ZEMOSO ENGINEERING STUDIO
August 10, 2022
6 min read

Drone Technology: The upswing of its usage and value realization in EnergyTech

The hype around drones, their prowess and possibilities, their supernatural abilities started becoming the rage a decade ago. And then reality set in. 

Governments and regulatory bodies are still trying to figure out how much they want to regulate commercial drones. Skilled professionals to operate drones are still hard to find. The world is still waiting for other technologies such as IoT, sensor technology, telemetry data processing speeds, computer vision, and such to reach a certain level of sophistication. 

And so on and so forth! 

Yet, we have now helped two Fortune 500 companies productize and find phenomenal use for drone technology. From Wizard of Oz testing to validate data to building the software architecture that delivers true benefits — we’ve done the gamut. 

For an older HealthTech initiative, we enabled one of the earliest successful human organ deliveries by an unmanned aerial vehicle (UAV). The other is the true hero of this blog post: drones and their application in the oil and gas industry.  

The global drone surveillance market for energy and power is projected to grow at a CAGR 18.9%, to $476.5 million during 2021- 2028. And we couldn’t be more excited about that prospect. 

Monitoring, preventing, and managing emissions have become priority number one for governments and large corporations everywhere. One Fortune 200 company in the space worked with Zemoso to build and productize an aerial system that helped detect and prevent leaks at remote plants: oil wells, subsea stations, oil rigs, and more. 

So, how does aerial drone-based monitoring work: data across different wireless sensors and drones are collated, processed, and analyzed in real time to identify if there is a leak, what is the rate of that leak, and notify the stakeholders to act as needed. 

Zemoso’s scrum teams worked as a part of a leading conglomerate’s internal team to help develop the software product that would ultimately take advantage of all the data streaming in from drones and sensors, empower decision makers to take immediate action and bring about real reductions in emissions at remote energy plants. 

How did we do it?

We aligned with our stakeholders on the minimum feature set and created the blueprint for this complex product. After that, we ran weekly sprints and scrums to design, build, and deploy the product within eight months. 

Engineering

With regulations and traffic not being a major point of concern in remote oil and gas production plants, we set off to deal with the other engineering challenges that would determine the success and failure of this fantastic product we were co-building with our enterprise customer. 

On the engineering front, we stuck to all the agile best practices: using microservices architecture, service choreography, GraphQL layer for faster deployments, CI/CD, automated testing, containerization, and so on. The two main challenges we focused on were: 

   As a diverse set of data points flew in from different points and sources, how do we ensure that the solution we built can draw learnings and marry them to deliver actionable insights to the users?

   How do we make the system dynamic enough to handle volume without allowing for lags and delays?

Firstly, to expedite deployment, we decided to use a third-party API to gather and transmit geographic information, making it easier for operators to act. This helped us gain access to superior geolocation tech without slowing down the actual build timeline. 

In addition to automating testing in general, we used Selenium to ensure accuracy and speed. We used Postman to automate API testing at scale. 

We used Cloudfront content delivery network (CDN) to deliver data, and videos fast, with low latency. We also used AWS Network Load Balancer to prepare for sudden shifts in data traffic; this ensured that the solution could handle millions of requests per second. 

We used PostgreSQL with Redis for cache, ensuring sub-millisecond response times, enabling millions of requests per second. Amazon S3 was used to store generated reports and images, making the product even more robust in handling data in multiple formats. 

We used Python, MongoDB, and PyMongo for analytics, and Pytorch for computer vision apps to process the visual data (images and videos) coming in from the drone.  

Images and videos collected via drones, processed using AI and computer vision helped generate the right notifications. These images and videos are ingested, sorted, and analyzed to generate insights in real time. Region-based Convolutional Neural Networks (R-CNN) helped detect objects in any image. 

P.S. Since we work on early-stage products, many of them in stealth mode, we have strict Non-disclosure agreements (NDAs). The data, insights, and capabilities discussed in this blog have been anonymized to protect our client’s identity and don’t include any proprietary information. 

