scholarly journals Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2386 ◽  
Author(s):  
Jing Sun ◽  
Hong Wang ◽  
Zhenglin Song ◽  
Jinbo Lu ◽  
Pengyu Meng ◽  
...  
Keyword(s):  
Land Use ◽  
Classification Accuracy ◽  
Urban Land ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Level Ii ◽  
Training Samples ◽  
Level I ◽  
Use Categories ◽  
Land Use Maps

High-spatial-resolution (HSR) urban land use maps are very important for urban planning, traffic management, and environmental monitoring. The rapid urbanization in China has led to dramatic urban land use changes, however, so far, there are no such HSR urban land use maps based on unified classification frameworks. To fill this gap, the mapping of 2018 essential urban land use categories in China (EULUC-China) was jointly accomplished by a group of universities and research institutes. However, the relatively lower classification accuracy may not sufficiently meet the application demands for specific cities. Addressing these challenges, this study took Nanjing city as the case study to further improve the mapping practice of essential urban land use categories, by refining the generation of urban parcels, resolving the problem of unbalanced distribution of point of interest (POI) data, integrating the spatial dependency of POI data, and evaluating the size of training samples on the classification accuracy. The results revealed that (1) the POI features played the most important roles in classification performance, especially in identifying administrative, medical, sport, and cultural land use categories, (2) compared with the EULUC-China, the overall accuracy for Level I and Level II in EULUC-Nanjing has increased by 11.1% and 5%, to 86.1% and 80% respectively, and (3) the classification accuracy of Level I and Level II would be stable when the number of training samples was up to 350. The methods and findings in this study are expected to better inform the regional to continental mappings of urban land uses.

scholarly journals Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

2020 ◽  
Vol 12 (7) ◽  
pp. 1058 ◽  
Author(s):  
Ying Tu ◽  
Bin Chen ◽  
Tao Zhang ◽  
Bing Xu
Keyword(s):  
Land Use ◽  
Learning Algorithm ◽  
Accuracy Assessment ◽  
Regional Analysis ◽  
Urban Land ◽  
Urban Land Use ◽  
Level Ii ◽  
Segmentation Approach ◽  
Level I ◽  
Use Categories

Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

scholarly journals Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

2020 ◽  
Vol 12 (17) ◽  
pp. 2817
Author(s):  
Wanliu Mao ◽  
Debin Lu ◽  
Li Hou ◽  
Xue Liu ◽  
Wenze Yue
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Ann Model ◽  
Level Ii ◽  
Svm Model ◽  
Machine Leaning ◽  
Ann Models ◽  
Level I

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.

scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

scholarly journals Scenario Analysis on Climate Change Impacts of Urban Land Expansion under Different Urbanization Patterns: A Case Study of Wuhan Metropolitan

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Xinli Ke ◽  
Feng Wu ◽  
Caixue Ma
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Urban Expansion ◽  
Regional Climate ◽  
Urban Land ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Urban Land Expansion ◽  
The Impact

Urban land expansion plays an important role in climate change. It is significant to select a reasonable urban expansion pattern to mitigate the impact of urban land expansion on the regional climate in the rapid urbanization process. In this paper, taking Wuhan metropolitan as the case study area, and three urbanization patterns scenarios are designed to simulate spatial patterns of urban land expansion in the future using the Partitioned and Asynchronous Cellular Automata Model. Then, simulation results of land use are adjusted and inputted into WRF (Weather Research and Forecast) model to simulate regional climate change. The results show that: (1) warming effect is strongest under centralized urbanization while it is on the opposite under decentralized scenario; (2) the warming effect is stronger and wider in centralized urbanization scenario than in decentralized urbanization scenario; (3) the impact trends of urban land use expansion on precipitation are basically the same under different scenarios; (4) and spatial distribution of rainfall was more concentrated under centralized urbanization scenario, and there is a rainfall center of wider scope, greater intensity. Accordingly, it can be concluded that decentralized urbanization is a reasonable urbanization pattern to mitigate climate change in rapid urbanization period.

scholarly journals Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

2019 ◽  
Vol 8 (1) ◽  
pp. 28 ◽  
Author(s):  
Quanlong Feng ◽  
Dehai Zhu ◽  
Jianyu Yang ◽  
Baoguo Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Urban Land ◽  
Urban Land Use ◽  
Lidar Data ◽  
Adaptive Fusion

Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on "Squeeze-and-Excitation Networks"). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

2020 ◽  
Vol 65 (3) ◽  
pp. 182-187 ◽  
Author(s):  
Peng Gong ◽  
Bin Chen ◽  
Xuecao Li ◽  
Han Liu ◽  
Jie Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Preliminary Results ◽  
Use Categories

scholarly journals Spatial Structure of Urban Landuse in Kathmandu Valley

2017 ◽  
Vol 14 ◽  
pp. 41-45
Author(s):  
Shobha Shrestha
Keyword(s):  
Land Use ◽  
Spatial Structure ◽  
Urban System ◽  
Urban Land ◽  
Urban Land Use ◽  
Kathmandu Valley ◽  
Remarkable Change ◽  
Rapid Urbanization ◽  
Spatial Metrics ◽  
Gis And Remote Sensing

Spatial structure of urban land use has been interest of study since early 20th century. The current study examines dynamics of spatial structure of urban agricultural landuse and how agricultural landuse is placed within the existing structure. The study explores the direction and dimension of landuse change and characteristics of spatial fragmentation in Kathmandu Valley. Technological tools like GIS and Remote Sensing, and Spatial metrics/indices has been used for spatial analysis. The study shows that within ten years time span of 2003 to 2012, urban land use has gone drastic change in Kathmandu valley. Remarkable change in terms of pace and direction is evident in agriculture and built-up classes which signifies the rapid urbanization trend in the valley. The finding shows that spatial structure of the urban landuse of the valley is impending towards more heterogeneous and diverse landscape. Similarly, spatial fragmentation analysis highlights characteristic development of new isolated urban patches inside relatively larger agriculture patches fragmenting them into number of smaller patches. The study concludes that the importance of GIS/RS tools and technology in identifying and analyzing structure and dynamics of land use within prevailing complex urban system of Kathmandu valley is reasonable. The composition and configuration of spatial structure computed through spatial metrics are thus helpful for understanding how landscape develops and changes over time.Nepalese Journal on Geoinformatics, Vol. 14, 2015, Page: 41-45

scholarly journals Spatio-Econometric Analysis of Urban Land Use Efficiency in China from the Perspective of Natural Resources Input and Undesirable Outputs: A Case Study of 287 Cities in China

2020 ◽  
Vol 17 (19) ◽  
pp. 7297
Author(s):  
Xiao Han ◽  
Anlu Zhang ◽  
Yinying Cai
Keyword(s):  
Economic Growth ◽  
Land Use ◽  
Natural Resources ◽  
Clean Energy ◽  
Urban Land ◽  
Urban Land Use ◽  
Sustainable Urban Development ◽  
Rapid Urbanization ◽  
Land Use Efficiency ◽  
Use Efficiency

The rapid urbanization in China has had a huge impact on land use and the scarcity of land resources has become a constraint for sustainable urban development. As urban land is an indispensable material basis in economic development, measuring its use efficiency and adopting effective policies to improve urban land use efficiency (ULUE) are important links in maintaining sustainable economic growth. By establishing a comprehensive ULUE evaluation index system that emphasizes on incorporating the natural resources input and the undesirable output, ULUE from 2010 to 2016 was calculated based on super efficiency SBM model, and its potential influencing factors were explored using a spatial econometric model. The results show that: (1) temporally, the overall ULUE in China is upward growing, and the gap among regions is becoming gradually convergent. (2) Spatially, the ULUE of Chinese cities are positively correlated. (3) Economic agglomeration and industrial structure significantly improve ULUE in China, but the intensity of energy consumption has a negative impact on ULUE. We suggest that: (1) differentiated industrial development strategies should be formulated; (2) the economic growth pattern should be changed from energy-consuming to energy-saving; (3) priority should be given to innovation on science and education, so as to increase in clean energy input and cleaner production.

scholarly journals Integrating Aerial and Street View Images for Urban Land Use Classification

2018 ◽  
Vol 10 (10) ◽  
pp. 1553 ◽  
Author(s):  
Rui Cao ◽  
Jiasong Zhu ◽  
Wei Tu ◽  
Qingquan Li ◽  
Jinzhou Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Ground Level ◽  
Urban Land ◽  
Urban Land Use ◽  
Aerial Images ◽  
Land Use Classification ◽  
Street View

Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.

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