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mariacallous · 2 years ago

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The open internet once seemed inevitable. Now, as global economic woes mount and interest rates climb, the dream of the 2000s feels like it’s on its last legs. After abruptly blocking access to unregistered users at the end of last month, Elon Musk announced unprecedented caps on the number of tweets—600 for those of us who aren’t paying $8 a month—that users can read per day on Twitter. The move follows the platform’s controversial choice to restrict third-party clients back in January.

This wasn’t a standalone event. Reddit announced in April that it would begin charging third-party developers for API calls this month. The Reddit client Apollo would have to pay more than $20 million a year under new pricing, so it closed down, triggering thousands of subreddits to go dark in protest against Reddit’s new policy. The company went ahead with its plan anyway.

Leaders at both companies have blamed this new restrictiveness on AI companies unfairly benefitting from open access to data. Musk has said that Twitter needs rate limits because AI companies are scraping its data to train large language models. Reddit CEO Steve Huffman has cited similar reasons for the company’s decision to lock down its API ahead of a potential IPO this year.

These statements mark a major shift in the rhetoric and business calculus of Silicon Valley. AI serves as a convenient boogeyman, but it is a distraction from a more fundamental pivot in thinking. Whereas open data and protocols were once seen as the critical cornerstone of successful internet business, technology leaders now see these features as a threat to the continued profitability of their platforms.

It wasn’t always this way. The heady days of Web 2.0 were characterized by a celebration of the web as a channel through which data was abundant and widely available. Making data open through an API or some other means was considered a key way to increase a company’s value. Doing so could also help platforms flourish as developers integrated the data into their own apps, users enriched datasets with their own contributions, and fans shared products widely across the web. The rapid success of sites like Google Maps—which made expensive geospatial data widely available to the public for the first time—heralded an era where companies could profit through free, mass dissemination of information.

“Information Wants To Be Free” became a rallying cry. Publisher Tim O’Reilly would champion the idea that business success in Web 2.0 depended on companies “disagreeing with the consensus” and making data widely accessible rather than keeping it private. Kevin Kelly marveled in WIRED in 2005 that “when a company opens its databases to users … [t]he corporation’s data becomes part of the commons and an invitation to participate. People who take advantage of these capabilities are no longer customers; they’re the company’s developers, vendors, skunk works, and fan base.” Investors also perceived the opportunity to generate vast wealth. Google was “most certainly the standard bearer for Web 2.0,” and its wildly profitable model of monetizing free, open data was deeply influential to a whole generation of entrepreneurs and venture capitalists.

Of course, the ideology of Web 2.0 would not have evolved the way it did were it not for the highly unusual macroeconomic conditions of the 2000s and early 2010s. Thanks to historically low interest rates, spending money on speculative ventures was uniquely possible. Financial institutions had the flexibility on their balance sheets to embrace the idea that the internet reversed the normal laws of commercial gravity: It was possible for a company to give away its most valuable data and still get rich quick. In short, a zero interest-rate policy, or ZIRP, subsidized investor risk-taking on the promise that open data would become the fundamental paradigm of many Google-scale companies, not just a handful.

Web 2.0 ideologies normalized much of what we think of as foundational to the web today. User tagging and sharing features, freely syndicated and embeddable links to content, and an ecosystem of third-party apps all have their roots in the commitments made to build an open web. Indeed, one of the reasons that the recent maneuvers of Musk and Huffman seem so shocking is that we have come to expect data will be widely and freely available, and that platforms will be willing to support people that build on it.

But the marriage between the commercial interests of technology companies and the participatory web has always been one of convenience. The global campaign by central banks to curtail inflation through aggressive interest rate hikes changes the fundamental economics of technology. Rather than facing a landscape of investors willing to buy into a hazy dream of the open web, leaders like Musk and Huffman now confront a world where clear returns need to be seen today if not yesterday.

This presages major changes ahead for the design of the internet and the rights of users. Twitter and Reddit are pioneering an approach to platform management (or mismanagement) that will likely spread elsewhere across the web. It will become increasingly difficult to access content without logging in, verifying an identity, or paying a toll. User data will become less exportable and less shareable, and there will be increasingly fewer expectations that it will be preserved. Third-parties that have relied on the free flow of data online—from app-makers to journalists—will find APIs ever more expensive to access and scraping harder than ever before.

We should not let the open web die a quiet death. No doubt much of the foundational rhetoric of Web 2.0 is cringeworthy in the harsh light of 2023. But it is important to remember that the core project of building a participatory web where data can be shared, improved, critiqued, remixed, and widely disseminated by anyone is still genuinely worthwhile.

The way the global economic landscape is shifting right now creates short-sighted incentives toward closure. In response, the open web ought to be enshrined as a matter of law. New regulations that secure rights around the portability of user data, protect the continued accessibility of crucial APIs to third parties, and clarify the long-ambiguous rules surrounding scraping would all help ensure that the promise of a free, dynamic, competitive internet can be preserved in the coming decade.

For too long, advocates for the open web have implicitly relied on naive beliefs that the network is inherently open, or that web companies would serve as unshakable defenders of their stated values. The opening innings of the post-ZIRP world show how broader economic conditions have actually played the larger role in architecting how the internet looks and feels to this point. Believers in a participatory internet need to reach for stronger tools to mitigate the effects of these deep economic shifts, ensuring that openness can continue to be embedded into the spaces that we inhabit online.

WIRED Opinion publishes articles by outside contributors representing a wide range of viewpoints. Read more opinions here. Submit an op-ed at [email protected].

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allaboutmarketing4you · 1 year ago

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How Google Maps, Spotify, Shazam and More Work

"How does Google Maps use satellites, GPS and more to get you from point A to point B? What is the tech that powers Spotify’s recommendation algorithm?

From the unique tech that works in seconds to power tap-to-pay to how Shazam identifies 23,000 songs each minute, WSJ explores the engineering and science of technology that catches our eye.

Chapters:

0:00 Google Maps

9:07 LED wristbands

14:30 Spotify’s algorithm

21:30 Tap-to-Pay

28:18 Noise-canceling headphones

34:33 MSG Sphere

41:30 Shazam "

Source: The Wall Street Journal

#Tech#Algorithm#WSJ

Additional information:

" How Does Google Map Works?

Google Maps is a unique web-based mapping service brought to you by the tech giant, Google. It offers satellite imagery, aerial photography, street maps, 360° panoramic views of streets, real-time traffic conditions, and route planning for traveling by foot, car, bicycle, or public transportation.

A short history of Google maps:

Google Maps was first launched in February 2005, as a desktop web mapping service. It was developed by a team at Google led by Lars and Jens Rasmussen, with the goal of creating a more user-friendly and accurate alternative to existing mapping services. In 2007, Google released the first version of Google Maps for mobile, which was available for the Apple iPhone. This version of the app was a huge success and quickly became the most popular mapping app on the market. As time has passed, Google Maps has consistently developed and enhanced its capabilities, including the addition of new forms of map data like satellite and aerial imagery and integration with other Google platforms like Google Earth and Google Street View.

In 2013, Google released a new version of Google Maps for the web, which included a redesigned interface and new features like enhanced search and integration with Google+ for sharing and reviewing places.

Today, Google Maps is available on desktop computers and as a mobile app for Android and iOS devices. It is used by millions of people around the world to get directions, find places, and explore new areas.

How does google maps work?

