Joining Street-View Images and Building Footprint GIS Data

2021 ◽  
Author(s):  
Yoshiki Ogawa ◽  
Takuya Oki ◽  
Shenglong Chen ◽  
Yoshihide Sekimoto
Keyword(s):  
Street View ◽  
Gis Data ◽  
Building Footprint

scholarly journals Developing Sidewalk Inventory Data Using Street View Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3300
Author(s):  
Bumjoon Kang ◽  
Sangwon Lee ◽  
Shengyuan Zou
Keyword(s):  
Detection Method ◽  
Random Forest Classifier ◽  
Inventory Data ◽  
City Development ◽  
Accuracy Rate ◽  
Street Level ◽  
Segmented Image ◽  
Street View ◽  
Image Segment ◽  
Gis Data

(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.

scholarly journals Suggestive Site Planning with Conditional GAN and Urban GIS Data

2021 ◽  
pp. 103-113
Author(s):  
Runjia Tian
Keyword(s):  
Neural Network ◽  
Landscape Architecture ◽  
Multiple Constraints ◽  
Building Layout ◽  
Gis Data ◽  
Open Source Gis ◽  
Building Footprint ◽  
Site Planning ◽  
A Site ◽  
The City

AbstractIn architecture, landscape architecture, and urban design, site planning refers to the organizational process of site layout. A fundamental step for site planning is the design of building layout across the site. This process is hard to automate due to its multi-modal nature: it takes multiple constraints such as street block shape, orientation, program, density, and plantation. The paper proposes a prototypical and extensive framework to generate building footprints as masterplan references for architects, landscape architects, and urban designers by learning from the existing built environment with Artificial Neural Networks. Pix2PixHD Conditional Generative Adversarial Neural Network is used to learn the mapping from a site boundary geometry represented with a pixelized image to that of an image containing building footprint color-coded to various programs. A dataset containing necessary information is collected from open source GIS (Geographic Information System) portals from the city of Boston, wrangled with geospatial analysis libraries in python, trained with the TensorFlow framework. The result is visualized in Rhinoceros and Grasshopper, for generating site plans interactively.

Development of Building Height Data in Peru from High-Resolution SAR Imagery

2014 ◽  
Vol 9 (6) ◽  
pp. 1042-1049 ◽  
Author(s):  
Wen Liu ◽  
◽  
Fumio Yamazaki ◽  
Bruno Adriano ◽  
Erick Mas ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Image ◽  
Ground Surface ◽  
Sar Images ◽  
Building Height ◽  
Radar Sensors ◽  
Gis Data ◽  
Building Footprint ◽  
Image Displays ◽  
Sar Imagery

Building data, such as footprint and height, are important information for pre- and post-event damage assessments when natural disasters occur. However, these data are not easily available in many countries. Because of the remarkable improvements in radar sensors, high-resolution (HR) Synthetic Aperture Radar (SAR) images can provide detailed ground surface information. Thus, it is possible to observe a single building using HR SAR images. In this study, a new method is developed to detect building heights automatically from two-dimensional (2D) geographic information system (GIS) data and a single HR TerraSAR-X (TSX) intensity image. A building in a TSX image displays a layover from the actual position to the direction of the sensor, because of the side-looking nature of the SAR. Since the length of the layover on a ground-range SAR image is proportional to the building height, it can be used to estimate this height. We shift the building footprint obtained from 2D GIS data toward the sensor direction. The proposed method was applied to a TSX image of Lima, Peru in the HighSpot mode with a resolution of about 1 m. The results were compared with field survey photos and an optical satellite image, and a reasonable level of accuracy was achieved.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

New GNSS receivers expand the range of applications in the GIS data acquisition Neue GNSS-Empfänger erweitern das Einsatzspektrum bei der GIS-Datenerfassung

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 36-37
Author(s):  
Schmitt, T
Keyword(s):  
Data Acquisition ◽  
Gis Data ◽  
Gnss Receivers

New GNSS receivers expand the range of applications in the GIS data acquisition Neue GNSS-Empfänger erweitern das Einsatzspektrum bei der GIS-Datenerfassung

Printing GIS data: More than "click to print" GIS-Daten drucken: Mehr als "click to print"

GIS Business ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. 36-37
Author(s):  
Tschunkert, A
Keyword(s):  
Gis Data

Printing GIS data: More than "click to print" GIS-Daten drucken: Mehr als "click to print

APLIKASI PENAJAMAN CITRAA STREET VIEW MENGGUNAKAN METODE GRAMD SCHMIDT SPECTRAL SHARPENING DAN HIGH PASS FILTERING

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Erna Verawati ◽  
Surya Darma Nasution ◽  
Imam Saputra
Keyword(s):  
Image Processing ◽  
Visual Information ◽  
Intensity Difference ◽  
Original Image ◽  
Filtering Method ◽  
The Road ◽  
Street View ◽  
Result Show ◽  
Filter Process ◽  
High Pass

Sharpening the image of the road display requies a degree of brightness in the process of sharpening the image from the original image result of the improved image. One of the sharpening of the street view image is image processing. Image processing is one of the multimedia components that plays an important role as a form of visual information. There are many image processing methods that are used in sharpening the image of street views, one of them is the gram schmidt spectral sharpening method and high pass filtering. Gram schmidt spectral sharpening method is method that has another name for intensity modulation based on a refinement fillter. While the high pass filtering method is a filter process that btakes image with high intensity gradients and low intensity difference that will be reduced or discarded. Researce result show that the gram schmidt spectral sharpening method and high pass filtering can be implemented properly so that the sharpening of the street view image can be guaranteed sharpening by making changes frome the original image to the image using the gram schmidt spectral sharpening method and high pass filtering.Keywords: Image processing, gram schmidt spectral sharpening and high pass filtering.

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