To
evaluate the financial health of a company, four main factors can be
examined: liquidity, solvency, profitability and operating efficiency.
Out of these 4 metrics, profitability is the best measurement of an
institution's financial health. To survive in the long run, a company
must attain and maintain profitability.
One
of the key indicators of increasing profits is the number of active
customers. For e.g., a brick and mortar store such as McDonald's would
have high profitability and increased net margin (assuming operating
costs remain the same) if more customers visit the store and buy its
product. This is true for any customer-facing store such as Walmart,
Decathalon, Starbucks, etc.
Predicting
the number of customers for such stores could help us forecast the
growth of the company and make better investment decisions.
Now there are many ways to predict the number of customers for a store:
- Manual survey of the number of people anytime inside the store
- Counting people remotely using in-store CCTV video stream
or doing what Sam Walton, the founder of Walmart, did:
It
is rumored that Walton would count the number of cars outside the
Walmart stores using aerial images taken by a plane flying over his
stores to predict how well his stores were doing. Now, this could be
real or fiction, but what is real is that in 2010 Neil
Currie, an analyst at UBS, analyzed the satellite images of Walmart’s
parking lot and suggested that its stocks are undervalued. Given
this was 2010 and the resolution of satellite imagery wasn’t as good as
it is now and neither did we have powerful computing, both from hardware
and software perspective, Currie would have had to manually count the
number of cars in the parking lot.
With
the advances in satellite imagery and software methods, especially AI,
there is now a way to automatically count the number of cars in a
parking lot to predict how well the company is doing.
The motivation for this article
came from this research paper from Berkeley Haas which describes the
negative effects of using such technology to predict the financial
health of a company that can influence the stock price.
…researchers confirmed that year-over-year changes in the number of cars in individual stores’ parking lots is a reliable predictor of quarterly sales — a widely used metric for retailers’ performance. — Berkeley Haas Assoc. Prof. Panos Patatoukas in his working paper
This
article will briefly describe the resources available to the general
public to experiment in this domain. The reader can take inspiration
from this article and use it as a starting point to develop such a
system or pivot to other use-cases that can be solved using this
technology.
Data
Cars Overhead with Context Dataset (COWC)
This
dataset consists of ~33,000 unique cars from six different regions.
There are both grayscale and RGB datasets available. Data is collected
via aerial platforms, but at a nadir view
angle such that it resembles satellite imagery. The imagery has a
resolution of 15 cm GSD that is approximately twice as good as the
current best resolution of commercial satellite imagery (30 cm GSD for
DigitalGlobe). In order to make the COWC dataset resemble satellite
images at 30cms GSD, you can downsample the images by a factor of 0.5 to
match the current state-of-the-art satellite image resolution.
Now
you have a dataset available where the location of each car is marked
as a non-zero pixel. Assuming the mean car size of 3M and creating a
bounding box around the non-zero pixel, you can generate training data
that is ready to be ingested into the machine learning model described
below.
Dataset for Object Detection in Aerial images (DOTA)
This
dataset is collected from multiple sensors and platforms (e.g. Google
Earth) with multiple resolutions, with a total of 2806 images and
188,282 annotated instances of the following classes: plane,
ship, storage tank, baseball diamond, tennis court, swimming pool,
ground track field, harbor, bridge, large vehicle, small vehicle,
helicopter, roundabout, soccer ball field and basketball court. An
added advantage of this dataset is that it splits up the vehicle class
into two separate classes: large and small, leading to a more granular
classification system.
Vehicle Detection in Aerial Imagery (VEDAI)
This
is a benchmark dataset used for evaluating the model developed for
counting the target object. It contains 1210 images with the following
annotated classes: plane, boat, camping car, car, pick-up, tractor, truck, van, and other. It also provides an evaluation protocol to benchmark the developed model.
Overhead Imagery Research DataSet (OIRDS)
This dataset contains 900 annotated images. It contains five classes of vehicles: truck, pick up, car, van and unknown.
There are other open-source satellite imagery datasets available as well which could be used for this analysis but would require the manual labeling effort.
Algorithms
Object Detection
There
are open-source algorithms available that have achieved reasonably good
performance in the field of object localization such as RCNN and YOLO
family of models. However, both of these family of models fail to
identify really small objects in very large images such as cars in
satellite images.
Luckily for us, there is an object detection model available that can alleviate this problem — YOLT2.
YOLT2 is specifically built for identifying small objects in aerial
imagery and has shown good performance in use-cases such as localization of buildings, boats, and aircraft in satellite images.
Another
algorithm that has shown promise in this area in the Single Shot
Detector (SSD). It is a uniform one-stage model that utilizes the
feature maps at different scales. This algorithm and modifications of it have yielded successful results in recognizing really small objects in very large images
Semantic Segmentation
Identifying
individual objects close to each other can be a difficult task for
object localization models and can lead to poor performance especially
when the end goal is to perform object counting. To alleviate this
challenge, semantic segmentation
is a method that helps in recognizing and understanding what is in the
image at a pixel level. Variety of non-AI and AI-based methods are
present to achieve this, but one of the more popular AI-based models to
achieve this is U-Net,
that has shown successful results in the biomedical image segmentation
field. Modifying and training such a model for counting cars could lead
to an increase in performance compared to solely using object detection
methods as described above.
Existing solutions in the market
With
the availability of high-resolution aerial imagery, computing power,
and open-source AI algorithms, companies are capitalizing on this
opportunity to provide advanced analytics solutions that can predict the
financial health of the institution and many similar use-cases. Some of
the companies are mentioned below:
2. RS Metrics
3. Picterra
The potential of Satellite Imagery is promising
The use of satellite imagery for addressing use-cases such as above is heating up. Agriculture departments all over the world are using such images to predict crop yield and analyze animal health. The extent of natural disasters is being studied by analyzing satellite imagery. National security
use-cases such as counting cars at the border and analyzing weapon
stockpiles in terrorist invested regions could benefit from such
technology.
With
advances in AI algorithms (and being open-source) and computing
infrastructure (read: cloud), the development of the above use-cases is
within the reach of the general public. This article intends to
introduce the topic of combining satellite images with advanced AI
methods for one such use-case and serves as a starting point for the
development of many such applications.
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