scholarly journals A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

2020 ◽  
Vol 12 (6) ◽  
pp. 1032
Author(s):  
Shengyu Xu ◽  
Linbo Qing ◽  
Longmei Han ◽  
Mei Liu ◽  
Yonghong Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Image Data ◽  
Urban Region ◽  
Remote Sensing Images ◽  
Social Sensing ◽  
Sensing Data ◽  
Functional Regions ◽  
Point Of Interest ◽  
Wide Range

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

scholarly journals Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3597
Author(s):  
Xuanyan Dong ◽  
Yue Xu ◽  
Leping Huang ◽  
Zhigang Liu ◽  
Yi Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Remote Sensing Images ◽  
Social Sensing ◽  
Sensing Data ◽  
Object Based ◽  
Land Use Types ◽  
Functional Areas

The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.

scholarly journals Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

2019 ◽  
Vol 11 (5) ◽  
pp. 574 ◽  
Author(s):  
Xuchao Yang ◽  
Tingting Ye ◽  
Naizhuo Zhao ◽  
Qian Chen ◽  
Wenze Yue ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Population Density ◽  
Census Data ◽  
Population Distribution ◽  
Environmental Data ◽  
Ancillary Data ◽  
Population Redistribution ◽  
Social Sensing ◽  
Sensing Data ◽  
Point Of Interest

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

scholarly journals Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

2019 ◽  
Vol 16 (21) ◽  
pp. 4102 ◽  
Author(s):  
Shen ◽  
Zhou ◽  
Li ◽  
Zeng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Urban Areas ◽  
Heterogeneous Data ◽  
Research Strategy ◽  
Concentration Data ◽  
Social Sensing ◽  
Sensing Data ◽  
Nonlinear Relationships ◽  
Pm2.5 Concentration

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

scholarly journals Defining Boundaries and Dynamics of Urbanized Areas According to Remote Data Sensing

2020 ◽  
pp. 46-52
Author(s):  
Andrii Marushchynets
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Spatial Development ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Wide Range ◽  
The Usa ◽  
Urbanized Areas

The article is devoted to the original approach of defining continuously built-up urban areas as well as the dynamics of this process by means of Earth remote sensing data analysis. Basing on this approach, the spreading borders of continuously built-up urban areas of Kyiv city and its suburbs during 1976-2018 have been defined. Earth remote sensing data, as a valuable source of information on land surface in general and built-up areas in particular, provides wide range of opportunities for researching the process of spatial development of urbanized areas. Analysis of built-up territories during significant period of time allows defining spatial development vectors of urbanized regions, modern continuously built-up areas and their borders. The review of similar researches has revealed that the most convenient sources of Earth remote sensing data for defining the area of built-up territories are represented by multispectral space footages of Landsat space program of the USA. The deciphering of space footages and defining of built-up areas has been conducted involving spectral indexes, which is the most precise method of deciphering the Earth remote sensing data. Thus, we managed to define built-up and non-built-up areas as well as water objects of Kyiv city and its suburbs for 1976, 1985, 2002 and 2008. A set of illustrating schematic maps has been created, depicting borders of built-up area. A continuously built-up urban area has amalgamated Kyiv city and a number of surrounding settlements into a highly-urbanized core. During 1976-2018, the area of continuously built-up urban territory of Kyiv expanded 1,5 times and mostly southwestwards.

Fuzzy segmentation of urban areas in panchromatic remote sensing images

2009 ◽  
Vol 28 (12) ◽  
pp. 3112-3115
Author(s):  
Yan CHEN ◽  
Shou-hong WAN ◽  
Yu-chang GONG
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Remote Sensing Images ◽  
Fuzzy Segmentation

scholarly journals An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2818
Author(s):  
Hai Sun ◽  
Xiaoyi Dai ◽  
Wenchi Shou ◽  
Jun Wang ◽  
Xuejing Ruan
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water Body ◽  
Remote Sensing Data ◽  
Flood Inundation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Tracking Model

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

scholarly journals Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

2021 ◽  
Vol 13 (4) ◽  
pp. 699
Author(s):  
Tingting Zhou ◽  
Haoyang Fu ◽  
Chenglin Sun ◽  
Shenghan Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Mean Shift ◽  
Color Space ◽  
Compensation Method ◽  
Shadow Detection ◽  
Processing Unit ◽  
Shadow Region ◽  
Remote Sensing Images ◽  
Shadow Compensation

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

scholarly journals Estimation of the temperature distribution in urban areas by using remote sensing data.

