How the City of Memphis Uses Technology to Identify 75 Percent More Potholes
7153
Of your peers have already read this article.
4:30 Minutes
The most insightful time you'll spend today!
At 340 square miles, the City of Memphis is among the largest in the United States in terms of land area. Memphis has over 6,800 lane-miles of city streets, enough to drive back and forth to Los Angeles four times. Keeping these streets well maintained and safe for citizens and visitors is a major priority for the city.
Lots of traffic, lots of roads, and a four-season climate prone to wintertime freeze-thaw-refreeze cycles means the opportunity for potholes. Although the city aims to fill potholes within five business days of notification, it can take longer, especially during winter and early spring. Last year, the city’s Public Works crews repaired some 63,000 potholes, only 20% of which were reported by residents. Approximately 32,000-man-hours each year are spent repairing potholes, with seasonal fluctuations requiring ten to twelve Street Maintenance crews working steadily during the winter months. Still, many went unreported, leading the city to flag pothole request resolution under “needs improvement” on its open data portal website.
Like many large cities, Memphis also struggles with vacant and blighted properties. Nearly 15,000 properties in Memphis are likely vacant, and city officials contend that many are owned by out-of-town investors who live elsewhere and do not take necessary restoration or maintenance steps. These properties can decrease the value of surrounding real estate and discourage new businesses and other residents from moving to an area. Citizen frustration and concerns over the number of blighted properties has made blight eradication a major focus of the City of Memphis.
Historically, residents reported potholes and blighted properties by calling 311, or more recently by using the Memphis 311 app. However, these reports only covered about 20 percent of the problems — often the worst cases. And by the time residents took the initiative to submit a 311 report, they usually weren’t feeling good about the situation.
Recognizing that potholes and vacant properties are often the most visible indicators of whether a city government is doing its job efficiently, Memphis Mayor Jim Strickland and CIO Mike Rodriguez began looking for ways they could apply technology to fix the problems. Mike approached Google for ideas, and Google recommended conducting a machine learning proof-of-concept (POC) with SpringML, a Google Cloud Partner.
“Memphis is focused on easy living, and we want to do everything we can to keep our citizens happy,” says Mike Rodriguez. “Working with Google and SpringML to reduce potholes and urban blight using machine learning and artificial intelligence was an easy decision.”
Bringing machine learning to city operations and budgets
The city’s goal is to detect potholes and abandoned properties by analyzing video footage of roads and residential properties. It wanted to classify potholes by width and depth, and share the information with workers who can repair them. For abandoned properties, it wanted to enable more strategic deployment of resources for homeowners citywide and take action to hold neglectful property owners accountable.
The POC began by training TensorFlow models for ML object detection using preconfigured AI Platform Deep Learning VM Images on Compute Engine. SpringML helped set up cameras and developed a user interface to collect pothole data and automate the 311 ticketing process.
Together, the teams analyzed 30 days of video from a moving city bus and high-resolution video from 360-degree cameras mounted to a code enforcement vehicle, overlaid with data from 311 reports. As the models were refined, accuracy quickly climbed from 50 percent to over 90 percent as models were taught to differentiate a pothole from a manhole cover or other object.
The city also imported routes, potholes, and paving data along with geolocation data from ArcGIS and Google Maps into BigQuery to better understand street conditions and the proximity of potholes to one another. BigQuery also analyzes city property records, tax records, 311 reports, and third-party survey data on-demand to predict where homes are starting to become run down and where neighborhood decay is most likely to occur. The SpringML team created a pilot analysis to begin vacant property protections and developed a user interface tool to interact with the model’s results.
“Google Cloud Platform made it possible for us to experiment with machine learning and artificial intelligence to help solve our city’s problems while working within the budget constraints of a municipal IT organization,” says Mike. “Google turned a ‘nice to have’ into a ‘let’s do this!'”
Identifying 75 percent more potholes
Memphis expects to substantially reduce the number of potholes on its streets, creating a better driving experience for residents and visitors alike. Because drivers won’t be as likely to swerve to miss a pothole, streets will be safer and friendlier to bicycles and scooters. Fewer potholes will also save the city between $10,000 and $20,000 annually in city claims that it pays out in cases where vehicle damage results from a pothole that was not addressed in a timely manner.
