This page has only limited features, please log in for full access.
(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street-level sidewalk detection method with image-processing Google Street View data. (2) Methods: Street view images were processed to produce graph-based segmentations. Image segment regions were manually labeled and a random forest classifier was established. We used multiple aggregation steps to determine street-level sidewalk presence. (3) Results: In total, 2438 GSV street images and 78,255 segmented image regions were examined. The image-level sidewalk classifier had an 87% accuracy rate. The street-level sidewalk classifier performed with nearly 95% accuracy in most streets in the study area. (4) Conclusions: Highly accurate street-level sidewalk GIS data can be successfully developed using street view images.
Bumjoon Kang; Sangwon Lee; Shengyuan Zou. Developing Sidewalk Inventory Data Using Street View Images. Sensors 2021, 21, 3300 .
AMA StyleBumjoon Kang, Sangwon Lee, Shengyuan Zou. Developing Sidewalk Inventory Data Using Street View Images. Sensors. 2021; 21 (9):3300.
Chicago/Turabian StyleBumjoon Kang; Sangwon Lee; Shengyuan Zou. 2021. "Developing Sidewalk Inventory Data Using Street View Images." Sensors 21, no. 9: 3300.
Abandoned houses (AH) are focal points in urban communities by threatening local security, destroying housing markets, and burdening government finance in the U.S. legacy cities. In particular, individual-level AH detection provides essential information for fine-resolution urban studies, government decision-makers, and private sector practitioners. However, three primary conventional data sources (field data, utility data, and remote sensing data) cannot suffice to collect such fine-resolution data in the large spatial area via a cost-effective approach. To this end, Google Street View (GSV) imagery, which emerges as the mainstream open-access data source with global coverage, provides an opportunity to address this issue. Subsequently, a follow-up challenge confronting the detection of AH arises from the fact that it lacks an effective method that can discern authentic visual features from the redundant noise in GSV images. In this study, we aim to develop an effective method to detect individual-level AH from GSV imagery. Specifically, we developed a new hierarchical deep learning method to leverage both global and local visual features of AH in the detection. The method can be further divided into three steps: (1) Scene-based classification that can extract global visual features of AH was implemented through fine-tuning a pre-trained deep convolutional neural network (CNN) model. (2) We developed a patch-based classification method that can extract specific local features of AH. In this method, patches were generated from GSV images based on auto-detected local features, followed by being labeled as three categories: building patches, vegetation patches, and others. Two deep CNN models were employed to identify deteriorated building façade patches and overgrown vegetation patches, respectively. (3) Individual-level AH were detected by integrating scene classification results and patch classification results in a decision-tree model. Experimental results showed that the F-score of AH was 0.84 in a well-prepared dataset collected from five different Rust Belt cities. The proposed hierarchical deep learning approach effectively improved the accuracy comparing with the traditional scene-based method. In addition, the proposed method was applied to generate an AH map in a new site in Detroit, MI. Our study demonstrated the feasibility of GSV imagery in AH detection and showed great potential to detect AH in a large spatial extent.
Shengyuan Zou; Le Wang. Detecting individual abandoned houses from google street view: A hierarchical deep learning approach. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 175, 298 -310.
AMA StyleShengyuan Zou, Le Wang. Detecting individual abandoned houses from google street view: A hierarchical deep learning approach. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 175 ():298-310.
Chicago/Turabian StyleShengyuan Zou; Le Wang. 2021. "Detecting individual abandoned houses from google street view: A hierarchical deep learning approach." ISPRS Journal of Photogrammetry and Remote Sensing 175, no. : 298-310.
To embrace the burgeoning land change science studies that exploit public-domain cloud-computing platforms such as Google Earth Engine (GEE), for the first time, we organized a special issue entitled “Remote Sensing of Land Change Science with Google Earth Engine” in the journal “Remote Sensing of Environment”. This paper serves as a summary to a collection of 19 papers that have been published since the inception of the special issue in November 2017. In particular, we summarized their contributions with regard to two perspectives: what new themes of questions are articulated, what contributions have been made. Taking account of the disciplinary difference, we carried out the summary separately in two major science domains: Remote Sensing of Environment (RSE), i.e., naturally-induced land change, and Remote Sensing of Society (RSS), i.e., human-induced land change. Furthermore, we presented a historical review of the developments of GEE-relevant studies published before our special issue. Finally, we provided a future prospect on how GEE will continue to evolve to further the study of land change science.
Le Wang; Chunyuan Diao; George Xian; Dameng Yin; Ying Lu; Shengyuan Zou; Tyler A. Erickson. A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment 2020, 248, 112002 .
AMA StyleLe Wang, Chunyuan Diao, George Xian, Dameng Yin, Ying Lu, Shengyuan Zou, Tyler A. Erickson. A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment. 2020; 248 ():112002.
Chicago/Turabian StyleLe Wang; Chunyuan Diao; George Xian; Dameng Yin; Ying Lu; Shengyuan Zou; Tyler A. Erickson. 2020. "A summary of the special issue on remote sensing of land change science with Google earth engine." Remote Sensing of Environment 248, no. : 112002.
