Urban Land Use Classification Research Articles

Enhanced urban functional land use map with free and open-source data

ABSTRACT The study aims at developing an applicable methodology to produce the functional land-use map using only free and open-source data. Top-view Sentinel image and ground-view Open Street Map (OSM) data are chosen due to their extensive availability. The three-stage framework, including object-based image analysis, OSM data cleaning, and ontology-based decision fusion, is proposed and implemented with open-source tools. We applied the developed approach to districts 1, 4, and 7 of HoChiMinh city, representing the complexities of the dynamic change in big cities. The result showed a good functional land use map with 78.70% overall accuracy. The outcome presents the mismatch between the data-driven approach and human knowledge, which can be improved by ontology-based fusion with OSM data. The ontology-based framework comprises the common urban land-use classes and OSM attributes, which can be applied and extended in other urban areas. Additional text attributes may be applicable only locally and can be modified in our open-source framework. Object-based image analysis takes advantage of Google Earth Engine computing power, whereas ontology-based processing works well on a local computer. In future studies, adopted natural language processing to pre-process OSM data and ontology-based fusion will be implemented on the cloud-computing platform to enhance computational efficiency.

Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China

The significant economic development witnessed in China in recent decades has been accompanied by the increasing expansion of urban areas. Although a growing literature has analyzed the characteristics and driving forces of urban land expansion, less attention has been paid to examining the different expansion determinants driving fine-scale urban land use (residential land, administration and public services land, commercial land, and industrial land) change. This paper aims to identify the differences of multi-mechanisms driving fine-scale urban land use expansion based on big data and machine learning, in the Huizhou downtown area in 2000–2015. The Random Forest (RF) algorithm is used to identify the natural, transportation, location, social, and POI factors driving land expansion by considering different urban land-use categories. Our RF estimations showed that enormous differences existed between various urban land-use types in terms of the role they played in this expansion and their relation to potential determinants, during the different urban development stages studied. Transportation, location, and the distribution of actual land use were found to exert a greater influence on urban land expansion than other factors. All the findings above provide detailed spatiotemporal knowledge and targeted information that can aid in understanding fine-scale urban land use dynamics. In this way, sound planning strategies for different fine-scale land uses can be formulated more scientifically. The strength of association between these factors and urban land expansion differed greatly depending on the different land-use types involved as well as the urban development stage that it occurred within. These results cast a new light on the importance of investigating the potential driving forces in the expansion of different urban land-use types.

Synthesizing location semantics from street view images to improve urban land-use classification

ABSTRACT Land-use maps are instrumental to inform urban planning and environmental research. Street view images (SVIs) have shown great potential for automated land-use classification for land-use mapping. However, previous studies overlooked SVI-derived location contextual information that may help improve land-use classification. This study proposes a novel land-use classification method that synthesizes location semantics from SVIs to account for contextual information from SVIs, land parcels and roads around the SVIs. The proposed method first generates land-use scene images (LUSIs) by using an SVI-derived straightforward algorithm. The LUSIs are then relocated to land parcels by using a displacement strategy and classified into land-use types by using a deep learning network. This study determines the land-use types of land parcels with classified LUSIs. Two case studies, consisting of LUSIs for five land-use types, show that introducing location semantics of SVIs can remarkably improve the classification accuracy of land-use types.

Spatiotemporal analysis of urban growth on forest and agricultural land using geospatial techniques and Shannon entropy method in the satellite town of Ethiopia, the western fringe of Addis Ababa city