1994 ◽  
Vol 22 ◽  
pp. 267-273 ◽  
Author(s):  
Shinji KANEKO ◽  
Toshiie MAEDA ◽  
Takahito UENO ◽  
Hidefumi IMURA
Keyword(s):  
Remote Sensing ◽  
Temperature Distribution ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Visual Categorization ◽  
Class Differences ◽  
Fine Grained ◽  
Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

From inflow to interflow, through plunging and lofting: uncovering the dominant flow processes of a sediment-rich negatively buoyant river inflow into a stratified lake

2021 ◽  
Author(s):  
Stan Thorez ◽  
Koen Blanckaert ◽  
Ulrich Lemmin ◽  
David Andrew Barry
Keyword(s):  
Remote Sensing ◽  
River Mouth ◽  
Ambient Water ◽  
Remote Sensing Images ◽  
Reservoir Water ◽  
Neutral Buoyancy ◽  
Wide Range ◽  
Adcp Measurements ◽  
Vorticity Production ◽  
Negatively Buoyant

<p>Lake and reservoir water quality is impacted greatly by the input of momentum, heat, oxygen, sediment, nutrients and contaminants delivered to them by riverine inflows. When such an inflow is negatively buoyant, it will plunge upon contact with the receiving ambient water and form a gravity-driven current near the bed (density current). If such a current is sediment-laden, its bulk density can be higher than that of the surrounding ambient water, even if its carrying fluid has a density lower than that of the surrounding ambient water. After sufficient sediment particles have settled however, the buoyancy of the current can reverse and lead to the plume rising up from the bed, a process referred to as lofting. In a stratified environment, the river plume may then find its way into a layer of neutral buoyancy to form an intermediate current (interflow). A deeper understanding of the wide range of hydrodynamic processes related to the transitions from open-channel inflow to underflow (plunging) and from underflow to interflow (lofting) is crucial in predicting the fate of all components introduced into the lake or reservoir by the inflow.</p><p>Field measurements of the plunging inflow of the negatively buoyant Rhône River into Lake Geneva (Switzerland/France) are presented. A combination of a vessel-mounted ADCP and remote sensing cameras was used to capture the three-dimensional flow field of the plunging and lofting transition zones over a wide range of spatial and temporal scales.</p><p>In the plunge zone, the ADCP measurements show that the inflowing river water undergoes a lateral (perpendicular to its downstream direction) slumping movement, caused by its density surplus compared to the ambient lake water and the resulting baroclinic vorticity production. This effect is also visible in the remote sensing images in the form of a distinct plume of sediment-rich water with a triangular shape leading away from the river mouth in the downstream direction towards a sharp tip. A wide range of vortical structures, which most likely impact the amount of mixing taking place, is also visible at the surface in the plunging zone.</p><p>In the lofting zone, the ADCP measurements show that the underflow undergoes a lofting movement at its edges. This is most likely caused by a higher sedimentation rate due to the lower velocities at the underflow edges and leads to a part of the underflow peeling off and forming an interflow, while the higher velocity core of the underflow continues following the bed. Here, the baroclinic vorticity production works in the opposite direction as that in the plunge zone. Further downstream, as more particles have settled and the surrounding ambient water has become denser, the remaining underflow also undergoes a lofting motion. The remnants of these lofting processes show in the remote sensing images as intermittent ‘boils’ of sediment rich water reaching the surface and traces of surface layer leakage.</p>

Sign in / Sign up

Export Citation Format

Share Document