Google Maps works by using satellite and aerial imagery to create detailed maps of the world. These maps are then made available to users through a web-based interface or a mobile app.

When you open Google Maps, you can search for a specific location or browse the map to explore an area. You can also use the app to get directions to a specific place or find points of interest, such as businesses, landmarks, and other points of interest. Google Maps uses a combination of GPS data, user input, and real-time traffic data to provide accurate and up-to-date information about locations and directions. The app also integrates with other Google services, such as Google Earth and Google Street View, to provide additional information and features.

Overall, Google Maps is a powerful tool that makes it easy to find and explore locations around the world. It’s available on desktop computers and as a mobile app for Android and iOS devices.

Google uses a variety of algorithms in the backend of Google Maps to provide accurate and up-to-date information about locations and directions. Some of the main algorithms used by Google Maps include:

Image recognition: Google Maps uses image recognition algorithms to extract useful information from the satellite and street view images used to create the map. These algorithms can recognize specific objects and features in the images, such as roads, buildings, and landmarks, and use this information to create a detailed map of the area.

Machine learning: Google Maps uses machine learning algorithms to analyze and interpret data from a variety of sources, including satellite imagery, street view images, and user data. These algorithms can identify patterns and trends in the data, allowing Google Maps to provide more accurate and up-to-date information about locations and directions.

Geospatial data analysis: Google Maps uses geospatial data analysis algorithms to analyze and interpret data about the earth’s surface and features. This includes techniques like geographic information systems (GIS) and geospatial data mining, which are used to extract useful information from large datasets of geospatial data.

Overall, these algorithms are an essential part of the backend of Google Maps, helping the service to provide accurate and up-to-date information to users around the world.

Google Maps uses a variety of algorithms to determine the shortest path between two points:

Here are some of the algorithms that may be used:

Dijkstra’s algorithm: This is a classic algorithm for finding the shortest path between two nodes in a graph. It works by starting at the source node and progressively exploring the graph, adding nodes to the shortest path as it goes.

A* search algorithm: This is another popular algorithm for finding the shortest path between two points. It works by combining the benefits of Dijkstra’s algorithm with a heuristic function that helps guide the search toward the destination node.

It’s worth noting that Google Maps may use a combination of these algorithms, as well as other specialized algorithms, to determine the shortest path between two points. The specific algorithms used may vary depending on the specifics of the route, such as the distance, the number of turns, and the type of terrain. "

Source: geeksforgeeks.org - -> You can read the full article at geeksforgeeks.org

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zzuniverses · 2 years ago

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Would Governments Disclose the Truth About Aliens?

What are the odds? What do they gain or lose by disclosure?

Aliens. They keep some people up at night and ignite the imagination of millions; all the while, most people still wave the idea of aliens visiting our planet as a fairytale.

For some of us, the existence of “other” life somewhere in the Universe is inevitable, given the magnitude of the known space and the diversity of life on our planet alone. It makes no sense to imagine that ours is the only planet capable of supporting life.

There are an estimated 300 million potentially habitable planets in our galaxy alone, and there are billions of galaxies containing billions of planets, some galaxies much larger than our Milky Way.

In this article, I talk about the incentives for any and all governments to keep such potential knowledge secret versus revealing it to the public.

In light of recent events, with the whistleblower David Charles Grusch, a former member of the National Geospatial-Intelligence Agency and National Reconnaissance Office, saying that the government possesses craft of non-human origin, it is time to investigate the government’s motives for disclosure or secrecy.

We don’t know what is true and what is not, but we can talk about the relationship between aliens, the government, and the truth in theoretical terms.

What is the likelihood of the government disclosing the truth about aliens?

Why would governments want to keep information about extraterrestrial life hidden?

What incentives do governments have to reveal the existence of aliens?

What are the risks and consequences of government disclosure of extraterrestrial life?

Could they keep something as big as extraterrestrials a secret?

Read the article now and find out.

#aliens #aliens and ufos #disclosure #government #extraterrestial life

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afiliatevasu · 2 years ago

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What is Solr – Comparing Apache Solr vs. Elasticsearch

In the world of search engines and data retrieval systems, Apache Solr and Elasticsearch are two prominent contenders, each with its strengths and unique capabilities. These open-source, distributed search platforms play a crucial role in empowering organizations to harness the power of big data and deliver relevant search results efficiently. In this blog, we will delve into the fundamentals of Solr and Elasticsearch, highlighting their key features and comparing their functionalities. Whether you're a developer, data analyst, or IT professional, understanding the differences between Solr and Elasticsearch will help you make informed decisions to meet your specific search and data management needs.

Overview of Apache Solr

Apache Solr is a search platform built on top of the Apache Lucene library, known for its robust indexing and full-text search capabilities. It is written in Java and designed to handle large-scale search and data retrieval tasks. Solr follows a RESTful API approach, making it easy to integrate with different programming languages and frameworks. It offers a rich set of features, including faceted search, hit highlighting, spell checking, and geospatial search, making it a versatile solution for various use cases.

Overview of Elasticsearch

Elasticsearch, also based on Apache Lucene, is a distributed search engine that stands out for its real-time data indexing and analytics capabilities. It is known for its scalability and speed, making it an ideal choice for applications that require near-instantaneous search results. Elasticsearch provides a simple RESTful API, enabling developers to perform complex searches effortlessly. Moreover, it offers support for data visualization through its integration with Kibana, making it a popular choice for log analysis, application monitoring, and other data-driven use cases.

Comparing Solr and Elasticsearch

Data Handling and Indexing

Both Solr and Elasticsearch are proficient at handling large volumes of data and offer excellent indexing capabilities. Solr uses XML and JSON formats for data indexing, while Elasticsearch relies on JSON, which is generally considered more human-readable and easier to work with. Elasticsearch's dynamic mapping feature allows it to automatically infer data types during indexing, streamlining the process further.

Querying and Searching

Both platforms support complex search queries, but Elasticsearch is often regarded as more developer-friendly due to its clean and straightforward API. Elasticsearch's support for nested queries and aggregations simplifies the process of retrieving and analyzing data. On the other hand, Solr provides a range of query parsers, allowing developers to choose between traditional and advanced syntax options based on their preference and familiarity.

Scalability and Performance

Elasticsearch is designed with scalability in mind from the ground up, making it relatively easier to scale horizontally by adding more nodes to the cluster. It excels in real-time search and analytics scenarios, making it a top choice for applications with dynamic data streams. Solr, while also scalable, may require more effort for horizontal scaling compared to Elasticsearch.

Community and Ecosystem

Both Solr and Elasticsearch boast active and vibrant open-source communities. Solr has been around longer and, therefore, has a more extensive user base and established ecosystem. Elasticsearch, however, has gained significant momentum over the years, supported by the Elastic Stack, which includes Kibana for data visualization and Beats for data shipping.

Document-Based vs. Schema-Free

Solr follows a document-based approach, where data is organized into fields and requires a predefined schema. While this provides better control over data, it may become restrictive when dealing with dynamic or constantly evolving data structures. Elasticsearch, being schema-free, allows for more flexible data handling, making it more suitable for projects with varying data structures.