“Historically, Public Works has relied primarily upon Street Maintenance crews to proactively locate and fill potholes. As Memphis has over 6,800 lane-miles of public streets, it is a daunting task to reliably survey the entire system in an efficient and systematic way,” says Robert Knecht, Public Works Director for the City of Memphis. “The outcome of the data collected will be invaluable to Public Works so that it can ensure it is managing the city’s street system in a more proactive manner.”
Memphis will be able to better prioritize road maintenance based on condition and impact, increasing the efficiency of its Public Works road crews. Analyzing video of streets also gave the city visibility into issues it wasn’t previously aware of, such as curbs, gutters, and manhole covers that had been mistakenly paved over and need to be excavated. The ML process is easily transferrable to other concerns as well, helping the city identify illegal signs or spools of cable hanging on light posts that could be potentially unsafe.
Helping communities recover and thrive
Memphis is also having success in analyzing predictive trends to combat high rates of abandoned and blighted properties, surpassing 97.5 percent accuracy. “In the past, Public Works experimented with comprehensive, city-wide blight identification by using approximately 200 volunteers to survey and photograph over 237,000 city parcels. This effort was costly, took a long time to complete, and resulted in inconsistent data collection,” says Robert. “Blighted property conditions can change quickly in a city the size of Memphis. Now, with this new technology, Memphis will be able to make a significant difference in the efforts to proactively and comprehensively identify and manage blighted and substandard properties.”
Code Enforcement with better data-driven detection mechanisms enables the city to also identify cases where homeowners are not physically or financially able to keep up with the challenges of homeownership and make them aware of resources that are available to assist them. Memphis Code Enforcement can do a better job of finding people living in derelict properties that pose hazards to inhabitants’ health and safety, and help them fix those problems or find a new place to live.
“Using SpringML and Google Cloud Platform to detect indicators of vacant or blighted properties will help Memphis create safer neighborhoods that will be more attractive to businesses and home buyers,” says Mike. “Property values and employment will go up, crime will go down, and social services can be more focused and effective.”
Revolutionizing service delivery for citizens
Memphis is proving the viability of a cost-effective, cloud-based machine learning model that other cities can follow. The city is already looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents.
As part of his commitment to a transparent government, Memphis Mayor Jim Strickland created an open data policy that commits to releasing raw data and sharing it with citizens in a variety of downloadable formats. Going forward, this transparency will help citizens understand how their needs are being served and uncover new, innovative use cases for AI and ML.
“Our goal is to become a smart city, and technologies such as Google Cloud Platform and SpringML put us ahead of the game,” says Mayor Strickland. “Google understands data, and there isn’t a better company to help us analyze our data resources for actionable insights.”
Rubin Observatory Leverages Google Cloud to Power Astronomical Research
6090
Of your peers have already read this article.
2:00 Minutes
The most insightful time you'll spend today!
This week, the Vera C. Rubin Observatory is launching the first preview of its new Rubin Science Platform (RSP) for an initial cohort of astronomers. The observatory, which is located in Chile but managed by the U.S. National Science Foundation’s NOIRLab in Tucson, AZ and SLAC in California, is jointly funded by the NSF and the U.S. Department of Energy. The platform provides an easy-to-use interface to store and analyze the massive datasets of the Legacy Survey of Space and Time (LSST), which will survey a third of the sky each night for ten years, detecting billions of stars and galaxies, and millions of supernovae, variable stars, and small bodies in our Solar System.
The LSST datasets are unprecedented in size and complexity, and will be far too large for scientists to download to their personal computers for analysis. Instead, scientists will use the RSP to process, query, visualize, and analyze the LSST data archives through a mixture of web portal, notebook, and other virtual data analysis services. An initial launch with simulated data, called Data Preview 0, builds on the Rubin Observatory’s three-year partnership with Google to develop an Interim Data Facility (IDF) on Google Cloud to prototype hosting of the massive LSST dataset. This agreement marks the first time a cloud-based data facility has been used for an astronomy application of this magnitude.
