Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View
| Autor: | Dean M. Hanink, Weixing Zhang, Xiaojiang Li, Weidong Li, Chuanrong Zhang, Wenjie Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Light detection Land use Ecological Modeling Geography Planning and Development 0211 other engineering and technologies Orthophoto High resolution 02 engineering and technology 15. Life on land Urban land 01 natural sciences Random forest Urban Studies Geography Lidar Megacity 11. Sustainability Cartography 021101 geological & geomatics engineering 0105 earth and related environmental sciences General Environmental Science Remote sensing |
| Zdroj: | Computers, Environment and Urban Systems. 64:215-228 |
| ISSN: | 0198-9715 |
| DOI: | 10.1016/j.compenvurbsys.2017.03.001 |
| Popis: | Urban land use information is increasingly important for a variety of purposes. With their increasing coverage and availability, airborne light detection and ranging (LiDAR) data, high resolution orthoimagery (HRO), and Google Street View (GSV) images are showing great potential for accurate land use classification. However, no study mapped land use in megacity using GSV-derived features or the three kinds of data together for land use classification. The main objectives of this study are (1) to test the performance of a parcel-based land use classification method using a Random Forest classifier with LiDAR data, HRO, and GSV images in a megacity, and (2) to explore the use of GSV in separating parcels of mixed residential & commercial buildings from other land use parcels. Two neighboring community districts in Brooklyn, New York, were selected as the study area. Thirteen automatically-derived parcel features, including nine common parcel features and four GSV-derived parcel features, were used in land use classification. The average overall classification accuracy was 77.5%, with producer's accuracies exceeding 92% for single-family housing. Comparing the results of classifications with and without GSV-derived parcel features shows that GSV-derived parcel features on average contribute to the classification accuracy of mixed residential & commercial buildings by 10 percentage points, improving it from 41.3% to 51.4%. In general, the results show that even in a complex megacity, the parcel-based land use classification technique, with parcel features extracted from airborne LiDAR, HRO, and GSV, is able to discriminate among different land use classes, such as single-family house, commercial & industrial building, and open space & park, with acceptable accuracies, and that integrating GSV into classification improves the classification accuracy of some urban land use classes, especially mixed residential & commercial building. |
| Databáze: | OpenAIRE |
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