Trees Detection on Google Street View Images Using Deep Learning and City Open Data

2020 ◽  
pp. 236-243
Author(s):  
Lieu-Hen Chen ◽  
Hao-Ming Hung ◽  
Cheng-Yu Sun ◽  
Eric Hsiao-Kuang Wu ◽  
Toru Yamaguchi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Data ◽  
Google Street View ◽  
Street View

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

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2484 ◽  
Author(s):  
Weixing Zhang ◽  
Chandi Witharana ◽  
Weidong Li ◽  
Chuanrong Zhang ◽  
Xiaojiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Measurement Method ◽  
Detection Algorithm ◽  
Quality Data ◽  
Visual Interpretation ◽  
Buffer Zones ◽  
Google Street View ◽  
Street View ◽  
Utility Poles

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.

scholarly journals Identificação de Composições da Paisagem Urbana

2020 ◽  
Vol 8 (1) ◽  
pp. e00140
Author(s):  
Ana Luiza Favarão Leão ◽  
Hugo Queiroz Abonizio ◽  
Sylvio Barbon Júnior ◽  
Milena Kanashiro
Keyword(s):  
Deep Learning ◽  
Google Street View ◽  
Street View

A composição do ambiente pode exercer impactos sobre comportamentos, no entanto, esta relação permanece incerta até que qualidades e a paisagem das cidades possam ser analisadas empiricamente. Imagens obtidas através do Google Street View (GSV) possibilitam um grande volume de dados para avaliação automatizada das características ambientais. Técnicas de deep learning tem avançado na identificação de elementos compositivos do ambiente construído. Neste sentido, este estudo busca investigar e testar um procedimento de identificação da configuração e composição da paisagem urbana, por meio da classificação de imagens obtidas pelo GSV. A partir de um banco de imagens de três bairros de Londrina-PR, um modelo de deep learning para classificação de imagens foi proposto. O modelo obteve um bom desempenho, atribuindo corretamente 87,6% das amostras dos respectivos bairros do estudo de caso. Características compositivas foram empiricamente identificadas, considerando a distribuição das amostras no espaço de busca obtido. O modelo proposto contribui na definição de recortes espaciais bem como na mensuração de qualidades ambientais, otimizando coletas de dados, ampliando amostras e conferindo objetividade aos resultados. Esta abordagem contribui na expansão das escalas analíticas da cidade, identificando padrões compositivos e relacionais para o entendimento de elementos influentes no comportamento humano.

scholarly journals Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States

2017 ◽  
Vol 114 (50) ◽  
pp. 13108-13113 ◽  
Author(s):  
Timnit Gebru ◽  
Jonathan Krause ◽  
Yilun Wang ◽  
Duyun Chen ◽  
Jia Deng ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
The United States ◽  
Motor Vehicles ◽  
Google Street View ◽  
Street View ◽  
Fine Spatial Resolution ◽  
Gender Education ◽  
The City ◽  
Us Cities

The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.

scholarly journals Deep mapping gentrification in a large Canadian city using deep learning and Google Street View

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0212814 ◽  
Author(s):  
Lazar Ilic ◽  
M. Sawada ◽  
Amaury Zarzelli
Keyword(s):  
Deep Learning ◽  
Google Street View ◽  
Street View ◽  
Canadian City
Sign in / Sign up

Export Citation Format

Share Document