Bringing the stars to the cloud
For Data Preview 0, the IDF leverages Cloud Storage, Google Kubernetes Engine (GKE), and Compute Engine to provide the Rubin Observatory user community access to simulated LSST data in an early version of the RSP. The simulated data were developed over several years by the LSST Dark Energy Science Collaboration to imitate five years of an LSST-like survey over 300 square degrees of the sky (about 1,500 times the area of the moon). The resulting images are very realistic: they have the same instrumental characteristics, such as pixel size and sensitivity to photons, that are expected from the Rubin Observatory’s LSST Camera, and they were processed with an early version of the LSST Science Pipelines that will eventually be used to process LSST data. “This will be the first time that these workloads have ever been hosted in a cloud environment. Researchers will have an opportunity to explore an early version of this platform,” says Ranpal Gill, senior manager and head of communications at the Rubin Observatory.
Broadening access for more researchers
Over 200 scientists and students with Rubin Observatory data rights were selected to participate in Data Preview 0 from a pool of applicants that represents a wide range of demographic criteria, regions, and experience level. Participants will be supported with resources such as tutorials, seminars, communication channels, and networking opportunities—and they will be free to pursue their own science at their own pace using the data in the RSP.
“The revolutionary nature of the future LSST dataset requires a commensurately innovative system for data access and analysis paired with robust support for scientists,” says Melissa Graham, lead community scientist for the Rubin Observatory and research scientist in the astronomy department at the University of Washington. “I’m personally excited to enhance my own skills by using the RSP’s tools for big data analysis, while also helping others to learn and to pursue their LSST-related science goals during Data Preview 0.”
At the same time, the fact that the RSP is hosted in the cloud provides researchers at smaller institutions access to state-of-the-art astronomy infrastructure that is comparable to that of the largest national research centers.
The launch benefits the observatory too: the development team can learn what researchers are interested in while also testing and debugging the platform. Graham says that “the platform is still in active development so researchers using it will be able to follow along in the progress, and provide feedback on ways that we can optimize the development of the tools.”
Next steps
The LSST aims to begin the ten-year survey in 2023-24 and expects it to include 500 petabytes of data. Through the cloud, Google aims to help make this extraordinary project scalable and accessible to researchers everywhere. To learn more about Data Preview 0, watch this video.
Want to ramp up your own research in the cloud? We offer research credits to academics using Google Cloud for qualifying projects in eligible countries. You can find our application form on Google Cloud’s website or contact our sales team.
Ahead of the Curve: 5 Data and AI Trends Set to Shape 2023
2283
Of your peers have already read this article.
4:00 Minutes
The most insightful time you'll spend today!
How will your organization manage this year’s data growth and business requirements? Your actions and strategies involving data and AI will improve or undermine your organization’s competitiveness in the months and years to come. Our teams at Google Cloud have an eye on the future as we evolve our strategies to protect technology choice, simplify data integration, increase AI adoption, deliver needed information on demand, and meet security requirements.
Google Cloud worked with* IDC on multiple studies involving global organizations across industries in order to explore how data leaders are successfully addressing key data and AI challenges. We compiled the results in our 2023 Data and AI Trends report. In it, you’ll find the metrics-rich research behind the top five data and AI trends, along with tips and customer examples for incorporating them into your plans.
1: Show data silos the door
Given the increasing volumes of data we’re all managing, it’s no surprise that siloed transactional databases and warehousing strategies can’t meet modern demands. Organizations want to improve how they store, manage, analyze, and govern all their data, while reducing costs. They also want to eliminate conflicting insights from replicated data and empower everyone with fresh data.
A unified data cloud enables the integration of data and insights into transformative digital experiences and better decision making.
Andi Gutmans, GM and VP of Engineering for Databases, Google Cloud
In the report, you can learn how to adopt a unified data cloud that supports every stage of the data lifecycle so that you can improve data usage, accessibility, and governance. Inform your strategy by drawing on organizations’ examples such as a data fabric that improves customer experiences by connecting more than 80 data silos, as well as other unified data clouds that save money and simplify growth.
2: Usher in the age of the open data ecosystem
Data is the key to unlocking AI, speeding up development cycles, and increasing ROI. To protect against data and technology lock-in, more organizations are adopting open source software and open APIs.
Tweet this quote
Understand how you can simplify data integration, facilitate multicloud analytics, and use the technologies you want with an open data ecosystem, as described in the report. Learn from metrics about global open source adoption and public dataset usage. And explore how global companies adopted open data ecosystems to improve patient outcomes, increase website traffic by 25%, and cut operating costs by 90%.
3: Embrace the AI tipping point
Pulling useful information out of data is easier with AI and ML. Not only can you identify patterns and answer questions faster but the technologies also make it easier to solve problems at scale.
