20tree_ai vegetation management
20tree.ai ingests large volumes of satellite imagery and data, analyzing it through AI techniques like computer vision, and then breaks down the information into operative insights for their customers.
Credit: 20tree.ai

Creating a digital twin of the Earth with computer vision

20tree.ai is on a mission to help protect the Earth’s forests by providing data-driven and actionable planet intelligence.

As climate change, in tandem with accelerating population growth, becomes an increasingly critical issue, the marriage of environmentalism and data-driven intelligence has never been more necessary. This is the intersection where 20tree.ai exists, with a declared mission to deliver actionable planet intelligence.

Their work in short: creating snapshots of the Earth in real-time. In essence, a digital twin of the Earth to better understand its natural resources, specifically its forests and how humans can be more responsible to them. They are accomplishing this by ingesting large volumes of satellite imagery and data, analyzing it through AI techniques like computer vision, and then breaking down this information into operative insights for their customers in a digestible and actionable manner.

Roelof Pieters
Roelof Pieters, 20tree.ai co-founder and chief technology officer.
CREDIT: 20tree.ai

“Trees are still the best technology we have to sequester carbon,” Roelof Pieters, 20tree.ai co-founder and chief technology officer explains. “They’re 1,000-times more cost effective right now than any kind of technical solution.”

20tree.ai’s data challenges

Like many startups, 20tree.ai faced a number of data questions from the get-go.

How do they deal with massive amounts of data pouring in? Where do they store it? How do they make it accessible to themselves and their clients? How do they accomplish all of this in a cost-effective way? And these questions were further complicated by the type of data they work with — high-resolution satellite imagery and radar data.

While first operating off their own hardware, 20tree.ai eventually moved into Amazon’s startup program to help build up their infrastructure. Amazon S3, an object storage service, allows them to serve encrypted insights to their clients, providing them with a simple solution to replicate data across different regions.

Trees are still the best technology we have to sequester carbon. They’re 1,000-times more cost effective right now than any kind of technical solution.
Roelof Pieters, 20tree.ai co-founder and chief technology officer

And when it comes to heavy lifting for AI, Amazon EFS (Elastic File Storage), a cloud storage service, has proven to be a life saver for them, providing what Pieters described as like having “a hard disk of unlimited size.”

Applying computer vision

Looking a layer deeper, because forests and atmospheric conditions are always changing, the kind of data that is pulled is, from a computer vision perspective, very noisy. In order to accommodate these changes, 20tree.ai needs flexibility in its infrastructure and data pipeline.

While satellite imagery allows them to pull images rapidly on a massive scale, the maximum resolution can only provide so much information accurately. It’s one thing to be able to pull insights from data, but it’s even more important to be able to understand that you are pulling the right insights from your data, and computer vision makes this possible without having boots on the ground.

For image classification, 20tree.ai relies on deep learning algorithms. By applying a convolutional neural network to satellite images, they can figure out an image’s shape and exact location, and determine things like if an image is cloudy or not. 20tree.ai finds image segmentation networks are helpful for land cover classification, because they can partition shapes and objects, like trees, roads and buildings.

20tree_ai satellite imagery input
For image classification, 20tree.ai relies on deep learning algorithms. By applying a convolutional neural network to satellite images, they can figure out an image’s shape and exact location, and determine things like if an image is cloudy or not.
CREDIT: 20tree.ai

In order to enable the custom builds of their AI algorithms for satellite imagery, 20tree.ai employs Amazon SageMaker, a fully-managed machine learning service, to help put their various custom trained models into production and easily deploy them in a scalable way. As Pieters notes, using SageMaker means they don't have to worry about the specifics of where and how their models are exposed as scalable rest points internally for their products, as well as externally.

Because of this, 20tree.ai can train its algorithms with satellite images from different locations and points in time, to predict high risk areas and detect deforestation. With most companies in their field still relying solely on traditional AI techniques, 20tree.ai’s application of the latest advancements in computer vision provides them with a significant competitive advantage.

Actionable intelligence for the planet

Partnering primarily with large enterprises, 20tree.ai’s work touches on a variety of initiatives — helping denote deforestation patterns, improving productivity on farms, monitoring urban green spaces, and catching risks to forests like insect plagues or drought.

One major focus of their work has centered around managing the interaction between vegetation and power lines, with several large electric utilities companies across the U.S. and Europe as clients. In areas like California that have seen consistent issues with forest fires, tree interaction with power lines are one of the leading causes of these instances.

20tree_ai_vegetation_management
20tree.ai helps electric utilities identify and mitigate risk by monitoring vegetation around power lines.
CREDIT: 20tree.ai

Utilities companies have been spending lots of money to try to prevent this from continuing, but the tools they’ve had to this point — drones, helicopters, people on the ground — have been inefficient relative to the size of lands they are attempting to manage. It can take them years to get a snapshot of their powerline infrastructure, and by the time they do that information is often outdated.

