Convolutional Neural Network leveraging Google Street View API
Robert Brasso
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Abstract— Baltimore has upwards of 16,000 vacant homes and
cause numerous criminal problems for a city including violence,
prostitution, and drug use. Additionally, vacant homes represent
lost taxes for cities, lost intergenerational wealth for families,
drains on public resources, and lost home value. This research
uses Google Street View images of homes in Baltimore to create a
binary classification model using a convolutional neural network.
The key goal of this research is to aid in building a machine
learning model that can classify vacant homes in real time and
provide actionable insights to community service organizations in
the city of Baltimore.
I. INTRODUCTION
In 1950, Baltimore’s population was recorded at just under
a million people and it ranked as the sixth largest city in the
United States. Today, Baltimore’s population is around
620,000 representing a 35% decline in the city’s population
over the last 60 years. One result of this dramatic decrease in
population is that the city contains a significant amount of
vacant homes. According to an article in The Atlantic from
2014, Baltimore has upwards of 16,000 vacant homes and
vacant homes outnumber homeless people 6 to 1 (Semuels,
2018). While the city has tried for years to address this issue,
most policies have fallen short of making a large-scale
difference in the problem.
Vacant homes cause numerous problems for the overall
health of a city. In a study by the Department of Biostatistics
and Epidemiology at the University of Pennsylvania,
researchers identified that despite socioeconomic factors, there
was a significant relationship between aggravated assaults –
particularly involving guns – and the percentage of vacant
homes in census block groups in Philadelphia (Branas, Rubin
and Guo, 2018). Additionally, in 2011, the New York Times
wrote an article referencing the relationship between vacant
homes, prostitution, and drug use in New York neighborhoods
(Wilson, 2018). In combination with the criminal elements
that take rise, vacant homes also represent lost taxes for cities,
lost intergenerational wealth for families, drains on public
resources, and lost home value (Apgar, 2018).
In previous research, I used the 2011 Housing Market
Typology dataset from Baltimore Open Data to determine if a
model could be created to accurately predict the market
classification for a census block neighborhood based on a
variety of housing market metrics such as the percent of
vacant homes in a neighborhood, the amount of commercial
land, average home value, etc. From that research, I concluded
that many classification and clustering algorithms could
classify market typology for census block neighborhoods with
high accuracy, precision, and recall. Additionally, I identified
that the percent of vacant homes was a key metric in
classifying the market category.
The focus of this previous research was to aid in building a
model that could ingest the housing market metrics, classify a
neighborhood, and then provide actionable insights to
community service organizations in the city of Baltimore. For
instance, if a rise in one metric leads to a drop in the market
category for a census block neighborhood and that drop is
identified in real time, perhaps community services can be
redirected to quickly stem the problem in that neighborhood
before it becomes worse.
Many housing market metrics can easily be obtained in real
time. For instance, home sales are quickly and readily reported
on by websites like Zillow and Redfin, so we can easily apply
that information to census block groups and get the average
home sale price for each census block neighborhood. One
metric that provides some problems for real time analysis
however is the percent of vacant homes. Many cities in the
country report on vacant homes, but they require people
checking homes door to door or reporting from the USPS on
homes where mail is not being received. The process of
reporting a vacant home is thus slow and time consuming.
However, as we established earlier, this metric is also one of
the most important in identifying the market category of a
neighborhood.
In this paper, I attempt to create a model that can quickly
identify whether a home is vacant with little manual
intervention. To achieve this, I used the existing list of known
vacant homes from Baltimore Open Data plus I created a list
of known non-vacant homes, fed the addresses from both lists
into the Google Street View API to download images of the
facades of the homes, then used those images to train a
convolutional neural network. If the convolutional neural
network could accurately classify whether a home was vacant
simply by viewing the façade of a home, we could create a
process such as affixing a camera to the top of a car and
streaming the images into a database that checks against the
neural network to determine in real time whether or not a
home is vacant. That would then flag the homes that classified
as vacant, so community services could be dispatched to the
homes. Additionally, the model would be able to determine
which homes were at risk of becoming vacant in the near
future, so these homes could receive the urgent attention they
need.
II. DATASET DESCRIPTION
The vacant buildings dataset came from Baltimore Open
Data and contains a list of all known vacant buildings in
Classifying Vacant Homes from Google Street
Views using a Convolutional Neural Network Robert Brasso
2
Baltimore City, with addresses and coordinates, updated bi-
weekly. Finding a list of non-vacant homes proved to be
considerably more problematic. Many websites offer
mailing lists at a cost, but those lists are not always very
accurate and the cost of getting a list for the entire city can
be very high.
The first idea I had to get a list of non-vacant homes was
to create a web scraper to scrape all of the non-foreclosed
homes for sale on Zillow. After many failed attempts, I
looked at alternative real estate sites. After browsing
Redfin, I found a feature where I could export a list of up to
350 homes to a comma separated values file. I filtered
Redfin to only look at Baltimore City non-foreclosed single
family and townhomes. Redfin gave me results in Baltimore
City that were grouped and scattered across the city as seen
below:
Next, I went to each of those groupings and downloaded up
to 350 homes data to a csv file. I used all of the groupings,
attempting to get a reflective spread across the city. I
combined all of the csv files into one master csv file and used
that to define my non-vacant homes list.