Conclusion

In summary, Apache Solr and Elasticsearch are both powerful search platforms, each excelling in specific scenarios. Solr's robustness and established ecosystem make it a reliable choice for traditional search applications, while Elasticsearch's real-time capabilities and seamless integration with the Elastic Stack are perfect for modern data-driven projects. Choosing between the two depends on your specific requirements, data complexity, and preferred development style. Regardless of your decision, both Solr and Elasticsearch can supercharge your search and analytics endeavors, bringing efficiency and relevance to your data retrieval processes.

Whether you opt for Solr, Elasticsearch, or a combination of both, the future of search and data exploration remains bright, with technology continually evolving to meet the needs of next-generation applications.

#solr #solr consulting services #elasticsearch #solr vs elasticsearch #apache solr

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geowgs84 · 2 days ago

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How Tessellation Enhances GPU-Accelerated Geospatial Rendering

In high-performance geospatial visualization, tessellation is crucial for optimizing rendering pipelines, particularly by leveraging the parallel processing capabilities of modern Graphics Processing Units (GPUs). Whether it's for terrain rendering in 3D Geographic Information Systems (GIS) or real-time visualization of point clouds, tessellation significantly improves both visual quality and computational efficiency.

https://www.geowgs84.ai/post/how-tessellation-enhances-gpu-accelerated-geospatial-rendering

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daintilyultimateslayer · 3 days ago

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Mapinfo Pro India

Network efficiency is not just a technical priority—it’s a business imperative. Weak signal zones and outages disrupt operations and customer experiences, making proactive monitoring and management essential. Advintek Geoscience, leveraging the powerful capabilities of MapInfo, offers advanced solutions to optimize network performance.

This blog delves into two key aspects of network performance monitoring: Signal Strength Analysis and Outage Mapping, showcasing how MapInfo’s tools transform raw data into actionable insights.

Signal Strength Analysis: Gaining Insight from Real-Time Data

Monitoring signal strength is crucial for identifying and addressing weak coverage areas. However, interpreting real-time data across vast geographic regions demands more than just conventional tools. Here’s how MapInfo enhances signal strength analysis.

Integrating Real-Time Data Feeds

MapInfo’s advanced geospatial capabilities allow seamless integration of real-time data from network devices. This integration enables:

Dynamic Signal Visualization: Network operators can view signal strength across geographic zones in real time, identifying weak spots with precision.

Customizable Dashboards: Tailored visualizations highlight trends and patterns, facilitating faster decision-making.

Pinpointing Weak Signal Zones

MapInfo’s sophisticated analytics enable precise identification of weak signal zones.

Data-Driven Insights: Algorithms analyze real-time and historical signal data, spotlighting areas needing infrastructure improvements.

Impact Assessment: Operators can correlate weak signal zones with user complaints or performance metrics, ensuring targeted interventions.

Planning Network Upgrades

With a clear view of signal deficiencies, MapInfo helps businesses prioritize infrastructure investments.

Strategic Site Selection: Pinpointing optimal locations for new towers or repeaters.

Resource Allocation: Ensuring budgets are directed toward the most critical upgrades.

By providing granular insights into signal performance, MapInfo enables organizations to maintain robust network coverage, reducing downtime and enhancing customer satisfaction.

Outage Mapping: Visualizing and Resolving Network Disruptions

Outages are inevitable, but their impact can be minimized with effective visualization and resolution strategies. MapInfo simplifies this complex process by transforming outage data into actionable maps.

Real-Time Disruption Visualization

During an outage, speed is critical. MapInfo allows teams to:

Pinpoint Affected Areas: Visualize disruptions on an intuitive map interface, showing the exact locations and extent of outages.

Overlay Critical Infrastructure: Identify nearby facilities or resources impacted by the disruption, helping prioritize restoration efforts.

Planning Maintenance with Precision

Beyond real-time responses, MapInfo’s tools support strategic maintenance planning:

Historical Data Analysis: Use outage patterns to predict and prevent future disruptions.

Resource Deployment Optimization: Strategically position repair teams and equipment for rapid response.

Enhancing Communication During Outages

Clear communication during outages is essential for customer trust.

Stakeholder Reports: MapInfo-generated visuals simplify updates to management, partners, and customers.

Outage Notifications: Geospatial data ensures that only affected users receive updates, reducing unnecessary alerts.

By integrating geospatial intelligence into outage management, MapInfo equips organizations to respond swiftly, limit downtime, and build customer confidence.

Benefits of Advintek Geoscience’s Solutions

Adopting Advintek Geoscience’s solutions powered by MapInfo provides organizations with a competitive edge in network performance monitoring. By leveraging signal strength analysis and outage mapping, businesses can achieve transformative improvements in their network operations.

Improved Network Reliability

Ensuring reliable network performance is a cornerstone of operational success. With Advintek’s solutions:

Proactive Weak Spot Identification: Organizations can monitor and address areas with weak signal strength before they escalate into significant issues, minimizing service disruptions.

Reduced Downtime: Outage mapping enables faster identification and resolution of network disruptions, significantly reducing the time customers experience connectivity problems.

Consistent Performance: Continuous monitoring ensures that networks operate at optimal levels, providing consistent service quality across all geographic regions.

Cost Efficiency

Advintek’s approach helps organizations optimize costs while maintaining high standards of network performance.

Targeted Investments: With precise data on weak signal zones and recurring outage patterns, businesses can focus their investments on areas that need the most attention, avoiding unnecessary expenditure.

Optimized Resource Deployment: MapInfo’s geospatial insights streamline the allocation of resources like repair teams, equipment, and infrastructure, ensuring they are deployed where they’re needed most.

Preventative Maintenance Savings: Predictive analytics based on historical data help reduce costs associated with reactive repairs by enabling planned maintenance schedules.

Data-Driven Decision-Making

The ability to analyze real-time and historical data empowers organizations to make informed decisions that directly impact network performance and business outcomes.

Strategic Planning: Businesses can use data insights to develop long-term strategies for network expansion, ensuring maximum coverage and performance.

Performance Benchmarking: By comparing historical and real-time data, organizations can identify trends, measure improvements, and refine their operations for continuous efficiency.

Predictive Insights: Advintek’s tools enable predictive analytics, helping businesses anticipate potential issues and address them before they affect users.

Enhanced User Experience

A well-performing network is key to maintaining customer satisfaction and loyalty. Advintek’s solutions contribute to a superior user experience by:

Minimizing Interruptions: Quick resolution of outages and improved signal strength ensures that users face minimal disruptions, leading to a seamless connectivity experience.

Building Customer Trust: Transparent communication during outages and rapid resolution builds trust and strengthens customer relationships.

Boosting Loyalty and Retention: A consistently reliable network reduces user frustrations and promotes long-term customer retention, helping businesses grow their customer base and reputation.

Scalability and Future-Readiness

In addition to these immediate benefits, Advintek’s solutions provide the foundation for future growth and scalability:

Adaptability to Emerging Technologies: With advanced tools like MapInfo, businesses can integrate new technologies and adapt to evolving industry needs seamlessly.

Support for Expanding Networks: As networks grow, Advintek’s tools ensure that monitoring and management remain efficient, no matter the scale.

By adopting these comprehensive solutions, organizations not only optimize their current network performance but also build a robust framework for sustained growth and innovation.

Why Choose Advintek Geoscience?

Advintek Geoscience combines industry expertise with MapInfo’s advanced geospatial capabilities to deliver tailored solutions for network performance monitoring. From signal strength analysis to outage mapping, Advintek empowers organizations to make data-driven decisions that improve operational efficiency and customer satisfaction.