Rapid conversion of conserved land and cropland to non-agricultural purposes is threatening the ecological areas and dominant agricultural activities that are the main sources of livelihood in urban fringe areas of Addis Ababa City. The combinations of government policy, socioeconomic, demographic dynamics, and biophysical triggers have driven such transformation. However, the recent fast urban expansion and infrastructural development could have accelerated the severity and rate at which urban growth impacts the ecosystem and fertile agricultural land. Yet, little attention is given about their impacts on forest and farming communities in the western fringe areas of Addis Ababa during the recent past. This study, therefore, aimed at quantifying and analyzing the trends of the urban growth and its impacts on flora and agricultural land in Sebeta-Awas town using an integrated GIS tool, remote sensing technique, and Shannon entropy method. Landsat TM of 1986, ETM+ of 2002, and OLI of 2019 were used to produce land use/land cover (LULC) classes. Object-based classification technique was carried out to generate the LULC and to measure the changes in the urban land-use class within the satellite town in the year 1986, 2002, and 2019. Shannon entropy method was applied to model study area’s urban sprawl, growth trend, and spatial change. Over the past three decades (1986–2019), Sebeta-Awas town has experienced severe urban sprawl following lack of proper development control and management. The annual urban growth rates of 1.2, 5.5, and ~15% for the periods of 1986, 2002, and 2019, respectively were obtained mainly at the expenses of agricultural land (25.48%) and forest land (16.6%), catalyzed urban sprawl which finally led to serious deforestation and reduction in rural farmland. The findings indicate that the average of entropy index increased from 0.02 in the year 1986 to 0.996 in the year 2019, indicating more dispersed urban growth to the outskirts, and spatially indicating anticlockwise shifting. In this regard, more than 90.2% of forest loss due to agricultural encroachment, built-up area expansion, and construction was widely observed in Sebeta and Alem Gena areas, of course, Northeast zone in this study. It is concluded that deforestation and continual evacuation of farming communities in the urban fringe areas due to rapid urban expansion in the name of investment and infrastructural development is expected to be worsened in the near future unless strong policy revision and management actions are undertaken.

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

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.

Landscape Patterns and Building Functions for Urban Land-Use Classification from Remote Sensing Images at the Block Level: A Case Study of Wuchang District, Wuhan, China

Landscape patterns and building functions are successfully used to provide the social sensing information of urban areas. However, previous studies treated ground objects equally, ignoring their size differences. Considering the different contributions of various types of ground objects in land-use classification, this paper measured nine area-weighted mean landscape-level metrics to describe landscape patterns based on the land-cover map, derived from remote sensing images. Additionally, the same idea was applied for identifying building functions. Impervious surfaces, which occupy the majority of urban areas, have a decisive impact on land-use classes. In terms of this, this paper proposed the impervious surface area-weighted building-based indexes from the building outline data. To better represent the physical structure of urban areas, the entire study was based on the analysis units delineated by the OpenStreetMap road network. Finally, a random forest model combining the landscape-level metrics and building-based indexes was adopted in Wuchang District of Wuhan city, China. The results showed that the proposed method was effective at describing landscape patterns and identifying building functions for accurate urban land-use classification, increasing the precision by 10.67%. In general, the contribution of landscape-level metrics to the urban land-use classification is slightly greater than that of building-based indexes. Moreover, different land-use types of analysis units express different landscape patterns. It is of great significance for improving urban form and guiding future urban design. The paper demonstrates that area-weighted landscape metrics and building-based indexes offer a better understanding of urban land use, which plays a vital role in urban planning, construction, and management.

Regional classification of urban land use based on fuzzy rough set in remote sensing images

Regional classification of urban land use based on fuzzy rough set in remote sensing images

Twenty Years of Post-Soviet Union Urban Land Use Change of St. Petersburg

Since the 1990s, Russia (the former Soviet Union, FSU) has undergone radical institutional transformation and drastic economic, political, and social changes. These changes have resulted in complex transformations of the urban land-use patterns. We extracted four major urban land-use classes (residential, industrial, social well-being, and green land) before (1987) and after (2015) the transformation of St. Petersburg. In particular, we focused on residential and industrial land use. In the core and peripheral areas of St. Petersburg, we calculated the percentage of each land-use class and conducted spatial pattern analyses for 1987 and 2015. Specifically, we measured the degree of interspersion/juxtaposition, aggregation and contagion for the major land-use classes in the study area. Results indicated that St. Petersburg experienced significant urban land restructuring from 1987 to 2015. While the city retained some urban land structure from before FSU, it showed trajectories of urban development similar to those of developed counties in Western Europe. The FSU period was the main stage of the outward expansion of urban administrative boundaries. The process of deindustrialization in the urban core during the post-Soviet Union period lagged behind other international cities. The most significant change in the urban land-use pattern was suburbanization in the peripheral area. The suburbanization of St. Petersburg was different from that of other developed countries in that it had its own unique characteristics, both in the residential style and in the resident class. Russia's institutional transformation had strong impacts on urban land-use patterns in the post-Soviet Union period, thus providing a rich practical and theoretical basis for analyzing new models of urban land-use change.