The next step in getting my image dataset involved writing
a program in Python to leverage the Google Street Views API.
The program randomly assigned 70 percent of the data from each list to a training set and the remaining 30 percent to a
testing set. The training and testing lists for both the vacant
and non-vacant homes were cleaned to only have the street
names and numbers. Those lists of names and numbers were
pushed into a program that appended “Balitmore, MD” to the
end of the address and then downloaded the images using the
Google Street View API. The downloads from Google Street
View API created a folder for each image and each folder
contained the image and the metadata (as a json file) for the
image. I wrote a program that combed through the folders and
moved the images into a training and testing directory with
sub directories for vacant and non-vacant homes, so the
folders would only contain the images and not the metadata.
The metadata was discarded.
III. DATA PREPROCESSING
For the first batch of images that I downloaded using the
Google Street View API, I passed in the coordinates hoping
that those would be better at pinpointing the home I was
targeting. After looking at the images, I realized that with the
Google Street View API, if you use coordinates it will find the
street closest to the exact coordinates and give a view of the
coordinates from that street. In most instances, there was no
uniformness of the images. Sometimes the images would be of
the side of the home, other times the back or front.
Since this data was unusable, I proceeded to use the
addresses, which provided me the façade of each home. I ran
these into a very basic convolutional neural network and the
accuracy of the classifier was very low. This prompted me to
take a closer look at the images and I noticed the images from
Google Street View API contained significant issues. The
biggest issues were that most of the images contained trees,
blurred sections of the image, or the viewing angle was
different than that specified in the API settings.
Below is an example of an image that had too much noise
from trees and thus it was very difficult to see the home:
Additionally, below is an example of different image that has
a viewing angle that is not normal:
The majority of images that I downloaded from the Google
Street View API contained issues like this.
The quality of the images I was left with was very strong,
however the image sets ended up quite small since about 85%
of the images were unusable.
Below are sample images that were used in the neural network.
Vacant:
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Non-vacant:
Due to the manual work of creating each image set, I first
started out with smaller image sets and once I confirmed that
the images with better quality resulted in a more accurate
model, I increased the quantity of images in the image sets.
Since the image sets are randomly created, I needed to repeat
the process of checking all of the images every time I
increased the size of the image set.
IV. ARCHITECTURE
Convolutional neural networks, like the one used in this
research require high amounts of CPU cores to process jobs in
a time effective manner. Current quad core CPUs are too
underpowered to provide timely results from convolutional
neural network models. The key to success with convolutional
neural networks is to leverage video cards that have several
thousand CPU cores, such as Nvidia cards with CUDA cores
(Shi et al., 2018).
The research in this paper was completed in early 2018
during a time of high demand for video cards with CUDA
cores. The prices of video cards were currently double their
normal price at this time due to a massive shortage caused by
the rush on crypto currency mining.
The alternative solution I decided upon was to run my code
leveraging an AWS EC2 instance. The design diagram for this project is shown below:
I choose the p2.xlarge instance and used the deep learning
Ubuntu image. The p2.xlarge instance has 12gb of GPU
memory and 2496 parallel processing cores (1 Telsa K80
GPU). Using spot pricing, I was able to get leverage this
system at .42 cents per hour, while it would typically cost
around $1.20 per hour. The major downfall to spot pricing is
that the only way to stop your instance is to terminate it, thus
losing all of the data on your instance.
Once my instance was set up, I connected over SSH and
created directories, installed python packages, and setup
Jupyter Notebooks. I used Secure Copy to copy all of the
images I downloaded locally to my EC2 instance. Next, I
launched my EC2 instance so that local host from my instance
would be passed as my computers local host, so when I
launched Jupyter Notebooks from my instance, it would load
locally on my computer.
V. NEURAL NETWORK MODEL
The model for this project was built using Keras with
Tensorflow handling the backend processing. For all of my
model iterations, I used RELU and Sigmoid for the fully
connected layers. Additionally, I compiled the results using
the Adam optimizer (Kingma and Ba, 2018), RELU activation
(Agarap, 2018), and binary cross-entropy for loss.
For the first version of the model I used two convolutional
layers. The first and second layers used a batch size of 32 with
a 3x3 sliding window and pooling window of 2x2. This
version used 400 steps per epoch, had 2 epochs, and 80
validation steps.
The second version of my model used a batch size of 4 with
a 11x11 sliding window and 3x3 pooling window for the first
layer, a batch size of 16 with a 6x6 sliding window and 3x3
pooling window for the second layer, and a batch size of 32
with a 3x3 sliding window and 2x2 pooling window for the
third layer. I kept the steps per epoch at 400, used 5 epochs,
and 100 validation steps. The final version of my model used a batch size of 4 with a
12x12 sliding window and 3x3 pooling window for the first
layer, a batch size of 8 with a 9x9 sliding window and 3x3
pooling window for the second layer, and a batch size of 32
with a 6x6 sliding window and a 3x3 pooling window for the
third layer.