We’ve reached the AI tipping point. Whether people realize it or not, we’re already using applications powered by AI—every day. Social media platforms, voice assistants, and driving services are easy examples.
June Yang, VP, Cloud AI and Industry Solutions, Google Cloud
Organizations share how they’re reaching their goals using AI and ML by empowering “citizen data scientists” and having them focus on small wins first. Gain tips from Yang and other experts for developing your AI strategy. And read how organizations achieve outcomes such as a reduction of 7,400 tons per year in carbon emissions and a more than 200% increase in ROI from ad spend by using pattern recognition and other AI capabilities.
4: Infuse insights everywhere
Yesterday’s BI solutions have led to outdated insights and user fatigue with the status quo, based on generic metrics and old information. Research shows that as new tools come online, expectations for BI are changing, with companies revising their strategies to improve decision making, speed up the development of new revenue streams, and increase customer acquisition and retention by providing individuals with needed information on demand.
Organizations are equipping business decision-makers with the tools they need to incorporate required insights into their everyday workflows.
Kate Wright, Senior Director, Product Management, Google Cloud
In the report, you’ll discover why and how data leaders are rethinking their BI analytics strategies and applications to improve users’ trust and use of data in automated workflows, customizable dashboards, and on-demand reports. Global companies also share how they improve decision making with self-service BI, customer experiences with IoT analysis, and threat mitigation with embedded analytics.
5: Get to know your unknown data
Increasing data volumes can make it harder to know where and what data they store, which may create risk. Case in point: If a customer unexpectedly shares personally identifiable information during a recorded customer support call or chat session, that data might require specialized governance, which the standardized storage process may not provide.
If you don’t know what data you have, you cannot know that it’s accurately secured. You also don’t know what security risks you are incurring, or what security measures you need to take.
Anton Chuvakin, Senior Staff Security Consultant, Google Cloud
Check out the report to learn about data security risks that are often overlooked and how to develop proactive governance strategies for your sensitive data. You can also read how global organizations have increased customer trust and productivity by improving how they discover, classify, and manage their structured and unstructured data.
Be ready for what’s next
What’s exciting about these trends is that they’re enabling organizations across industries to realize very different goals using their choice of technologies. And although all the trends depend on each other, research shows you can realize measurable benefits whether you adopt one or all five.
Review the report yourself and learn how you can refine your organization’s data and AI strategies by drawing on the collective insights, experiences, and successes of more than 800 global organizations.
S4 Agtech Transforms Agriculture with Google Cloud
7507
Of your peers have already read this article.
8:30 Minutes
The most insightful time you'll spend today!
Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help increase the quantity and quality of crops.
This is essential for businesses in the agriculture industry, but it’s also critical to address growing food shortages around the world.
At S4, we create technology to de-risk crop production. We provide customers seeking agricultural risk management solutions with the tools to make better, data-driven decisions for their crop planning, based on machine learning and proprietary algorithms.
We interpret plant evolution on a global scale with predictive modeling and analytics, and offer super-efficient risk-transferring solutions. Our multi-cloud platform includes a petabyte-scale database, an open source stack, and—after 50 proof-of-concept evaluations—BigQuery for our data warehouse and the Cloud SQL database service to handle OLTP queries to our PostgreSQL database.
These PoCs included, among others, Microsoft Azure Data Lake Analytics, IBM Netezza, Postgres/PostGIS running on IBM bare-metal servers with SATA SSDs and on Google’s Compute Engine with NVMe disks, and on-premises memSQL, CitusData and Yandex ClickHouse.
Weeding out risk in an uncertain market
According to recent research, climate extreme events like drought, heat waves, and heavy precipitation are responsible for 18-43% of global variation in crop yields for maize, spring wheat, rice, and soybeans. This is a clear trend for other crops as well. Such variation poses risks of food shortages as well as large financial risks to farmers, insurers, and regions dependent on successful crop yields. Also, it creates vast humanitarian difficulties.
Our mission at S4 is to help de-risk crop production by matching the right data with analytics tools so farmers and other participants in the agricultural value chain can plan better, resulting in more reliable food supplies.
In a nutshell, we create indices out of biological assets. These indices measure yield losses on crops that are caused by the effects of weather and other factors, which are then used as underlying assets for products, such as swap/derivative contracts and parametric insurance policies, to transfer risk to the financial markets.
