scholarly journals Towards a Multi-Temporal Deep Learning Approach for Mapping Urban Fabric Using Sentinel 2 Images

2020 ◽  
Vol 12 (3) ◽  
pp. 423 ◽  
Author(s):  
Lamiae El Mendili ◽  
Anne Puissant ◽  
Mehdi Chougrad ◽  
Imane Sebari
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Urban Fabric ◽  
Temporal Dimension ◽  
The Future ◽  
Multi Temporal ◽  
Sentinel 2

The major part of the population lives in urban areas, and this is expected to increase in the future. The main challenges faced by cities currently and towards the future are the rapid urbanization, the increase in urban temperature and the urban heat island. Mapping and monitoring urban fabric (UF) to analyze the environmental impact of these phenomena is more necessary than ever. This coupled with the increased availability of Earth observation data and their growing temporal capabilities leads us to consider using temporal features for improving land use classification, especially in urban environments where the spectral overlap between classes makes it challenging. Urban land use classification thus remains a central question in remote sensing. Although some research studies have successfully used multi-temporal images such as Landsat-8 or Sentinel-2 to improve land cover classification, urban land use mapping is rarely carried using the temporal dimension. This paper explores the use of Sentinel-2 data in a deep learning framework, by firstly assessing the temporal robustness of four popular fully convolutional neural networks (FCNs) trained over single-date images for the classification of the urban footprint, and secondly, by proposing a multi-temporal FCN. A performance comparison between the proposed framework and a regular FCN is also conducted. In this study, we consider four UF classes typical of many European Western cities. Results show that training the proposed multi-date model on Sentinel 2 multi-temporal data achieved the best results with a Kappa coefficient increase of 2.72% and 6.40%, respectively for continuous UF and industrial facilities. Although a more definitive conclusion requires further testing, first results are promising because they confirm that integrating the temporal dimension with a high spatial resolution into urban land use classification may be a valuable strategy to discriminate among several urban categories.

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 Urban Land-Use Dynamics in the Niger Delta: The Case of Greater Port Harcourt Watershed

Urban Science ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 108 ◽  
Author(s):  
Nimi Dan-Jumbo ◽  
Marc Metzger ◽  
Andrew Clark
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Niger Delta ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Hard Copy ◽  
Land Use Classification ◽  
Depth Analysis

Cities in developing countries are urbanising at a rapid rate, resulting in substantial pressures on environmental systems. Among the main factors that lead to flooding, controlling land-use change offers the greatest scope for the management of risk. However, traditional analysis of a “from–to” change matrix is not adequate to provide information of all the land-use changes that occur in a watershed. In this study, an in-depth analysis of land-use change enabled us to quantify the bulk of the changes accumulating from swap changes in a tropical watershed. This study assessed the historical and future land-use/land-cover (LULC) dynamics in the River State region of the Niger Delta. Land-use classification and change detection analysis was conducted using multi-source (Landsat TM, ETM, polygon map, and hard copy) data of the study area for 1986, 1995, and 2003, and projected conditions in 2060. The key findings indicate that historical urbanisation was rapid; urban expansion could increase by 80% in 2060 due to planned urban development; and 95% of the conversions to urban land occurred chiefly at the expense of agricultural land. Urban land was dominated by net changes rather than swap changes, which in the future could amplify flood risk and have other severe implications for the watershed.

scholarly journals An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3254
Author(s):  
Zhou Huang ◽  
Houji Qi ◽  
Chaogui Kang ◽  
Yuelong Su ◽  
Yu Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Ensemble Learning ◽  
Urban Land ◽  
Urban Land Use ◽  
Learning Approach ◽  
Land Use Classification ◽  
Remote Sensing Imagery ◽  
Sensing Data

Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

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