Below is a diagram of the final iteration of the network used
for my research:
VI. RESULTS
The results from my first model yielded very poor results.
After further consideration, it was apparent that the sliding
window I was using was much too small and could not pick
out the subtle details that are unique to the vacant homes.
After the first two model runs, I filtered through the pictures
and noticed some minor gains with the accuracy getting up to
52.68%. After that, I ran the model again with new images
with the second model. The initial results of this model were
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better, and it achieved an accuracy of 56.38%. After parsing
out the noisy images, the accuracy increased to 65.15% with a
validation accuracy of 70.45%. In my final model run, I used
another brand-new image set, but parsed through the images
very strictly to remove unusable images. Combined with the
parameters of the new model, the results for this run had an
accuracy of 67.60% and a validation accuracy of 73.68%. The
table below shows the results for each of the model runs and
each epoch.
Model Run Epoch Accuracy
Validation Accuracy FOV
1 1 0.4991 0.5008 40
1 2 0.5003 0.4992 40
2 1 0.4985 0.4993 40 2 2 0.5026 0.4997 40
3 1 0.5268 0.5007 60
3 2 0.5227 0.4976 60
4 1 0.5626 0.6415 80 4 2 0.5616 0.6396 80
4 3 0.5638 0.6428 80
4 4 0.5598 0.6412 80 4 5 0.5634 0.6441 80
5 1 0.6496 0.7048 80 5 2 0.646 0.7035 80
5 3 0.649 0.7065 80
5 4 0.6515 0.7045 80 5 5 0.6513 0.7052 80
6 1 0.6742 0.7362 90 6 2 0.676 0.7343 90
6 3 0.6742 0.7349 90
6 4 0.6746 0.7368 90 6 5 0.6754 0.7357 90
Yellow = Model 1, Green = Model 2, Blue = Model 3
VII. CONCLUSION
This research provides some evidence that using a
convolutional neural network to classify vacant homes may be
a viable option, however there are serious caveats.
First, it appears that image quality plays a huge part in the
results of the neural network. The general image quality using
google street views was very poor. My best results required
me to manually comb through every image and in total
remove about 85% of the total image set. This was a major
hurdle to overcome because it required manual effort and
judgement, making it was difficult to get a large enough
sample size to effectively train a model. The result was often
underfitting the model, which resulted in the training accuracy
being less than the validation accuracy. Also, the major hope
from this research was to make a model that could classify
images of homes in real time, but since trees and other objects
would be in the image of the façade of the home, it is difficult
to imagine a practical application of this technology.
The second major problem is that by manually parsing out
the images in the image set, I am introducing bias. For
instance, I removed images where a large portion of the image
contained a tree, but perhaps the existence of trees in the
image is an important feature in the classification of these
homes. Even though the accuracy of the model increased, it is
impossible to say if I did not subconsciously remove images in
another pattern.
The final problem is that due to the surge in GPU prices,
training a neural network like this can be costly. While spot
pricing on AWS made it possible to do this at a reasonable
cost, if this model was to be used in a production environment
the cost of computation may be greatly higher.
With all of those caveats, if future research was able to
leverage a platform that produces better image quality for the
facades of homes, this model for classifying vacant homes
would be valid. For instance, if someone went door to door
with a high definition camera taking pictures of the facades of
homes and those were the images used for the model, I feel
strongly that this model could produce strong results.
Additionally, if a better image source was available and the model produced better results, you could potentially feed
images from that source of all addresses within a city and
create a reasonably accurate database of all of the homes
within a city. This would be a valuable resource for cities and
towns that have formerly been hit by the loss of industry and
thus have a high percentage of vacant homes. Future research
on this topic should experiment with different types of
activation and optimization algorithms to see if any additional
efficiencies can be gained.
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REFERENCES
SEMUELS, A. (2018). COULD BALTIMORE'S 16,000 VACANT HOUSES SHELTER
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BRANAS, C., RUBIN, D. AND GUO, W. (2018). VACANT PROPERTIES AND
VIOLENCE IN NEIGHBORHOODS.
WILSON, M. (2018). FORECLOSURES EMPTY HOMES, AND CRIMINALS FILL
THEM UP. [ONLINE] NYTIMES.COM. AVAILABLE AT:
APGAR, W. (2018). THE MUNICIPAL COST OF FORECLOSURES: A CHICAGO
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AGARAP, A. (2018). DEEP LEARNING USING RECTIFIED LINEAR UNITS
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THE-ART DEEP LEARNING SOFTWARE TOOLS. [ONLINE] AVAILABLE AT:
KRIZHEVSKY, A., SUTSKEVER, I. AND HINTON, G. (2018). IMAGENET
CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS. [ONLINE]
GOODFELLOW, I., BULATOV, Y., IBARZ, J., ARNOUD, S. AND SHET, V. (2018).
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DEEP CONVOLUTIONAL NEURAL NETWORKS. [ONLINE] ARXIV.ORG.
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Posted Aug 24, 2024
Fed images of vacant homes from Google Street View API into a Convolutional Neural Network in order to build a model that could accurately classify vacant homes
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