We enable insurers and lenders to buy and sell agricultural risks through the futures market. Also, our other products help farmers and seed and fertilizer companies provide customized genotype recommendations and fertilization requirements. This helps to optimize planting by geography, resources, and crop species, monitor phenological, pests and humidity evolution throughout the crop season, and estimate yields.
Local communities benefit from S4’s technology, as the ability to manage weather risks allows farmers to stabilize their cash flows, invest more to produce more with fewer risks, and develop in a more sustainable manner.
Growing data sources, reducing costs, accelerating performance
With the volume of diverse data sources and analytical complexity both growing at a very fast pace, we decided that using a major cloud services provider with a broad roadmap and global partnerships would be beneficial to S4’s future evolution.
At the same time, we wanted to bring our services to users faster and cut costs by consolidating our on-premises technology stack. When we started evaluating providers, our leading criteria included a powerful geospatial database and data analytics tools along with excellent support, all at a competitive price. GCP prevailed in nearly all criteria categories among the 50 companies we measured.
Our previous platform architecture included a hybrid relational database that used Compute Engine for virtual machines and Cloud Storage for database backup. The RDBMS was slow. Maintaining our own data warehouse was complex and expensive.
We wanted to use machine learning and neural networks, but couldn’t do so easily and affordably. The complexity of that system meant that products or services requiring small changes or additions to the data model translated to expensive expansions of infrastructure or project time.
Also, agronomical or product teams couldn’t test these changes by themselves, always requiring the intervention on no small part of the IT team, which led to further delays.
We added GCP services like BigQuery as S4’s cloud data warehouse and use BigQuery GIS for geospatial analysis, Cloud Dataflow for simplified stream and batch data processing, and Cloud SQL for queries to the S4 database platform, which have all made a huge impact on our services and bottom line.
Database and analytics costs have decreased by 40% and customers are receiving our analytical results 25% faster. In addition, we’ve eliminated the time-consuming downloading of images, reducing storage and processing costs by 80%, because we no longer need expensive tools licenses, and have greatly reduced classification processing times.
Our customers working in the agriculture industry are also benefiting from this infrastructure change. They are now able to speed up their data analytics using our GCP-based platform.
“S4 products and technologies unlock the full potential of satellite imagery for crop prescriptions, monitoring and yield estimates,” says Nicolás Loria, Manager of Marketing Services, Southern Cone, Corteva Agriscience.
“We’ve worked with S4 for the last three (and starting year number four) crop seasons as its team capabilities, data integration capacities, and analytics insights have allowed Corteva to perform an entire new solution. Thanks to S4’s customized 360° approach, fast response and delivery times, we have safely outsourced our remote crop analytic technical needs.”
Also, this new architecture has allowed us to scale our models and databases with almost no limits, at a fraction of the cost vs. the previous models.
We’ve saved a lot of time on executing processes and reduced work needed by our internal teams to do certain tasks, like preparing images, converting them, validating results, and more. Using Google Earth Engine has decreased the execution time of daily tasks anywhere from 50% to 90% of the previous time, going from an average time of 30 minutes to between four and 15 minutes, depending on the task.
In addition to saving money and time, we are able to focus on innovation with the GCP performance and features we’re using. We’re able to seamlessly add satellite data to analytics using both public datasets and our own private data, and deliver GIS data management, analytics, crop classification and monitoring in real time.
We can do semi-automatic crop classification and classification using spectral signatures with Google Earth Engine. Later this year, we’ll be using neural networks for pattern recognition and machine learning in new applications to improve crop yields and fine-tune risk models. And using GCP and Google Earth Engine infrastructure means we can run models for customers in South America and around the world, since Google Earth Engine has global satellite imagery available.
We’ve heard from our customer Indigo Argentina that they’re able to bring customers data insights faster.
“We are working with S4 in the development of two different applications for satellite crop monitoring and yield assessment,” says Carlos Becco, CEO, Indigo Argentina. “S4’s technology allowed us to manage and analyze multiple sources and layers of information in real time, letting us uncover valuable insights in Indigo’s own microbiome technologies, and at a very competitive cost.”
