Detecting new building construction in urban areas based on images of small unmanned aerial system

Ning, Huan; Huang, Xiao; Li, Zhenlong; Wang, Cuizhen; Xiang, Duowen

doi:10.1080/23754931.2019.1707108

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…On the other hand, using aerial and street view images can be far more scalable, faster, and cost effective for extracting sidewalks that is highly practical and commonly used by researchers. Recently, many approaches have been implemented for the detection and mapping of objects from earth observation data, for example, generating land cover maps (Ning et al, 2020), monitoring construction of new buildings (Ning et al, 2019), and detection of road features (Kim et al, 2021; Moran, 2022) from remote sensing. Besides, the emergence of georeferenced photographs has created a new dimension for urban planning offering a variety of rich spatial and temporal datasets.…”

Section: Introductionmentioning

confidence: 99%

Mapping sidewalks on a neighborhood scale from street view images

Hamim,

Kancharla,

Ukkusuri

2023

Environment and Planning B: Urban Analytics and City Science

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Although reliable and accurate inventorying of sidewalks is time consuming, it can aid urban planners in decision making for infrastructure development. Recent advancements in computer vision and machine learning algorithms have improved the reliability and accuracy of automated inventorying. This research uses a deep learning architecture-based semantic segmentation model (i.e., HRNet + OCR) to automate sidewalk identification using Google Street View (GSV) images. The results show that retraining the model using local training images yields 114.16% and 178.11% higher performance in terms of intersection over union (IoU) metric compared to pretrained model using Cityscapes and Mapillary datasets, respectively. The developed model showed excellent performance in predicting the presence of sidewalks in an image by achieving high accuracy (0.9557), precision (0.9447), recall (0.9900), and F1- score (0.9668). Further, in comparison with EfficientNet, a computationally efficient image classification model, the present model showed superior performance in predicting sidewalk presence at the image level. Therefore, integrating local training images containing minimum required labels (in this study, roads, sidewalks, buildings, and walls) with publicly available training datasets can help increase the performance of the semantic segmentation model for extracting the required features (in this study, roads and sidewalks) from GSV images, especially in developing countries like Bangladesh. This study generates sidewalk maps on a neighborhood scale, which can be useful visualization tools for researchers and practitioners to understand the existing pedestrian infrastructure and plan for future improvements.

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Section: Introductionmentioning

confidence: 99%

Mapping sidewalks on a neighborhood scale from street view images

Hamim,

Kancharla,

Ukkusuri

2023

Environment and Planning B: Urban Analytics and City Science

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“…For example, land cover maps can be generated from remote sensing imagery for a large area (Kussul et al, 2017;Ning et al, 2020b). New buildings can be monitored for regulation purposes (Ning et al, 2020a;Vakalopoulou et al, 2015). Road detection has been a long-term research question in the remote sensing community (Li et al, 2018;Shi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Sidewalk extraction using aerial and street view images

Ning

Chen

et al. 2021

Environment and Planning B: Urban Analytics and City Science

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A reliable, punctual, and spatially accurate dataset of sidewalks is vital for identifying where improvements can be made upon urban environment to enhance multi-modal accessibility, social cohesion, and residents' physical activity. This paper develops a synthetically new spatial procedure to extract the sidewalk by integrating the detected results from aerial and street view imagery. We first train neural networks to extract sidewalks from aerial images, and then use pre-trained models to restore occluded and missing sidewalks from street view images. By combining the results from both data sources, a complete network of sidewalks can be produced. Our case study includes four counties in the U.S., and both precision and recall reach about 0.9. The street view imagery helps restore the occluded sidewalks and largely enhances the sidewalk network's connectivity by linking 20% of dangles.

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Change detection of urban objects using 3D point clouds: A review

Stilla¹,

Xu²

2023

ISPRS Journal of Photogrammetry and Remote Sensing

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Detecting new building construction in urban areas based on images of small unmanned aerial system

Cited by 6 publications

References 32 publications

Mapping sidewalks on a neighborhood scale from street view images

Mapping sidewalks on a neighborhood scale from street view images

Sidewalk extraction using aerial and street view images

Change detection of urban objects using 3D point clouds: A review

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