Big spatial data for urban and environmental sustainability

ABSTRACT Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.

The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types

ABSTRACT The formulation of mixed urban land uses is not only intended to find the ideal scenario of land use but also regarded as a way toward sustainable urban development. We propose a geo-semantic mining approach Traj2Vec to quantify the trajectories of residents as high-dimensional semantic vectors. Then, a random forest (RF) method is used to model the relationship between the semantic vectors and mixed urban land uses. The proposed Traj2Vec approach can obtain the highest accuracy (OA = 0.7733, kappa = 0.7245) in urban land-use classification and a high average proportion accuracy (64.0%) in capturing the proportions of urban land-use types. Diversity analysis indicates that Shenzhen has a high degree of mixed urban land use at the scale of a street block. By analyzing the mixing index and the travel distance, we find a weak but significant negative correlation between them (), which not only confirms the conclusion that an increase in the degree of mixing will reduce the travel distances of residents but also verifies the mixing index. This suggests that urban planning should focus on mixed urban land uses, which can reduce the travel distances of residents, reduce energy consumption, and make cities more compact.

Classification of Urban Land Use Based on Graph Theory and Geographic Information System

Classification of Urban Land Use Based on Graph Theory and Geographic Information System

Physical Determinants of Planned and Informal Development in Dar Es Salaam—a Regression Approach to Construct Multi-Temporal Land-Use Data

Developing multi-temporal urban land-use data is considered to be an important undertaking to understand urbanization dynamics and patterns over time and for use in future studies. This study explores approaches to developing more recent urban land-use data for the city of Dar es Salaam, Tanzania, incorporating informal settlements as an urban land-use classification type. Confidence interval and regression approaches are conducted to estimate 2014 land use based on relationships between past urban land use and various independent variables, including existing multi-temporal urban land use and built-up area data, as well as calculated road distance and density, slope, and neighborhood land use. Results show that the logistic binomial regression approach provides acceptable quality 2014 urban land-use estimates.

Combining multi-scale textural features from the panchromatic bands of high spatial resolution images with ANN and MLC classification algorithms to extract urban land uses

ABSTRACTHigh spatial resolution images have been increasingly used for urban land-use classification, but high spectral variations within same land use, the spectral confusion among different land uses, and the shadow problem often lead to poor classification performance of the traditional per-pixel classification methods. Main objectives of this paper were to extract phenomena with different altitudes with the absence of elevation features and shadowed areas without defining a shadow class, identifying the most effective textural features in classification by Regression analysis and also class differentiation with similar spectral properties. To achieve these aims, the panchromatic image of WorldView2, GeoEye1, and QuickBird satellites were applied in order to extract the statistical features of the first and the second order of multi-scale texture analysis, due to high potential for providing more detailed and high spatial resolution in five different window sizes, four different cell shifts, and three different angles or directions. Overall, 137 features were used as input in two classification algorithms including Maximum Likelihood Classifier (MLC) and Artificial Neural Network (ANN). The results showed that the multi-scale textural features and ANN made possible to differentiate three major classes of asphalt, vegetation and building surfaces even with the presence of shadowed area and the absence of elevation features. The experiments also presented that the more the elevation of vertical objects, the more the effect of textural parameters on extraction of these classes. Furthermore, the investigations denoted the validity of the Regression analysis in the detection of most effective textural features in classification.

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

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.

Функциональное зонирование города Кировска (Мурманская область) в условиях туристско-рекреационного освоения его территории

