Journal ArticleDOI
Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model
TLDR
An innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model is established and can be used to help urban planners to monitor dynamic urban landUse and evaluate the impact of urban planning schemes.Abstract:
Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing RS, geographic information systems GIS and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest POIs have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones TAZs by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta PRD are transformed into a TAZ-POI corpus using a greedy algorithm by considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm RFA model. Compared with some state-of-the-art probabilistic topic models PTMs, the proposed method can efficiently obtain the highest accuracy OA = 0.8728, kappa = 0.8399. Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes.read more
Citations
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Journal ArticleDOI
Deep learning in remote sensing applications: A meta-analysis and review
TL;DR: This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping, and a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.
Journal ArticleDOI
A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects
Xiaoping Liu,Xun Liang,Xia Li,Xia Li,Xiaocong Xu,Jinpei Ou,Yimin Chen,Yimin Chen,Shaoying Li,Shaojian Wang,Fengsong Pei +10 more
TL;DR: A future land use simulation (FLUS) model that explicitly simulates the long-term spatial trajectories of multiple LUCCs, and the simulation accuracy is higher than other well-accepted models, such as CLUE-S and CA models.
Journal ArticleDOI
Extracting urban functional regions from points of interest and human activities on location-based social networks
TL;DR: This research develops a statistical framework to help discover semantically meaningful topics and functional regions based on the co‐occurrence patterns of POI types and demonstrates the effectiveness of the proposed methodology by identifying distinctive types of latent topics and by extracting urban functional regions.
Journal ArticleDOI
Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method.
TL;DR: This paper argues that the delineation needs to integrate the top-down approach with CA for projecting complex land use changes under designed scenarios, and proposes a CA-based method called the future land use simulation (FLUS) that can support urban planning by generating feasible patterns for UGBs under different planning scenarios.
Journal ArticleDOI
Classifying urban land use by integrating remote sensing and social media data
TL;DR: This study proposes a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine and demonstrates the effectiveness of the strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model.
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Efficient Estimation of Word Representations in Vector Space
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