As a trusted partner in geospatial technology, Advintek Geoscience ensures your network stays resilient, reliable, and ready to meet tomorrow’s demands. VisitAdvintek Geoscience today to learn more about how we can transform your network performance strategies.

#network efficiency #MapInfo #signal strength analysis #outage mapping #geospatial intelligence #network monitoring #telecom solutions #predictive analytics #geoscience #business optimization

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earthobservation · 4 days ago

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How IP Protection Teams Can Leverage Earth Observation Data for Smarter Biotech Innovation

In the age of biotechnology and data-driven agriculture, safeguarding intellectual property has become a mission-critical task. For organizations and researchers investing in the development of new plant varieties, the rise in unauthorized cultivation and distribution poses significant financial and ecological risks. This is where the convergence of satellite technology, artificial intelligence, and biotechnology is transforming the landscape—offering IP Protection Teams a smarter, more scalable way to monitor and protect their innovations.

The Growing Need for Agricultural IP Protection

With the global agricultural industry undergoing rapid modernization, plant variety development has become a core component of biotech innovation. These new varieties, designed for better yield, resilience, or climate adaptability, represent years of research and investment. However, traditional methods of IP infringement detection in agriculture—such as field inspections or manual audits—are time-consuming, costly, and often ineffective across large geographic regions.

Enter Earth Observation and AI

Earth observation data, derived from satellite imagery and other remote sensing technologies, offers a game-changing solution. When paired with advanced AI models, it allows for the accurate identification of specific plant varieties over vast landscapes. SC Solutions, a pioneer in this field, has developed the Botanic Analyzer—a powerful tool designed to enhance natural selection tracking and mitigate IP leakage through intelligent earth observation.

By leveraging this platform, IP Protection Teams can now detect unauthorized cultivation in real-time, whether it’s in open fields, remote forests, or arid deserts. This eliminates the guesswork and delays associated with manual detection, making the process not only faster but more precise.

How the Botanic Analyzer Works

SC’s Botanic Analyzer integrates advanced machine learning algorithms with high-resolution satellite data to identify and classify plant varieties with remarkable accuracy. The tool taps into vast datasets and geospatial imagery to analyze traits that are unique to each plant variety, even under varying environmental conditions.

This capability enables the detection of protected plant species being grown without proper authorization. By mapping these patterns, organizations can take proactive legal and strategic actions, significantly reducing the risk of revenue loss and IP theft.

Enhancing Cultivation Management and Innovation

Beyond enforcement, earth observation data also empowers biotech companies to innovate with more precision. With SC’s Cultivation Management Platform, companies can optimize breeding strategies, assess regional adaptability, and fine-tune resource inputs—all while keeping IP protection in mind. This integrated approach ensures that new biotech innovations are not only effective but also secure.

Moreover, SC’s Carbon Analyzer adds another layer of intelligence by helping organizations evaluate the environmental impact of their cultivation efforts. This is particularly valuable for companies aiming to align with global sustainability standards while ensuring their intellectual property remains protected.

A Sustainable and Secure Future for Biotech

In a world where sustainability and innovation must go hand-in-hand, the use of earth observation and AI technologies offers a clear advantage. Not only do these tools improve the accuracy and efficiency of IP infringement detection in agriculture, but they also drive smarter decision-making across the entire biotech value chain.

SC Solutions is leading the way by offering end-to-end platforms that allow biotech companies to detect, analyze, and act—all in real-time. With these tools, IP Protection Teams are no longer confined to traditional methods. Instead, they can operate with greater intelligence, agility, and confidence, ensuring their innovations make it to market—protected and optimized.

As the world embraces the full potential of space and AI technologies, the fusion of earth observation data and biotech offers a forward-looking path to innovation. For those on the front lines of IP protection, the future is not only smarter but more secure than ever.

To learn more about how SCSolutions is redefining the future of field research and vegetation management, visit https://scsolutions.ai/.

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amrutmnm · 6 days ago

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The Satellite Data Services Market is valued at USD 9.3 billion in 2023 and is projected to reach USD 20.9 billion by 2028, at a CAGR of 17.5% from 2023 to 2028 according to a new report by MarketsandMarkets™. The satellite data services market encompasses the provision of geospatial information and imagery through satellite-based platforms.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=55690321

Browse in-depth TOC on "Satellite Data Services Market" 300 – Tables 70 – Figures 350 – Pages

Satellite Data Services Market Report Scope:

Report Coverage

Details

Market Revenue in 2023

$ 9.3 billion

Estimated Value by 2028

$ 20.9 billion

Growth Rate

Poised to grow at a CAGR of 17.5%

Market Size Available for

2019–2028

Forecast Period

2023–2028

Forecast Units

Value (USD Million/Billion)

Report Coverage

Revenue Forecast, Competitive Landscape, Growth Factors, and Trends

Segments Covered

By Vertical, End-Use, Service, Deployment and Region

Geographies Covered

North America, Europe, Asia Pacific, and Rest of World

Key Market Challenge

Concern over space debris

Key Market Opportunities

Increased government investment in space agencies

Key Market Drivers

Significant advancements in geospatial imagery analytics with the introduction of AI and big data vehicles

This market involves the collection, processing, analysis, and dissemination of data captured by satellites orbiting the Earth. The market is experiencing rapid growth driven by technological advancements, increasing demand for geospatial data, and expanding applications across diverse sectors. Satellite data services cater to a wide range of applications across various sectors, including agriculture, forestry, environmental monitoring, urban planning, infrastructure development, defense, and disaster management. Key drivers propelling market growth include continuous advancements in satellite technology, which enable higher resolution imagery and enhanced data analytics capabilities. Additionally, the rising demand for geospatial information for decision-making, resource management, and strategic planning fuels market expansion. Prominent players in the satellite data services market include industry leaders such as Maxar Technologies, Airbus, Planet Labs, and L3Harris Technologies, Inc, alongside emerging startups and innovative technology firms. However, despite the significant growth opportunities, the market faces challenges related to data privacy and security concerns. Ensuring the confidentiality, integrity, and availability of satellite data amidst increasing cybersecurity threats poses a notable challenge for industry stakeholders. Overcoming these challenges while capitalizing on the growing demand for satellite data services will be crucial for sustaining market growth and fostering innovation in the years ahead.

#Satellite Data Services #Satellite Data Services Market #Satellite Data Services Industry #Satellite Data Services Market Companies #Satellite Data Services Market Size #Satellite Data Services Market Share #Satellite Data Services Market Growth #Satellite Data Services Market Statistics

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epitome225 · 12 days ago

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Drone Surveying: The Epitome of Precision & Aerial Intelligence

In the modern age of rapid infrastructure development, environmental assessment, and data-driven decision-making, drone surveying has emerged as a revolutionary force in the world of geospatial technology. Known for its exceptional precision and efficiency, drone surveying—also referred to as UAV (Unmanned Aerial Vehicle) surveying—is redefining the way we capture, process, and analyze spatial data.

What Is Drone Surveying?

Drone surveying is the process of using unmanned aerial vehicles equipped with sensors, cameras, and GPS receivers to collect geospatial data from the air. Unlike traditional surveying methods that often require significant manpower and time, drone surveying allows surveyors to access and map large or hard-to-reach areas quickly, accurately, and cost-effectively.

