
- Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China
- Completing yearly land cover maps for accurately describing annual changes of tropical landscapes
- Exploring geo-tagged photos for land cover validation with deep learning
- A dynamic human activity‐driven model for mixed land use evaluation using social media data
- Social functional mapping of urban green space using remote sensing and social sensing data
- An Incentive Mechanism Based on a Bayesian Game for Spatial Crowdsourcing
- Inferring Spatial Distribution Patterns in Web Maps for Land Cover Mapping
- Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest
- The Identification and Use Efficiency Evaluation of Urban Industrial Land Based on Multi-Source Data
- Urban Functional Area Division Based on Cell Tower Classification
- Urban Function as a New Perspective for Adaptive Street Quality Assessment
- A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification
- MVDF-RSC: Multi-view data fusion via robust spectral clustering for geo-tagged image tagging
- CHANGES IN LAND COVER OF THE MOUNT SIRIMAU PROTECTED GROUP, AMBON CITY MALUKU PROVINCE
- Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research
- Automatic impervious surface mapping in subtropical China via a terrain-guided gated fusion network
- A review of crowdsourced geographic information for land-use and land-cover mapping: current progress and challenges
- Identifying urban villages: an attention-based deep learning approach that integrates remote sensing and street-level images
- Three-dimensional identification and attribution of flash and slow agricultural droughts in the North China Plain
- Predicting Urban Functional Zones with Twitter Data Using the Space-Time Scan Statistics Method and the Random Forest Classifier
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