Analytical products and app development thrive with GCP
With GCP, we are updating and improving algorithms that we built manually with machine learning processes to develop drought indices for upcoming crop seasons. Algorithms can recognize specific phases of crop phenology (e.g., bud burst, flowering, fruiting, leaf fall) and correlate them with photosynthetic activity, light, water, temperature, radiation, and plant genetics factors. Other analytical products like crop monitoring, pre-planting recommendations, financial scoring, and yield estimation can now do a lot more for users by offering multiple layers and datasets, faster image processing, and real-time access via APIs.
We also replaced our bare-metal S4 app deployment with the App Engine serverless application platform. It provides tighter integration between the S4 platform and our BigQuery data warehouse for integration with marketplaces and third-party solutions.
We get all of these Google Cloud features with all the benefits of managed cloud services, from multiversioning and security to automatic backups and high availability.
At S4, we trust technology to decode plant growth and help protect farmers and their communities from climate change. With growing food shortages due to increasing populations and intensifying weather, data and analytics can have a huge impact in lowering financial risks and improving agricultural yields. It’s one sector where cloud, database, analytics, and other technologies are combining to improve business outcomes and affect the lives of billions of people. Learn more about S4’s work and learn more about data analytics on Google Cloud.
Google Cloud Partnership Fuels ListenField’s Agriculture Revolution
920
Of your peers have already read this article.
3:30 Minutes
The most insightful time you'll spend today!
When I was growing up in Thailand, I witnessed the challenges facing farmers including rising food demand, shortage of labor, and uneven crop yields caused by climate change. As a result, many smallholder farmers found themselves trapped in a vicious circle, unable to reduce food insecurity due to low yields, but lacking the resources to invest for a more profitable future.
I was determined to make a difference. After earning my master’s degree in Information Management I joined a research project with the University of Tokyo where we used sensors to monitor spinach fields in Thailand. The results of this early experiment in precision farming were impressive. We proved that it was possible to grow organic crops with minimal use of fertilizers, while consumers benefited from higher quality spinach grown in Thailand.
This inspired me to found ListenField in 2017. Our mission is to transform farm management by collecting data from multiple sources, including field sensors, soil scanning, weather data, seasonal forecasts, and satellite imagery. By modeling this data, we provide farmers with insights that enable them to optimize production ‘from soil to harvest’.
A bumper crop of farming data
Artificial intelligence and machine learning play a central role in our prediction platform, combining crop health monitoring, growth prediction, and soil nutrition analysis. Farmers can apply real-time insights to their schedule from our FarmAI Mobile App, while our FarmAI Dashboard enables agri-food businesses to collaborate with agronomists and farmers so that all parties benefit from higher profit margins and more sustainable growing strategies.
Another important feature of our business model is AgroAPI, which makes our analytics available to third parties. Clients can embed deep analytics in their applications, including crop growth prediction and remote sensing analysis, without needing to develop complicated algorithms and data pipelines by themselves.
We are also excited about our research into genomic prediction in collaboration with the Japanese government and several research companies. Using our Data-Driven Breeding Platform, breeders and seed companies can upload their genomic data and gain practical insights that help accelerate the reproduction of high-quality seeds and plants.
Today, more than 30,000 farmers use our technology especially in Vietnam and Thailand where it is used to improve rice, cassava, and sugar cane yields. The technology is also being rolled out for orange and mango farmers enabling them to monitor individual trees and adjust irrigation to improve the sweetness of the fruit at harvest time.
Responding fast to changing conditions
We were using another cloud provider to run our business, but one of the ListenField team members drew our attention to Google Cloud. As well as the technology, we were also attracted by the Google for Startups Cloud Program which provides us with Google Cloud credits that cover our first and second years of Google Cloud usage. We also met our Account Representative, who provides us with training, business and tech support, and Google-wide discounts. It’s great to have a point of contact that can help us on our startup journey and make the most of Google’s resources.
By reducing the pressure on our finances and human resources, we were able to experiment and adapt in response to early experiments. This flexibility also enabled us to demonstrate a compelling business case to new and existing investors.
Our Google Cloud Platform Partner, Navagis, gave us additional momentum thanks to their expertise in mapping and geospatial data. They also played a crucial role in the integration of Google Earth Engine, which we use to map agricultural areas.
We also use Firebase for application development and Google Workspace for team collaboration. Colab and Vertex AI enable us to build, deploy, and scale our machine learning models quickly, ensuring that we remain competitive and attractive to new customers.