High-resolution imagery, LiDAR sensors, multispectral cameras, and thermal sensors can be mounted on drones depending on the application. The data captured is processed using photogrammetry software to create detailed maps, 3D models, orthophotos, and digital elevation models (DEMs).

Advantages of Drone Surveying

Unmatched Accuracy: With RTK (Real-Time Kinematic) and PPK (Post-Processed Kinematic) technologies, drone surveys can achieve centimeter-level accuracy, making them ideal for engineering, construction, and infrastructure projects.

Rapid Data Collection: Drones can survey hundreds of acres in a single flight, drastically reducing field time compared to traditional methods.

Cost Efficiency: Reduced manpower, minimal equipment setup, and quick data acquisition translate into lower overall project costs.

Enhanced Safety: Surveying hazardous, unstable, or dangerous terrains—such as cliffs, mines, and rooftops—can be done safely from the air without putting human lives at risk.

Versatile Applications: From construction site monitoring and agriculture to mining, forestry, flood modeling, and disaster assessment, drone surveying is applicable across numerous sectors.

Key Applications of Drone Surveying

Construction & Infrastructure: Drones provide topographic maps and volumetric calculations essential for earthworks, road design, and structural planning.

Agriculture: Multispectral imaging helps assess crop health, irrigation issues, and pest infestations with precision.

Mining & Quarrying: Drones offer real-time volume measurements of stockpiles and monitor ongoing excavation work efficiently.

Environmental Monitoring: Drones assist in tracking deforestation, erosion, and changes in water bodies with time-lapse mapping.

Urban Planning: They help in creating 3D city models for infrastructure development and land use planning.

The Future of Drone Surveying

The integration of AI, cloud computing, and machine learning is enhancing the analytical capabilities of drone data. Real-time insights, automation, and predictive modeling are becoming more accessible through user-friendly platforms.

As regulatory frameworks evolve and drone technology becomes more affordable, even small businesses and local governments are adopting UAV-based surveying to streamline projects and boost productivity.

Conclusion

Drone surveying stands as the epitome of precision and aerial intelligence, transforming industries with its dynamic capabilities. It not only improves the accuracy and speed of data collection but also opens up new possibilities for innovation in mapping and analysis. For anyone involved in geospatial projects—from engineers and architects to environmentalists and city planners—embracing drone surveying is no longer optional; it is essential for staying ahead in the digital age.

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govindhtech · 1 year ago

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Space Tech: Private Ventures and Mars Exploration

Space Tech

Beyond intrepid exploration, space technology has advanced to address pressing issues on Earth. It is becoming more and more essential to the effective operation of contemporary societies and their economic growth. Space has the potential to directly affect billions of people’s lives and open up large-scale, highly impactful solutions.

A broad term for satellites, space stations, ground stations, tracking and monitoring centers, downstream analytics and artificial intelligence, software, and other technologies, SpaceTech offers innovative ways to solve global concerns. Satellites increase communication, navigation, and earth observation capacity at low cost even in remote locations. Satellite-based earth observation data is vital, accurate, and reliable for data-driven decision-making by businesses and governments.

The underserved and otherwise unprofitable regions can benefit from high-speed connectivity thanks to the satellites. The application of action plans for intelligent agriculture, resource management (land and water), infrastructure development (urban and rural), climate and weather monitoring, environmental protection (including reducing the risk of disaster), and other purposes can all benefit from the use of satellite data.

Aerospace Innovation

The space industry is predicted to increase in value from USD 360 billion in 2018 to USD 558 billion by 2026 and roughly USD 1 trillion by 2040. Even though the Indian Space Research Organization (ISRO) is one of the world’s top space agencies and is working on projects like the Indian Regional Navigation Satellite System (NavIC) and the Mars Orbiter Mission (MOM), India currently only makes up 2%, or USD 7 Bn, of this market value.

One reason could be that the private sector’s contribution to the Indian space industry has primarily consisted of ISRO subcontracting, with ISRO historically handling the crucial value addition activities internally. Because of this, Indian private companies have lagged behind other world leaders in SpaceTech in terms of end-to-end capabilities.

The publication of SpaceCom Policy 2020, Space RS Policy 2020, Geospatial Policy 2021, and other policies, along with the creation of organizations like NewSpace India Ltd (NSIL) and the Indian National Space Promotion and Authorization Centre (IN–SPACe), have created a national push to expedite the private sector’s involvement in the Indian space area. The Department of Space is also working on a comprehensive Space Act and other policies, including launch vehicle and space exploration policies.

Because of our natural curiosity and desire to understand the universe, space travel has long fascinated people.

Recently, private enterprise and international cooperation have transformed space exploration.

This article will explore the changing face of space exploration and emphasize the importance of international collaboration and private industry.

New Space Technologies

Pioneers of Personal Space Travel

NASA, Roscosmos, and ESA were the only government space agencies allowed to explore space. However, private companies leading space innovation changed everything:

SpaceX since 2002 has resupplied the ISS, developed reusable rocket technology, and prepared to colonize Mars.

Jeff Bezos’ Blue Origin offers professional and recreational suborbital and orbital spaceflight.

Rick Branson’s suborbital space tourism company, Virgin Galactic.

Innovating, competing, and seeking commercial opportunities beyond Earth are redefining space exploration in private space ventures.

Space Exploration Companies

International Space Cooperation

Space exploration requires international cooperation even as private businesses grow:

The Earth-orbiting International Space Station (ISS) is a global collaboration marvel. European, Japanese, Canadian, Russian, and US space agencies participate.

Mars exploration: NASA, ESA, and others work on Curiosity and Mars Sample Return.

The Artemis Accords outlines global cooperation on the Moon and beyond, inviting international partners to lunar exploration.

Global Collaboration and Private Enterprises Benefits

Space exploration benefits from private sector involvement and international cooperation in a number of ways.

Innovation: By bringing in competition and innovation, private endeavors lower costs and advance technology.

Commercialization: Businesses worldwide can take advantage of commercial endeavors to expand their satellite deployment, space tourism, and resource exploitation capabilities.

Shared Resources: Working together, nations can pool resources, exchange knowledge, and take on challenging projects.

Scientific Discovery: Across national boundaries, international cooperation increases the possibility of scientific discovery and exploration.

Difficulties and Things to Think About

Although private and international partnerships present notable benefits, they also present certain challenges.

Regulation: To address new challenges, the framework governing international cooperation and private space endeavors needs to change.

Resource Management: A complex ethical and legal challenge is the responsible use of space resources, such as lunar mining.

Space Debris: Coordinated actions ought to tackle the expanding problem of space debris and environmentally friendly space operations.

Space Travel Prospects

Future space exploration could lead to asteroid mining, planet colonization, and scientific breakthroughs.

Space exploration is entering a new era as private companies and multinational partnerships change the space environment.

Space exploration is more accessible, sustainable, and transformative than ever thanks to private innovation and international collaboration. It shows our willingness to push the limits and our enduring spirit of exploration.

Mars Rover

What is Mars Rover?

A robotic vehicle that investigates the surface of Mars is called a rover. Rovers are long-range, remotely controlled vehicles that gather data and take images while traveling great distances. They have found evidence of water, ancient life, and possible resources on Mars, among many other significant discoveries.