ZEMOSO ENGINEERING STUDIO

Drone Technology: The upswing of its usage and value realization in EnergyTech

August 10, 2022
6 min read

The hype around drones, their prowess and possibilities, their supernatural abilities started becoming the rage a decade ago. And then reality set in. 

Governments and regulatory bodies are still trying to figure out how much they want to regulate commercial drones. Skilled professionals to operate drones are still hard to find. The world is still waiting for other technologies such as IoT, sensor technology, telemetry data processing speeds, computer vision, and such to reach a certain level of sophistication. 

And so on and so forth! 

Yet, we have now helped two Fortune 500 companies productize and find phenomenal use for drone technology. From Wizard of Oz testing to validate data to building the software architecture that delivers true benefits — we’ve done the gamut. 

For an older HealthTech initiative, we enabled one of the earliest successful human organ deliveries by an unmanned aerial vehicle (UAV). The other is the true hero of this blog post: drones and their application in the oil and gas industry.  

The global drone surveillance market for energy and power is projected to grow at a CAGR 18.9%, to $476.5 million during 2021- 2028. And we couldn’t be more excited about that prospect. 

Monitoring, preventing, and managing emissions have become priority number one for governments and large corporations everywhere. One Fortune 200 company in the space worked with Zemoso to build and productize an aerial system that helped detect and prevent leaks at remote plants: oil wells, subsea stations, oil rigs, and more. 

So, how does aerial drone-based monitoring work: data across different wireless sensors and drones are collated, processed, and analyzed in real time to identify if there is a leak, what is the rate of that leak, and notify the stakeholders to act as needed. 

Zemoso’s scrum teams worked as a part of a leading conglomerate’s internal team to help develop the software product that would ultimately take advantage of all the data streaming in from drones and sensors, empower decision makers to take immediate action and bring about real reductions in emissions at remote energy plants. 

How did we do it?

We aligned with our stakeholders on the minimum feature set and created the blueprint for this complex product. After that, we ran weekly sprints and scrums to design, build, and deploy the product within eight months. 

Engineering

With regulations and traffic not being a major point of concern in remote oil and gas production plants, we set off to deal with the other engineering challenges that would determine the success and failure of this fantastic product we were co-building with our enterprise customer. 

On the engineering front, we stuck to all the agile best practices: using microservices architecture, service choreography, GraphQL layer for faster deployments, CI/CD, automated testing, containerization, and so on. The two main challenges we focused on were: 

   As a diverse set of data points flew in from different points and sources, how do we ensure that the solution we built can draw learnings and marry them to deliver actionable insights to the users?

   How do we make the system dynamic enough to handle volume without allowing for lags and delays?

Firstly, to expedite deployment, we decided to use a third-party API to gather and transmit geographic information, making it easier for operators to act. This helped us gain access to superior geolocation tech without slowing down the actual build timeline. 

In addition to automating testing in general, we used Selenium to ensure accuracy and speed. We used Postman to automate API testing at scale. 

We used Cloudfront content delivery network (CDN) to deliver data, and videos fast, with low latency. We also used AWS Network Load Balancer to prepare for sudden shifts in data traffic; this ensured that the solution could handle millions of requests per second. 

We used PostgreSQL with Redis for cache, ensuring sub-millisecond response times, enabling millions of requests per second. Amazon S3 was used to store generated reports and images, making the product even more robust in handling data in multiple formats. 

We used Python, MongoDB, and PyMongo for analytics, and Pytorch for computer vision apps to process the visual data (images and videos) coming in from the drone.  

Images and videos collected via drones, processed using AI and computer vision helped generate the right notifications. These images and videos are ingested, sorted, and analyzed to generate insights in real time. Region-based Convolutional Neural Networks (R-CNN) helped detect objects in any image. 

P.S. Since we work on early-stage products, many of them in stealth mode, we have strict Non-disclosure agreements (NDAs). The data, insights, and capabilities discussed in this blog have been anonymized to protect our client’s identity and don’t include any proprietary information. 