Giving female entrepreneurs the opportunity to flourish
Both the Google Cloud team and our colleagues at Navagis helped us to navigate the challenges many early-stage startups face. My background is in science and academia, so I appreciated the business mindset offered by both organizations to help us continue to grow and scale.
Being a female entrepreneur leading a startup can also be tough, but Google Cloud and Navagis helped me to build a strong network, access funding, and make my voice heard. Today, ListenField has several female executives, while 50% of our researchers and many of the farmers on our platform are women.
Above all, Google Cloud helps us to power an agriculture revolution in south-east Asia. Smallholder farmers can transition from analog to digital farming, improving their yields and reducing waste. The benefits to the economy are also significant including greater food security and reducing the use of industrial fertilizers that generate potent greenhouse gasses.
And that’s just the beginning of what we can do. Our next milestone is to reach 50,000 farmers and cut one million tonnes of greenhouse gas emissions. It sounds ambitious, but with the Google Cloud and Navagis teams behind us, I’m confident that we will reach these targets.
ListenField team members
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
3142
Of your peers have already downloaded this article
22:30 Minutes
The most insightful time you'll spend today!
Companies everywhere are seeking to leverage the power of AI. And rightly so. The smart applications of AI enable organizations to improve, to scale, and to accelerate the decision-making process across most business functions, so as to work both more efficiently and more effectively. It can also open up new avenues and new revenue streams, providing the organization with an additional competitive edge.
In short, many believe (as we do) that the enterprises that invest in building industry-specific AI solutions today are positioning themselves to be the global economic leaders of tomorrow. But the path to building an effective AI capability is not an easy one. There are many challenges to overcome. Challenges with the technology to develop platforms and solutions. With the people who will implement and manage that technology. With the data that fuels the technology. And with the processes that govern the whole of it. How do you harness the power inherent in AI, while avoiding any potential missteps?
That’s where Google Cloud comes in. Our framework for AI adoption provides a guide to technology leaders who want to build an effective AI capability, one that enables them to leverage the power of AI to enhance and streamline their business, smoothly and smartly. The framework is informed by Google’s own evolution, innovation, and leadership in AI, including experience deploying AI in production through products such as Gmail and Google Photos. It is also inspired by many years of experience helping cloud customers — from startups to enterprises, in various industries — to solve complex challenges.
With Google Cloud’s AI Adoption Framework, you’ll be able to create and evolve your own transformative AI capability. You’ll have a map for assessing where you are in the journey and where, at the end of it, you’d like to be. You’ll have a structure for building scalable AI capabilities to create better insights from big data with powerful algorithms across the entire business.
With Google Cloud as your guide, the path to AI is considerably smoother.
Download this whitepaper to find out:
- A map for assessing where you are in your AI journey and where you want to be
- A comprehensive structure for building an effective AI capability across your entire organisation to create actionable insights from data
- A technical deep dive for technology leaders
More Relevant Stories for Your Company
What’s Next for Personalization on Google Cloud
Customer shopping behavior has changed for good. With fewer in-store shopping visits retailers have had to shore up their digital storefronts and explore new ways to meaningfully engage with their customers. Delivering a superior customer experience has become even more of a differentiator for the early movers and personalized recommendations
Eli Lilly’s Custom-built Translation Service Leverages Google’s Translation Engine
Eli Lilly leverages Google Cloud's machine translation to globalize content to serve their multilingual employees. Watch the video to learn how the healthcare company built an in-house solution powered by Google Cloud APIs to address the challenge of translating multilingual content at scale. Also explore Google's innovations and investments into
Notebook Executor Feature of Vertex AI Workbench to Schedule Notebooks Ad Hoc or on Recurring Basis
When solving a new ML problem, it’s common to start by experimenting with a subset of your data in a notebook environment. But if you want to execute a long-running job, add accelerators, or run multiple training trials with different input parameters, you’ll likely find yourself copying code over to
Making Hybrid Work Human: Google Workspace and Economist Impact Survey
Google Workspace recently commissioned Economist Impact to complete a global survey (October 2021)* on the state of hybrid work, including its challenges and opportunities. We already knew that the pandemic had fundamentally changed the world of work, but the survey emphasizes the scale, reach, and longevity of those changes.. Over 75% of