20Tree.ai’s use of satellite imagery makes it possible to conduct this work every single day, and while it can’t provide the same level of image resolution as drones or helicopters, computer vision makes it possible to provide a complementary level of insights needed to make critical decisions. They can see exactly what types of trees are standing where, revealing the current heights of trees near powerlines, whether certain trees are more or less of a hazard because of the pace at which they grow, where there are protected species of trees, any storm damage that might have occurred, and more.

With these insights in hand, utilities companies are able to identify the hotspots that need their attention, reducing surveillance costs by up to 50%, according to Pieters. Down the line, proactive planning like risk-based tree trimming cycles and verified vegetation maintenance (being able to check up and prove that this work is being done) becomes a lot easier, ultimately making risk reduction more quantifiable.


US, CA, Santa Clara
Job summaryAre you passionate about the intersection of IoT and Computer Vision? Are you looking to impact businesses around the globe through cutting edge machine learning systems and intelligent edge devices? AWS Panorama is a machine learning (ML) appliance that brings CV to on-premises internet protocol (IP) cameras.The AWS Panorama team is looking for a Principal Data Scientist to apply their passion and experience in computer vision and manchine learning to build and deploy models and applications on AWS Panorama across industries. You will partner with Engineering, Product Management, Solution Architects, Sales, and Business Development to enable customers to solve their business problems with computer vision models. You will develop white papers, blogs, reference implementations, labs, and presentations to evangelize AWS Panorama design patterns and best practices.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.Key job responsibilities• Understand the customer’s business need and guide them to a solution• Assist customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.• Use Deep Learning frameworks like PyTorch, Tensorflow and MxNet to help our customers build DL models.• Work with our Professional Services DevOps consultants to help our customers operationalize models after they are built.• Assist customers with identifying model drift and retraining models.• Research and implement novel ML and DL approaches.• This position can have periods of up to 30-40% travel.• You may work out of any of the following cities: Southern California (i.e. south of San Diego to north of Los Angeles), Bay Area California, or Seattle.
CA, ON, Toronto
Job summaryAmazon Sponsored Ads is one of the fastest growing business domains and we are looking for talented scientists to join this team of incredible scientists to contribute to this growth. We are still in Day 1 and there is an abundance of opportunities that are yet to be explored. We are a team of highly motivated and collaborative team of machine learning scientists, with an entrepreneurial spirit and bias for action. We have a broad mandate to experiment and innovate, and we are growing at an unprecedented rate with a seemingly endless range of new opportunities.Key job responsibilities· Build machine learning models and utilize data analysis to deliver scalable solutions to business problems.· Perform hands-on analysis and modeling with very large data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience.· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production.· Design and run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders.· Work with scientists and economists to model the interaction between organic sales and sponsored content and to further evolve Amazon's business.· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.· Research new predictive learning approaches for the sponsored products business.· Write production code to bring models into production.
US, WA, Bellevue
Job summaryAmazon’s Modeling and Optimization Team (MOP) is looking for a passionate individual with strong optimization and analytical skills to join us in the endeavor of designing and planning the most complex transportation and fulfillment network in the world.The team is responsible for optimizing the global transportation and fulfillment network for Amazon.com and ensuring that the company is able to deliver to our customers as quickly, accurately, and cost effectively as possible. We design the network that delivers packages from fulfillment centers (FCs) to end customers, through both Amazon’s internal network as well as external partners, using ground and air methods. Optimizing these package flows over time requires optimizing the timing of operations such as truck departure and sortation schedule. The scale of Amazon's transportation network and the added complexity of the time-expanded nature of this problem makes it very challenging and exciting. It is a terrific opportunity to have a direct impact in the business while pushing the boundaries of science.Key job responsibilitiesWe are seeking an experienced scientist who has solid background in Operations Research, Operations Management, Applied Mathematics, or other similar domain.In this role, you will develop models and solution algorithms that are innovative and scalable to solve new challenges in Amazon's global fulfillment network. You will collaborate with other scientists across teams to create integrated solutions that improves fulfillment speed, cost, and carbon emission. You have deep understanding of business challenges and provide scientific analysis to support business decision using a range of methodologies. You will also work with engineering teams to identify new data requirements, deploy new models or simplifying existing processes.About the teamhttps://www.aboutamazon.com/news/innovation-at-amazon/how-artificial-intelligence-helps-amazon-deliver
US, WA, Seattle
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US, WA, Seattle
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US, Virtual