Recent Publications
Zemoso’s next big move: Entering Europe with new offices open in London
Zemoso’s next big move: Entering Europe with new offices open in London
August 29, 2022
Beyond Methodologies: 5 engineering do’s for an agile product
Beyond Methodologies: 5 engineering do’s for an agile product
August 28, 2022
Docs, Guides, Resources: Getting developer microsites right in a product-led world
Docs, Guides, Resources: Getting developer microsites right in a product-led world
August 16, 2022
Winning first place at O'Reilly Media’s Architectural Katas — Spring 2022
Winning first place at O'Reilly Media’s Architectural Katas — Spring 2022
July 13, 2022
How to remix Amazon’s Working Backwards with Google’s Venture’s User Journey: The Dr. Strange Way
How to remix Amazon’s Working Backwards with Google’s Venture’s User Journey: The Dr. Strange Way
June 14, 2022
How we built a big data platform for a futuristic AgriTech product
How we built a big data platform for a futuristic AgriTech product
June 3, 2022
Zemoso Labs starts operations in Waterloo, Canada
Zemoso Labs starts operations in Waterloo, Canada
May 25, 2022
Deconstructing Elon Musk’s dog ate my homework answer for Twitter: More validation will be asked of startups
Deconstructing Elon Musk’s dog ate my homework answer for Twitter: More validation will be asked of startups
May 20, 2022
Zemoso’s next big move: Entering Europe with new offices open in London
Zemoso’s next big move: Entering Europe with new offices open in London
August 29, 2022
Beyond Methodologies: 5 engineering do’s for an agile product
Beyond Methodologies: 5 engineering do’s for an agile product
August 28, 2022
Docs, Guides, Resources: Getting developer microsites right in a product-led world
Docs, Guides, Resources: Getting developer microsites right in a product-led world
August 16, 2022
Winning first place at O'Reilly Media’s Architectural Katas — Spring 2022
Winning first place at O'Reilly Media’s Architectural Katas — Spring 2022
July 13, 2022
How to remix Amazon’s Working Backwards with Google’s Venture’s User Journey: The Dr. Strange Way
How to remix Amazon’s Working Backwards with Google’s Venture’s User Journey: The Dr. Strange Way
June 14, 2022
ZEMOSO ENGINEERING STUDIO
August 10, 2022
6 min read

Drone Technology: The upswing of its usage and value realization in EnergyTech

The hype around drones, their prowess and possibilities, their supernatural abilities started becoming the rage a decade ago. And then reality set in. 

Governments and regulatory bodies are still trying to figure out how much they want to regulate commercial drones. Skilled professionals to operate drones are still hard to find. The world is still waiting for other technologies such as IoT, sensor technology, telemetry data processing speeds, computer vision, and such to reach a certain level of sophistication. 

And so on and so forth! 

Yet, we have now helped two Fortune 500 companies productize and find phenomenal use for drone technology. From Wizard of Oz testing to validate data to building the software architecture that delivers true benefits — we’ve done the gamut. 

For an older HealthTech initiative, we enabled one of the earliest successful human organ deliveries by an unmanned aerial vehicle (UAV). The other is the true hero of this blog post: drones and their application in the oil and gas industry.  

The global drone surveillance market for energy and power is projected to grow at a CAGR 18.9%, to $476.5 million during 2021- 2028. And we couldn’t be more excited about that prospect. 

Monitoring, preventing, and managing emissions have become priority number one for governments and large corporations everywhere. One Fortune 200 company in the space worked with Zemoso to build and productize an aerial system that helped detect and prevent leaks at remote plants: oil wells, subsea stations, oil rigs, and more. 

So, how does aerial drone-based monitoring work: data across different wireless sensors and drones are collated, processed, and analyzed in real time to identify if there is a leak, what is the rate of that leak, and notify the stakeholders to act as needed. 

Zemoso’s scrum teams worked as a part of a leading conglomerate’s internal team to help develop the software product that would ultimately take advantage of all the data streaming in from drones and sensors, empower decision makers to take immediate action and bring about real reductions in emissions at remote energy plants. 

How did we do it?

We aligned with our stakeholders on the minimum feature set and created the blueprint for this complex product. After that, we ran weekly sprints and scrums to design, build, and deploy the product within eight months. 

