A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources
Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data...
Main Authors: | Lang Sun, Lina Tang, Guofan Shao, Quanyi Qiu, Ting Lan, Jinyuan Shao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-12-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/1/91 |
Similar Items
-
Boundary Extraction of Urban Built-Up Area Based on Luminance Value Correction of NTL Image
by: Mingchang Wang, et al.
Published: (2021-01-01) -
Research on the Extraction Method Comparison and Spatial-Temporal Pattern Evolution for the Built-Up Area of Hefei Based on Multi-Source Data Fusion
by: Jianwei Huang, et al.
Published: (2023-12-01) -
Extraction of Urban Built-Up Areas Using Nighttime Light (NTL) and Multi-Source Data: A Case Study in Dalian City, China
by: Xueming Li, et al.
Published: (2023-02-01) -
A POI and LST Adjusted NTL Urban Index for Urban Built-Up Area Extraction
by: Fei Li, et al.
Published: (2020-05-01) -
A New Urban Built-Up Index and Its Application in National Central Cities of China
by: Linfeng Wang, et al.
Published: (2024-01-01)