Job summaryJob summaryMillions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem, the International Seller Services org (ISS) plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon.In Jeff’s own words (2019 Q1 shareholders letter): “Something strange and remarkable has happened over the last 20 years. Take a look at these numbers:1999 3% …… 2018 58%. The % represent the share of physical gross merchandise sales sold on Amazon by independent third-party sellers – mostly small- and medium-sized businesses – as opposed to Amazon retail's own first party sales. Third-party sales have grown from 3% of the total to 58%. To put it bluntly: Third-party sellers are kicking our first party butt. Badly.…. Why were independent sellers able to grow so much faster than Amazon's own highly organized first-party sales organization? There isn't one answer, but we do know one extremely important part of the answer: We helped independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. There are many such tools, including tools that help sellers manage inventory, process payments, track shipments, create reports, and sell across borders – and we're inventing more every year.”We are seeking a seasoned Data scientist with excellent hands-on experience in statistical modeling, machine learning and analytical abilities as well as data engineering skills. You are a self-starter, someone who thrives in a fast-paced and ever-changing environment, with an uncanny knack and passion for building data driven machine learning product and turning qualitative analysis and observations into diagnostics and metrics? Then you are the right candidate for our team.In this position, you will be a key contributor and sparring partner, building personalized recommendation engine for Amazon global selling partners, developing analytics and insights that global executive management teams and business leaders will use to define global strategies and deep dive businesses. You will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. You will also have the opportunity to display your skills in the following areas:Major responsibilitiesUse statistical, econometric and machine learning techniques to develop recommendation models and work with software engineering team and product managers to integrate with products.Perform A/B test to assess the performance of machine learning products and feature improvementAnalyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes.Design, development and evaluation of highly innovative models.Work closely with Economists and applied scientists to drive batch/real-time model implementations and new feature creations.Work closely with operations staff to optimize various business operations.Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.Please visit https://www.amazon.science for more information
US, CA, Sunnyvale
Job summaryAlexa is the groundbreaking voice service that powers Echo and other devices designed around your voice. Our team is creating the science and technology behind Alexa. We’re working hard, having fun, and making history. Come join our team! You will have an enormous opportunity to impact the customer experience, design, architecture, and implementation of a cutting edge product used every day by people you know.We’re looking for a passionate, talented, and inventive scientist to help build industry-leading conversational technologies that customers love. Our team's mission is to develop state-of-the-art on-device keyword spotting technology that provides delightful device invocation experience to the customers. As an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in speech and audio processing. Your work will directly impact our customers in the form of novel products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. You will mentor junior scientists, create and drive new initiatives.Prior domain knowledge in speech, natural language processing, or computer vision is strongly preferred; solid knowledge of fundamentals of statistics, machine learning, and deep learning is required. Candidates should possess strong software engineering skills and several years of industry experience or a PhD in related disciplines.A day in the lifeAs part of the wake word team you will be involved in inventing new deep learning and speech recognition techniques to significantly expand the capabilities of on-device keyword spotter systems, build new speech recognition models, deploy those models to Production, deploy, measure, and analyze various performance metrics, and work with stakeholders to communicate important operational insights to leadership to improve customer experience of end-users of a variety of Alexa devices and services.
US, WA, Seattle
Job summaryCan Alexa help anyone experience the music they enjoy? Even if they don't know what they'd like to listen to in this moment? Or, if they know they want “Happy rock from the 90s”, can she help them find it?Your machine learning skills can help make that a reality on the Amazon Music team. We are seeking an Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval.Key job responsibilitiesYou'll work in a collaborative environment where you can pursue ambitious, long-term research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web.The successful candidate will have a PhD in Computer Science with a strong focus on machine learning, or a related field, and 4+ years of practical experience applying ML to solve complex problems in recommender systems, information retrieval, signal processing, NLP or dialogue systems. Great if you have a passion for music, but this is not a requirement.About the teamAmazon Music reimagines music listening by enabling customers to unlock millions of songs and thousands of curated playlists and stations with their voice. Amazon Music provides unlimited access to new releases and classic hits across iOS and Android mobile devices, PC, Mac, Echo, and Alexa-enabled devices including Fire TV and more. With Amazon Music, Prime members have access to ad-free listening of 2 million songs at no additional cost to their membership. Listeners can also enjoy the premium subscription service, Amazon Music Unlimited, which provides access to more than 75 million songs and the latest new releases. Amazon Music Unlimited customers also now have access to the highest-quality listening experience available, with more than 75 million songs available in High Definition (HD), more than 7 million songs in Ultra HD, and a growing catalog of spatial audio. Customers also have free access to an ad-supported selection of top playlists and stations on Amazon Music. All Amazon Music tiers now offer a wide selection of podcasts at no additional cost, and live streaming in partnership with Twitch. Engaging with music and culture has never been more natural, simple, and fun. For more information, visit amazonmusic.com or download the Amazon Music app.
US, CA, Sunnyvale
Job summaryMULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Applied Scientist IILocation: Sunnyvale, CaliforniaPosition Responsibilities:Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques, optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.
US, WA, Seattle
Job summaryMULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Applied Scientist IILocation: Seattle, WashingtonPosition Responsibilities:Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques, optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.