Engineering

With regulations and traffic not being a major point of concern in remote oil and gas production plants, we set off to deal with the other engineering challenges that would determine the success and failure of this fantastic product we were co-building with our enterprise customer. 

On the engineering front, we stuck to all the agile best practices: using microservices architecture, service choreography, GraphQL layer for faster deployments, CI/CD, automated testing, containerization, and so on. The two main challenges we focused on were: 

   As a diverse set of data points flew in from different points and sources, how do we ensure that the solution we built can draw learnings and marry them to deliver actionable insights to the users?

   How do we make the system dynamic enough to handle volume without allowing for lags and delays?

Firstly, to expedite deployment, we decided to use a third-party API to gather and transmit geographic information, making it easier for operators to act. This helped us gain access to superior geolocation tech without slowing down the actual build timeline. 

In addition to automating testing in general, we used Selenium to ensure accuracy and speed. We used Postman to automate API testing at scale. 

We used Cloudfront content delivery network (CDN) to deliver data, and videos fast, with low latency. We also used AWS Network Load Balancer to prepare for sudden shifts in data traffic; this ensured that the solution could handle millions of requests per second. 

We used PostgreSQL with Redis for cache, ensuring sub-millisecond response times, enabling millions of requests per second. Amazon S3 was used to store generated reports and images, making the product even more robust in handling data in multiple formats. 

We used Python, MongoDB, and PyMongo for analytics, and Pytorch for computer vision apps to process the visual data (images and videos) coming in from the drone.  

Images and videos collected via drones, processed using AI and computer vision helped generate the right notifications. These images and videos are ingested, sorted, and analyzed to generate insights in real time. Region-based Convolutional Neural Networks (R-CNN) helped detect objects in any image. 

P.S. Since we work on early-stage products, many of them in stealth mode, we have strict Non-disclosure agreements (NDAs). The data, insights, and capabilities discussed in this blog have been anonymized to protect our client’s identity and don’t include any proprietary information. 

Recent Publications

ZEMOSO ENGINEERING STUDIO

Beyond Methodologies: 5 engineering do’s for an agile product

August 28, 2022
6 min read
ZEMOSO ENGINEERING STUDIO

How we built a big data platform for a futuristic AgriTech product

June 3, 2022
8 min read
ZEMOSO NEWS

Zemoso Labs starts operations in Waterloo, Canada

May 25, 2022
5 min read
ZEMOSO ENGINEERING STUDIO

Honorable mention at O’Reilly’s Architectural Katas event

May 17, 2021
5 min read
ZEMOSO ENGINEERING STUDIO

Product dev with testable spring boot applications, from day one

May 4, 2021
5 min read
ZEMOSO ENGINEERING STUDIO

When not to @Autowire in Spring or Spring Boot applications

May 1, 2021
5 min read
ZEMOSO ENGINEERING STUDIO

Efficiently handle data and integrations in Spring Boot

January 24, 2021
5 min read
ZEMOSO ENGINEERING STUDIO

Our favorite CI/CD DevOps Practice: Simplify with GitHub Actions

October 25, 2020
5 min read
ZEMOSO ENGINEERING STUDIO

How to use BERT and DNN to build smarter NLP algorithms for products

February 14, 2020
12 min read
ZEMOSO ENGINEERING STUDIO

GraphQL with Java Spring Boot and Apollo Angular for Agile platforms

April 30, 2019
9 min read
ZEMOSO ENGINEERING STUDIO

GraphQL — Why is it essential for agile product development?

April 30, 2019
12 min read
ZEMOSO ENGINEERING STUDIO

Deploying Airflow on Kubernetes 

November 30, 2018
2 min read
ZEMOSO PRODUCT STUDIO

How to validate your Innovation: Mastering Experiment Design

November 22, 2018
8 min read
ZEMOSO PRODUCT STUDIO

Working Backwards: Amazon's Culture of Innovation: My notes

November 19, 2018
8 min read
ZEMOSO ENGINEERING STUDIO

Product developer POV: Caveats when building with Spark

November 5, 2018
2 min read

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September 19, 2022