Six Mars rovers have been successful so far:

In 1997, Sojourner became the first rover to set foot on Mars. During 83 days, it investigated the Ares Vallis region. The twin rovers Spirit (2004) and Opportunity (2004) touched down on Mars in 2004. For many years, they investigated the Gusev Crater and Meridiani Planum, respectively. Opportunity stopped operating in 2018 and Spirit became stuck in 2010.

Gale Crater is presently being explored by Curiosity (2012). It has found evidence of ancient lakes and rivers, among many other significant discoveries.

The Jezero Crater region is being explored in Perseverance (2021). In addition to gathering samples of rock and regolith broken rock and soil for potential return to Earth, it is searching for indications of prehistoric life.

The first Chinese rover to set foot on Mars is Zhurong (2021). It is investigating the area of Utopia Planitia.

An essential component of our Mars exploration are the Mars rovers. They have made significant contributions to our understanding of the Red Planet’s potential for habitability.

Read more on Govindhtech.com

#Space Tech #MarsExploration #Ventures #SpaceTech #satellites #AI #Aerospace #NASA #technews #technology #govindhtech

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ireedacademy01 · 16 days ago

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Your Career Pathway After an MBA in Real Estate Analytics and Marketing

The real estate industry in India is evolving rapidly, with technology and data playing a vital role in transforming how decisions are made, investments are managed, and properties are marketed. As the sector becomes more complex and competitive, professionals with specialized skills in data analysis and strategic marketing are increasingly in demand. This has led to a rise in the popularity of the MBA in Real Estate Analytics and Marketing, a program tailored for those who want to blend business intelligence with industry knowledge.

Understanding MBA in Real Estate Analytics and Marketing

An MBA in Real Estate Analytics and Marketing is a two-year postgraduate program designed to offer students a unique combination of real estate fundamentals, data analytics, and marketing strategies. The course is ideal for individuals aspiring to become decision-makers in property development, sales, and market research.

Students learn how to assess market trends, evaluate property values, interpret financial data, and implement effective marketing campaigns. With these capabilities, graduates are well-equipped to support organizations in making data-driven decisions.

Career Scope After MBA in Real Estate

One of the major advantages of pursuing an MBA in Real Estate is the vast array of career opportunities it opens up. Whether in urban planning, construction management, sales, or investment analysis, graduates are well-prepared to take on various roles.

Some promising career options include:

Real Estate Market Analyst: Using data to evaluate market trends and advise on investment decisions.

Digital Marketing Specialist in Real Estate: Promoting property portfolios through SEO, social media, and online campaigns.

Real Estate Financial Analyst: Assessing the financial viability of real estate projects using forecasting and predictive modeling.

Project Manager: Overseeing the planning and execution of real estate developments.

Sales and Marketing Manager: Driving revenue through strategic branding and marketing campaigns.

These roles demand expertise in analytics and marketing, which are the core pillars of this specialized MBA.

What Makes the Program Unique?

Unlike traditional management courses, the MBA in Real Estate Analytics and Marketing focuses heavily on data and how it can be used to shape real estate decisions. Students are trained to:

Analyze customer behavior and preferences

Forecast housing trends and pricing patterns

Utilize CRM tools for lead management

Create and manage digital campaigns

Develop performance dashboards and real-time analytics reports

This results in graduates who are not only proficient in traditional management techniques but are also capable of leveraging technology to deliver results.

Industry Demand and Growth Opportunities

With the Indian real estate sector expected to reach USD 1 trillion by 2030, the need for data-savvy managers has never been greater. From metro cities to Tier-II and Tier-III towns, data and marketing are central to driving sales and development. An MBA in Real Estate Analytics bridges this gap by preparing professionals to:

Identify and target high-growth markets

Use geospatial analytics for site selection

Optimize pricing strategies based on buyer data

Run automated marketing funnels

Report key performance indicators to stakeholders

As the sector embraces PropTech and AI-driven solutions, graduates from this program are positioned as thought leaders and innovators.

Curriculum Overview

The curriculum of this MBA program is structured to provide both conceptual knowledge and practical skills. Core subjects typically include:

Real Estate Economics

Principles of Marketing and Consumer Behavior

Data Visualization and Analytics Tools

Financial Modeling in Real Estate

Digital Marketing and Automation

Legal and Regulatory Framework

Hands-on experience through internships and capstone projects is a crucial component of the program, helping students apply what they’ve learned in real-world scenarios.

Ideal Candidate Profile

This program is suitable for:

Graduates from engineering, architecture, commerce, or law backgrounds

Working professionals in real estate seeking upskilling

Marketing professionals wanting to transition into real estate

Entrepreneurs planning to build or scale a real estate venture

If you’re aiming for a career that combines data interpretation with client engagement and strategy execution, then this MBA offers the right blend of skills.

Salary Expectations

The salary structure for professionals with an MBA in Real Estate analytics and marketing background is highly attractive:

Entry-Level: ₹6 to ₹10 LPA

Mid-Level (5-8 years): ₹12 to ₹18 LPA

Senior-Level (10+ years): ₹20 LPA and above

Top recruiters include property developers, real estate consultancies, PropTech firms, and investment companies. Your salary will largely depend on your experience, skill set, and the region you’re working in.

Why Choose This MBA Program?

Here’s why this MBA program stands out:

Focused Curriculum: Targeted learning around real estate, analytics, and marketing.

Industry-Driven Projects: Real-world case studies and internships.

Cross-Functional Expertise: Learn to manage data, people, and brand strategy.

Networking Opportunities: Interact with real estate leaders, mentors, and recruiters.

Flexibility: Some institutes offer weekend or online classes for working professionals.

Conclusion

An MBA in Real Estate Analytics and Marketing is more than just a degree—it’s a career accelerator. With India’s real estate sector growing rapidly and technology reshaping the way we buy, sell, and manage properties, professionals who can interpret data and lead strategic campaigns are invaluable.

By combining domain knowledge with analytical and marketing skills, this MBA offers a well-rounded approach to building a successful career. Whether you're a graduate exploring new domains or a professional aiming for leadership, this course is your gateway to impactful roles in one of India’s most dynamic industries. https://www.ireedindia.com/blog-details/mba-in-real-estate-analytics-and-marketing

#real estate course #business analytics & marketing in real estate #mba business analytics in real estate #mba in real estate #real estate advance program #real estate education #real estate mba #diploma in real estate #real estate diploma #real estate management #mba in real estate india

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mariacallous · 1 year ago

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In 2019, a government contractor and technologist named Mike Yeagley began making the rounds in Washington, DC. He had a blunt warning for anyone in the country’s national security establishment who would listen: The US government had a Grindr problem.

A popular dating and hookup app, Grindr relied on the GPS capabilities of modern smartphones to connect potential partners in the same city, neighborhood, or even building. The app can show how far away a potential partner is in real time, down to the foot.

In its 10 years of operation, Grindr had amassed millions of users and become a central cog in gay culture around the globe.

But to Yeagley, Grindr was something else: one of the tens of thousands of carelessly designed mobile phone apps that leaked massive amounts of data into the opaque world of online advertisers. That data, Yeagley knew, was easily accessible by anyone with a little technical know-how. So Yeagley—a technology consultant then in his late forties who had worked in and around government projects nearly his entire career—made a PowerPoint presentation and went out to demonstrate precisely how that data was a serious national security risk.

