Landuse information extraction in Qingdao based on decision tree classification

Mingqin, Han; Jiang, Tao; Weizheng, Zhang; Shouyin, Dong; Lu, Wenhu

doi:10.1109/cisp.2010.5647497

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The Decision Tree algorithm relies on decision rules to classify new data based on the learning from training samples [43]. A couple of algorithms have been used to build a decision tree including CART (Classification and Regression Trees) and ID3 (Iterative Dichotomiser 3).…”

Section: Decision Treementioning

confidence: 99%

Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China

2019

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Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.In general, urban functional zones are categorized as commercial, recreational, industrial and residential zones. Numerous models have been developed to extract and analyze urban functional zones. Traditionally, urban functional areas are identified based on onsite survey and field observation [8]. With the improvement of high-resolution satellite images (Landsat, SPOT, QuickBird), many detailed urban land-use maps have also been produced with remote sensing technology, which mainly concentrates on feature representations, semantic cognition classification, and zonal segmentation [1,9,10]. The above-mentioned studies largely take advantage of the spectral features of a city, and satellite images can only describe the natural characteristics of ground elements, and largely ignore and cannot capture the real human activities.It is the activities of, and interactions between, urban inhabitants that give rise to the characteristic physical environment of the city, and which, in return, also conditions people's various behaviors in the urban setting. This also empowers social sensing studies in various disciplines. Liu et al. [11] proposed the concept of social sensing, an important complement to remote sensing that is able to capture social and economic activities in the city and explore the function of a city at a fine and temporal scale....

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Section: Decision Treementioning

confidence: 99%