Xiaojiang Li

Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset

Urban green space provides a series of esthetic, environmental and psychological benefits to urban residents. However, the relationship between the visibility of green vegetation and perceived safety is still in debate. This research investigated whether green vegetation could help to increase the perceived safety based on a crowdsourced dataset: the Place Pulse 1.0 dataset. Place Pulse 1.0 dataset, which was generated from a large number of votes by online participants, includes geo-tagged Google Street View images and the corresponding perceived safety score for each image. In this study, we conducted statistical analyses to analyze the relationship between perceived safety and green vegetation characteristics, which were extracted from Google Street View images. Results show that the visibility of green vegetation plays an important role in increasing perceived safety in urban areas. For different land use types, the relationship between vegetation structures and perceived safety varies. In residential, urban public/institutional, commercial and open land areas, the visibility of vegetation higher than 2.5 m has significant positive correlations with perceived safety, but there exists no significant correlation between perceived safety and the percentage of green vegetation in transportation and industrial areas. The visibility of vegetation below 2.5 m has no significant relationship with the perceived safety in almost all land use types, except for multifamily residential land and urban public/institutional land. In general, this study provided insight for the relationship between green vegetation characteristics and the perception of environment, as well as valuable reference data for developing urban greening programs.

An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery

Urban areas are major places where intensive interactions between human and the natural system occur. Urban vegetation is a major component of the urban ecosystem, and urban residents benefit substantially from urban green spaces. To measure urban green spaces, remote sensing is an established tool due to its capability of monitoring urban vegetation quickly and continuously. In this study: (1) a Building’s Proximity to Green spaces Index (BPGI) was proposed as a measure of building’s neighbouring green spaces; (2) LiDAR data and multispectral remotely sensed imagery were used to automatically extract information regarding urban buildings and vegetation; (3) BPGI values for all buildings were calculated based on the extracted data and the proximity and adjacency of buildings to green spaces; and (4) two districts were selected in the study area to examine the relationships between the BPGI and different urban environments. Results showed that the BPGI could be used to evaluate the proximity of residents to green spaces at building level, and there was an obvious disparity of BPGI values and distribution of BPGI values between the two districts due to their different urban functions (i.e., downtown area and residential area). Since buildings are the major places for residents to live, work and entertain, this index may provide an objective tool for evaluating the proximity of residents to neighbouring green spaces. However, it was suggested that proving correlations between the proposed index and human health or environmental amenity would be important in future research for the index to be useful in urban planning.

Incorporating Spectral Similarity Into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Postclassification

Spatial statistics provides useful methods for incorporating spatial dependence into land cover classification. However, the geometric features of land cover classes are difficult to be captured by geostatistical models due to smoothing effect. The objective of this study is to incorporate spectral similarity into the Markov chain random field (MCRF) cosimulation (coMCRF) model, that is, to propose a spectral similarity-enhanced MCRF cosimulation (SS-coMCRF) model, for land cover postclassification so that postclassification will cause less geometric loss. Two mutually complementary spectral similarity measures, Jaccard index and the spectral correlation measure, were employed as a constraining factor in SS-coMCRF. One medium spatial resolution scene with a complex landscape and one very high spatial resolution scene with an urban landscape were selected for case studies. Neural network classifier and support vector machine classifier were used to conduct land cover preclassifications. Both coMCRF and SS-coMCRF were used to postprocess preclassified images based on expert-interpreted sample datasets from multiple data sources. Compared with preclassified results that depend on only spectral information of pixels, postclassifications by both models achieved similar significant improvements in overall accuracy. However, compared with coMCRF, the SS-coMCRF model apparently improved postclassified land cover patterns by effectively capturing some geometric features (e.g., boundaries and linear stripes) and more details of land cover classes. In general, incorporating spectral similarity into land cover postclassification through SS-coMCRF may contribute significantly to the “shape” or geometric accuracy of classified land cover classes.

Building block level urban land-use information retrieval based on Google Street View images

Land-use maps are important references for urban planning and urban studies. Given the heterogeneity of urban land-use types, it is difficult to differentiate different land-use types based on overhead remotely sensed data. Google Street View (GSV) images, which capture the façades of building blocks along streets, could be better used to judge the land-use types of different building blocks based on their façade appearances. Recently developed scene classification algorithms in computer vision community make it possible to categorize different photos semantically based on various image feature descriptors and machine-learning algorithms. Therefore, in this study, we proposed a method to derive detailed land-use information at building block level based on scene classification algorithms and GSV images. Three image feature descriptors (i.e., scale-invariant feature transform-Fisher, histogram of oriented gradients, GIST) were used to represent GSV images of different buildings. Existing land-use maps were used to create training datasets to train support vector machine (SVM) classifiers for categorizing GSV images. The trained SVM classifiers were then applied to case study areas in New York City, Boston, and Houston, to predict the land-use information at building block level. Accuracy assessment results show that the proposed method is suitable for differentiating residential buildings and nonresidential buildings with an accuracy of 85% or so. Since the GSV images are publicly accessible, this proposed method would provide a new way for building block level land-use mapping in future.

Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

Modelling building proximity to greenery in a three-dimensional perspective using multi-source remotely sensed data

Urban vegetation is important for the well-being of urban residents. Remotely sensed datasets can be used to efficiently quantify urban green spaces (UGSs) across broad spatial extents. Different methods have been developed to quantitatively describe UGSs using remotely sensed datasets. However, few studies have taken the vertical dimension into consideration in evaluating human interactions with nearby greenery. In this study, a new index, called the ‘3D building proximity to greenery index’ (3DBPGI), is proposed to evaluate the proximity of a building to its nearby urban greenery within a buffer distance by accounting for the building’s height and different vegetation types. The 3DBPGI values for buildings in a Hungarian city, Székesfehérvár, were calculated. The results of the case study show that this index can indicate to some extent the human proximity to greenery for each building block in urban areas, which further can help planners to find critical areas for urban greening programmes.