The formation and demolition of vacant houses are the most visible sign of city shrinking and revitalization. Timely detection of vacant houses has become an inevitable task to aid the “Smart City” initiative. Two pressing problems exist for vacant houses, however: (1) No publicly accessible information is available at the individual house level and (2) the decennial census survey does not catch up with the rapidly changing status of vacant houses. To this end, remote sensing provides a low-cost avenue for detecting vacant houses. Traditionally, remote sensing was accredited for its success in deriving biophysical parameters of human settlements, such as the presence and physical size of buildings. It is still a challenge, though, to infer the functions of buildings, such as land-use types and occupancy status. In this study, we aim to detect individual vacant houses with very-high-resolution remote sensing images through a smart machine learning method. Our proposed method entails three steps: ground-truth data collection, classification, and feature selection. As a result, a new building change detection method was developed to collect ground-truth vacant house data from multitemporal images. Important features for classification of houses were identified. Subsequently, we carried out a classification of vacant houses and yielded promising results. Furthermore, the results indicate that both the area of the vacant house parcels and the healthy conditions of the surrounding vegetation contribute most to the detection accuracy. Our work shows the potential of using remote sensing to detect individual vacant houses at a large spatial extent. Key Words: machine learning, remote sensing, smart city, vacant house.
Shengyuan Zou; Le Wang. Individual Vacant House Detection in Very-High-Resolution Remote Sensing Images. Annals of the American Association of Geographers 2019, 110, 449 -461.
AMA StyleShengyuan Zou, Le Wang. Individual Vacant House Detection in Very-High-Resolution Remote Sensing Images. Annals of the American Association of Geographers. 2019; 110 (2):449-461.
Chicago/Turabian StyleShengyuan Zou; Le Wang. 2019. "Individual Vacant House Detection in Very-High-Resolution Remote Sensing Images." Annals of the American Association of Geographers 110, no. 2: 449-461.
Public Earth Observation (EO) data archives, e.g., MODIS, Landsat, and Sentinels, are valuable sources of information for a broad range of applications. For decision-supporting applications used in urban planning, land management, and sustainable development, images covering regions similar to the study area are prerequisites for high-accuracy decision making. These desirable images cannot be quickly searched for in the EO data archives via image metadata alone but can be obtained through content-based image retrieval methods. Land cover (LC) information, traditionally obtained through image segmentation or classification processing, is typically used in existing methods. Image processing is time consuming and has various accuracy levels for heterogeneous images, thus decreasing retrieval efficiency and accuracy. Additionally, the monotemporal LC information used has a limited ability to distinguish among confusable regions with different terrain, e.g., forests located on flatlands or mountains, and to obtain regions, e.g., urban regions, with similar growth rates. In this study, we employ free multiple-year 30 m LC products, a terrain product, and the Google Earth Engine (GEE) platform to accurately and efficiently locate the desired heterogeneous moderate spatial resolution images from various public EO data archives. Regions similar to the query region are detected with two-stage similarity calculations: First, monotemporal pixel-based LC and terrain information are used to filter out the most dissimilar regions; second, object-based LC change and terrain information are used to locate similar regions. Then, the desired images covering these detected similar regions are obtained from EO data archives via image metadata, e.g., geographical location and acquisition time. The experimental results of the two representative query regions show that our method can be used to obtain the desired images within several minutes and has higher accuracy than the LandEx method and a simplified method using only monotemporal LC information. The main contribution of our study is to reveal that LC changes and terrain information are helpful for improving the retrieval accuracy achieved from monotemporal LC information alone. Our method has great operability, with no need to perform EO data acquisition, image processing of raw EO images, or management of computational resources. Our method is conducive to making full use of images in various public EO archives to improve the decision making quality of decision-supporting applications.
Feifei Peng; Le Wang; Shengyuan Zou; Jing Luo; Shengsheng Gong; Xiran Li. Content-based search of earth observation data archives using open-access multitemporal land cover and terrain products. International Journal of Applied Earth Observation and Geoinformation 2019, 81, 13 -26.
AMA StyleFeifei Peng, Le Wang, Shengyuan Zou, Jing Luo, Shengsheng Gong, Xiran Li. Content-based search of earth observation data archives using open-access multitemporal land cover and terrain products. International Journal of Applied Earth Observation and Geoinformation. 2019; 81 ():13-26.
Chicago/Turabian StyleFeifei Peng; Le Wang; Shengyuan Zou; Jing Luo; Shengsheng Gong; Xiran Li. 2019. "Content-based search of earth observation data archives using open-access multitemporal land cover and terrain products." International Journal of Applied Earth Observation and Geoinformation 81, no. : 13-26.
The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities.
Mingzhu Du; Le Wang; Shengyuan Zou; Chen Shi. Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data. Remote Sensing 2018, 10, 1920 .
AMA StyleMingzhu Du, Le Wang, Shengyuan Zou, Chen Shi. Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data. Remote Sensing. 2018; 10 (12):1920.
Chicago/Turabian StyleMingzhu Du; Le Wang; Shengyuan Zou; Chen Shi. 2018. "Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data." Remote Sensing 10, no. 12: 1920.