As he would explain in a succession of bland government conference rooms, Yeagley was able to access the geolocation data on Grindr users through a hidden but ubiquitous entry point: the digital advertising exchanges that serve up the little digital banner ads along the top of Grindr and nearly every other ad-supported mobile app and website. This was possible because of the way online ad space is sold, through near-instantaneous auctions in a process called real-time bidding. Those auctions were rife with surveillance potential. You know that ad that seems to follow you around the internet? It’s tracking you in more ways than one. In some cases, it’s making your precise location available in near-real time to both advertisers and people like Mike Yeagley, who specialized in obtaining unique data sets for government agencies.

Working with Grindr data, Yeagley began drawing geofences—creating virtual boundaries in geographical data sets—around buildings belonging to government agencies that do national security work. That allowed Yeagley to see what phones were in certain buildings at certain times, and where they went afterwards. He was looking for phones belonging to Grindr users who spent their daytime hours at government office buildings. If the device spent most workdays at the Pentagon, the FBI headquarters, or the National Geospatial-Intelligence Agency building at Fort Belvoir, for example, there was a good chance its owner worked for one of those agencies. Then he started looking at the movement of those phones through the Grindr data. When they weren’t at their offices, where did they go? A small number of them had lingered at highway rest stops in the DC area at the same time and in proximity to other Grindr users—sometimes during the workday and sometimes while in transit between government facilities. For other Grindr users, he could infer where they lived, see where they traveled, even guess at whom they were dating.

Intelligence agencies have a long and unfortunate history of trying to root out LGBTQ Americans from their workforce, but this wasn’t Yeagley’s intent. He didn’t want anyone to get in trouble. No disciplinary actions were taken against any employee of the federal government based on Yeagley’s presentation. His aim was to show that buried in the seemingly innocuous technical data that comes off every cell phone in the world is a rich story—one that people might prefer to keep quiet. Or at the very least, not broadcast to the whole world. And that each of these intelligence and national security agencies had employees who were recklessly, if obliviously, broadcasting intimate details of their lives to anyone who knew where to look.

As Yeagley showed, all that information was available for sale, for cheap. And it wasn’t just Grindr, but rather any app that had access to a user’s precise location—other dating apps, weather apps, games. Yeagley chose Grindr because it happened to generate a particularly rich set of data and its user base might be uniquely vulnerable. A Chinese company had obtained a majority stake in Grindr beginning in 2016—amping up fears among Yeagley and others in Washington that the data could be misused by a geopolitical foe. (Until 1995, gay men and women were banned from having security clearances owing in part to a belief among government counterintelligence agents that their identities might make them vulnerable to being leveraged by an adversary—a belief that persists today.)

But Yeagley’s point in these sessions wasn’t just to argue that advertising data presented a threat to the security of the United States and the privacy of its citizens. It was to demonstrate that these sources also presented an enormous opportunity in the right hands, used for the right purpose. When speaking to a bunch of intelligence agencies, there’s no way to get their attention quite like showing them a tool capable of revealing when their agents are visiting highway rest stops.

Mike Yeagley saw both the promise and the pitfalls of advertising data because he’d played a key role in bringing advertising data into government in the first place. His 2019 road show was an attempt to spread awareness across the diverse and often siloed workforces in US intelligence. But by then, a few select corners of the intel world were already very familiar with his work, and were actively making use of it.

Yeagley had spent years working as a technology “scout”—looking for capabilities or data sets that existed in the private sector and helping to bring them into government. He’d helped pioneer a technique that some of its practitioners would jokingly come to call “ADINT”—a play on the intelligence community’s jargon for different sources of intelligence, like the SIGINT (signals intelligence) that became synonymous with the rise of codebreaking and tapped phone lines in the 20th century, and the OSINT (open source intelligence) of the internet era, of which ADINT was a form. More often, though, ADINT was known in government circles as adtech data.

Adtech uses the basic lifeblood of digital commerce—the trail of data that comes off nearly all mobile phones—to deliver valuable intelligence information. Edward Snowden’s 2013 leaks showed that, for a time, spy agencies could get data from digital advertisers by tapping fiber-optic cables or internet choke points. But in the post-Snowden world, more and more traffic like that was being encrypted; no longer could the National Security Agency pull data from advertisers by eavesdropping. So it was a revelation—especially given the public outcry over Snowden’s leaks—that agencies could just buy some of the data they needed straight from commercial entities. One technology consultant who works on projects for the US government explained it this way to me: “The advertising technology ecosystem is the largest information-gathering enterprise ever conceived by man. And it wasn’t built by the government.”

Everyone who possesses an iPhone or Android phone has been given an “anonymized” advertising ID by Apple or Google. That number is used to track our real-world movement, our internet browsing behavior, the apps we put on our phone, and much more. Billions of dollars have been poured into this system by America’s largest corporations. Faced with a commercially available repository of data this rich and detailed, the world’s governments have increasingly opened up their wallets to buy up this information on everyone, rather than hacking it or getting it through secret court orders.

Here’s how it works. Imagine a woman named Marcela. She has a Google Pixel phone with the Weather Channel app installed. As she heads out the door to go on a jog, she sees overcast skies. So Marcela opens the app to check if the forecast calls for rain.

By clicking on the Weather Channel’s blue icon, Marcela triggers a frenzy of digital activity aimed at serving her a personalized ad. It begins with an entity called an advertising exchange, basically a massive marketplace where billions of mobile devices and computers notify a centralized server whenever they have an open ad space.

In less than the blink of an eye, the Weather Channel app shares a ream of data with this ad exchange, including the IP address of Marcela’s phone, the version of Android it's running, her carrier, plus an array of technical data about how the phone is configured, down to what resolution the screen resolution is set to. Most valuable of all, the app shares the precise GPS coordinates of Marcela’s phone and the pseudonymized advertising ID number that Google has assigned to her, called an AAID. (On Apple devices, it’s called an IDFA.)

To the layperson, an advertising ID is a string of gibberish, something like bdca712j-fb3c-33ad-2324-0794d394m912. To advertisers, it’s a gold mine. They know that bdca712j-fb3c-33ad-2324-0794d394m912 owns a Google Pixel device with the Nike Run Club app. They know that bdca712j-fb3c-33ad-2324-0794d394m912 often frequents Runnersworld.com. And they know that bdca712j-fb3c-33ad-2324-0794d394m912 has been lusting after a pair of new Vaporfly racing shoes. They know this because Nike, Runnersworld.com, and Google are all plugged into the same advertising ecosystem, all aimed at understanding what consumers are interested in.

Advertisers use that information as they shape and deploy their ads. Say both Nike and Brooks, another running shoe brand, are trying to reach female running aficionados in a certain income bracket or in certain zip codes. Based on the huge amounts of data they can pull from the ether, they might build an “audience”—essentially a huge list of ad IDs of customers known or suspected to be in the market for running shoes. Then in an instantaneous, automated, real-time auction, advertisers tell a digital ad exchange how much they’re willing to pay to reach those consumers every time they load an app or a web page.

There are some limits and safeguards on all this data. Technically, a user can reset their assigned advertising ID number (though few people do so—or even know they have one). And users do have some control over what they share, via their app settings. If consumers don’t allow the app they’re using to access GPS, the ad exchange can’t pull the phone’s GPS location, for example. (Or at least they aren’t supposed to. Not all apps follow the rules, and they are sometimes not properly vetted once they are in app stores.)