Kirovsk town, located near Hibiny Mountain in the Murmansk region, is known as the industrial center as well as the favorable area for tourism industry development. On the example of Kirovsk town we examined the problem of functional urban zoning of the territory with intensive tourism and recreational development. Based on anintegrated assessment of the territory, analysis of space images and the general scheme of the city development we created the map of functional planning of Kirovsk town and territories of its jurisdiction. The content of existing and projected planning zoneswas disclosed and detailed. In total 10 categories of urban landuse are identified: residential, public, tourist and recreational, sport, industrial, agricultural lands, parks and forest plantations, etc. Current spatial-functional conflicts between the zones used for public utilities, industrial facilities and the zones of potential tourism and recreational (mountain skiing) development are defined as well as the recommendations for its mitigations are proposed. The ecological risks of urban development are analyzed,and the list of environment protection actions is proposed for the functional zones of Kirovsk town. The territories were ranked by favorable and relatively favorable zones of tourist-recreational and sports-recreational developmenttaking into account the identified natural and natural-anthropogenic processes. The conclusion of necessity to adjust the territorial city structure to its new role of tourism, recreational and ski center is made. The general factors, which should be considered in process of urban development, are identified. They include the comprehensive design of city environment considering the greening, the recultivation of industrial plots, located close to tourism and recreational, public zones of the city center, and the features of urban landscape and revealed risks of natural and natural-anthropogenic processes.

An object-based convolutional neural network (OCNN) for urban land use classification

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than Pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the Pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

Incorporating Open Source Data for Bayesian Classification of Urban Land Use From VHR Stereo Images

This study investigates the incorporation of open source data into a Bayesian classification of urban land use from very high resolution (VHR) stereo satellite images. The adopted classification framework starts from urban land cover classification, proceeds to building-type characterization, and results in urban land use. For urban land cover classification, a preliminary classification distinguishes trees, grass, and shadow objects using a random forest at a fine segmentation level. Fuzzy decision trees derived from hierarchical Bayesian models separate buildings from other man-made objects at a coarse segmentation level, where an open street map provides prior building information. A Bayesian network classifier combining commonly used land use indicators and spatial arrangement is used for the urban land use classification. The experiments were conducted on GeoEye stereo images over Oklahoma City, USA. Experimental results showed that the urban land use classification using VHR stereo images performed better than that using a monoscopic VHR image, and the integration of open source data improved the final urban land use classification. Our results also show a way of transferring the adopted urban land use classification framework, developed for a specific urban area in China, to other urban areas. The study concludes that incorporating open source data by Bayesian analysis improves urban land use classification. Moreover, a pretrained convolutional neural network fine tuned on the UC Merced land use dataset offers a useful tool to extract additional information for urban land use classification.

Urban land-use classification by combining high-resolution optical and long-wave infrared images

Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWIR) images are analyzed separately and then combined for urban mapping in this study. The framework of its methodology is based on a two-level classification approach. In the first level, contributions of these two data sources in urban mapping are examined extensively by four types of classifications, i.e. spectral-based, spectral-spatial-based, joint classification, and multiple feature classification. In the second level, an objected-based approach is applied to decline the boundaries. The specificity of our proposed framework not only lies in the combination of two different images, but also the exploration of the LWIR image as one complementary spectral information for urban mapping. To verify the effectiveness of the presented classification framework and to confirm the LWIR’s complementary role in the urban mapping task, experiment results are evaluated by the grss_dfc_2014 data-set.

SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS

Abstract. With the rapid progress of China’s urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model’s ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Urban Built-up Area Extraction and Change Detection of Adama Municipal Area using Time-Series Landsat Images

Urban built-up area information is required in various applications of land use planning and management. However, urban built-up area extraction from moderate spatial resolution Landsat time-series data is challenging because of significant intra-urban heterogeneity and spectral confusion between other landcover types. This paper proposes a technique to extract urban built-up area from time-series Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imageries and determines urban area changes between 1984 to 2015 of Adama Municipal Area of Ethiopia. The study selected three indices, the Enhanced Built-Up and Bareness Index (EBBI), Soil Adjusted Vegetation Index (SAVI) and Modified Normalized Difference Water Index (MNDWI), to represent three major urban land-use classes: built-up and barren/bare land, open waterbody, and vegetation, respectively. The built-up area was extracted by taking the difference between EBBI, SAVI and MNDWI to remove the vegetation and water noises, and the resulted index image was spectrally segmented to separate built-up area from the non-urban built-up lands. The derived index was used to map built-up area for 1984, 1995, 2005 and 2015 periods. The expansion of the built-up area has been revealed as a major change in the area when city area expanded substantially by 293% between 1984 to 2015 periods. The advantage of the method was to use almost the entire spectral range of Landsat imageries which cause less spectral confusion between land cover classes and hence resulted in higher accuracies compared to other indices. The method was effective and simple to implement, and can be used for built-up extraction in other areas.