Moreover, ad exchange bidding platforms do minimal due diligence on the hundreds or even thousands of entities that have a presence on their servers. So even the losing bidders still have access to all the consumer data that came off the phone during the bid request. An entire business model has been built on this: siphoning data off the real-time bidding networks, packaging it up, and reselling it to help businesses understand consumer behavior.

Geolocation is the single most valuable piece of commercial data to come off those devices. Understanding the movement of phones is now a multibillion-dollar industry. It can be used to deliver targeted advertising based on location for, say, a restaurant chain that wants to deliver targeted ads to people nearby. It can be used to measure consumer behavior and the effectiveness of advertising. How many people saw an ad and later visited a store? And the analytics can be used for planning and investment decisions. Where is the best location to put a new store? Will there be enough foot traffic to sustain such a business? Is the number of people visiting a certain retailer going up or down this month, and what does that mean for the retailer’s stock price?

But this kind of data is good for something else. It has remarkable surveillance potential. Why? Because what we do in the world with our devices cannot truly be anonymized. The fact that advertisers know Marcela as bdca712j-fb3c-33ad-2324-0794d394m912 as they’re watching her move around the online and offline worlds offers her almost no privacy protection. Taken together, her habits and routines are unique to her. Our real-world movement is highly specific and personal to all of us. For many years, I lived in a small 13-unit walk-up in Washington, DC. I was the only person waking up every morning at that address and going to The Wall Street Journal’s offices. Even if I was just an anonymized number, my behavior was as unique as a fingerprint even in a sea of hundreds of millions of others. There was no way to anonymize my identity in a data set like geolocation. Where a phone spends most of its evenings is a good proxy for where its owner lives. Advertisers know this.

Governments know this too. And Yeagley was part of a team that would try to find out how they could exploit it.

In 2015, a company called PlaceIQ hired Yeagley. PlaceIQ was an early mover in the location data market. Back in the mid-2000s, its founder, Duncan McCall, had participated in an overland driving race from London to Gambia across the land-mine-strewn Western Sahara. He had eschewed the usual practice of hiring an expensive Bedouin guide to help ensure safe passage through the area. Instead, he found online a GPS route that someone else had posted from a few days earlier on a message board. McCall was able to download the route, load it into his own GPS device, and follow the same safe path. On that drive through the Western Sahara, McCall recalled dreaming up the idea for what would become PlaceIQ to capture all of the geospatial data that consumers were emitting and generate insights. At first the company used data from the photo-sharing website Flickr, but eventually PlaceIQ started tapping mobile ad exchanges. It would be the start of a new business model—one that would prove highly successful.

Yeagley was hired after PlaceIQ got an investment from the CIA’s venture capital arm, In-Q-Tel. Just as it had poured money into numerous social media monitoring services, geospatial data had also attracted In-Q-Tel’s interest. The CIA was interested in software that could analyze and understand the geographic movement of people and things. It wanted to be able to decipher when, say, two people were trying to conceal that they were traveling together. The CIA had planned to use the software with its own proprietary data, but government agencies of all kinds eventually became interested in the kind of raw data that commercial entities like PlaceIQ had—it was available through a straightforward commercial transaction and came with fewer restrictions on use inside government than secret intercepts.

While working there, Yeagley realized that the data itself might be valuable to the government, too. PlaceIQ was fine selling software to the government but was not prepared to sell its data to the feds. So Yeagley approached a different company called PlanetRisk—one of the hundreds and hundreds of tiny startups with ties to the US government dotted around office parks in Northern Virginia. In theory, a government defense contractor offered a more secure environment than a civilian company like PlaceIQ to do the kind of work he had in mind.

PlanetRisk straddled the corporate world and the government contracting space—building products that were aimed at helping customers understand the relative dangers of various spots around the world. For example, a company that wanted to establish a store or an office somewhere in the world might turn to PlanetRisk to analyze data on crime, civil unrest, and extreme weather as they vary geographically.

PlanetRisk hired Yeagley in 2016 as vice president of global defense—essentially a sales and business development job. The aim was for him to develop his adtech technology inside the contractor, which might try to sell it to various government agencies. Yeagley brought with him some government funding from his relationships around town in the defense and intelligence research communities.

PlanetRisk’s earliest sales demo was about Syria: quantifying the crush of refugees flowing out of Syria after years of civil war and the advancing ISIS forces. From a commercial data broker called UberMedia, PlanetRisk had obtained location data on Aleppo—the besieged Syrian city that had been at the center of some of the fiercest fighting between government forces and US-backed rebels. It was an experiment in understanding what was possible. Could you even obtain location information on mobile phones in Syria? Surely a war zone was no hot spot for mobile advertising.

But to the company’s surprise, the answer was yes. There were 168,786 mobile devices present in the city of Aleppo in UberMedia’s data set, which measured mobile phone movements during the month of December 2015. And from that data, they could see the movement of refugees around the world.

The discovery that there was extensive data in Syria was a watershed. No longer was advertising merely a way to sell products; it was a way to peer into the habits and routines of billions. “Mobile devices are the lifeline for everyone, even refugees,” Yeagley said.

PlanetRisk had sampled data from a range of location brokers—Cuebiq, X-Mode, SafeGraph, PlaceIQ, and Gravy Analytics—before settling on UberMedia. (The company has no relation to the rideshare app Uber.) UberMedia was started by the veteran advertising and technology executive Bill Gross, who had helped invent keyword-targeted ads—the kinds of ads that appear on Google when you search a specific term. UberMedia had started out as an advertising company that helped brands reach customers on Twitter. But over time, like many other companies in this space, UberMedia realized that it could do more than just target consumers with advertising. With access to several ad exchanges, it could save bid requests that contained geolocation information, and then it could sell that data. Now, this was technically against the rules of most ad exchanges, but there was little way to police the practice. At its peak, UberMedia was collecting about 200,000 bid requests per second on mobile devices around the world.

Just as UberMedia was operating in a bit of a gray zone, PlanetRisk had likewise not been entirely forthright with UberMedia. To get the Aleppo data, Yeagley told UberMedia that he needed the data as part of PlanetRisk’s work with a humanitarian organization—when in fact the client was a defense contractor doing research work funded by the Pentagon. (UberMedia’s CEO would later learn the truth about what Mike Yeagley wanted the data for. And others in the company had their own suspicions. “Humanitarian purposes” was a line met with a wink and nod around the company among employees who knew or suspected what was going on with Yeagley’s data contracts.) Either way, UberMedia wasn’t vetting its customers closely. It appeared to be more eager to make a sale than it was concerned about the privacy implications of selling the movement patterns of millions of people.

When it came time to produce a demo of PlanetRisk’s commercial phone-tracking product, Yeagley’s 10-year-old daughter helped him come up with a name. They called the program Locomotive—a portmanteau of location and motive. The total cost to build out a small demo was about $600,000, put up entirely by a couple of Pentagon research funding arms. As the PlanetRisk team put Locomotive through the paces and dug into the data, they found one interesting story after another.

In one instance they could see a device moving back and forth between Syria and the West—a potential concern given ISIS’s interest in recruiting Westerners, training them, and sending them back to carry out terrorist attacks. But as the PlanetRisk team took a closer look, the pattern of the device’s behavior indicated that it likely belonged to a humanitarian aid worker. They could track that person’s device to UN facilities and a refugee camp, unlikely locales for Islamic State fighters to hang out.

They realized they could track world leaders through Locomotive, too. After acquiring a data set on Russia, the team realized they could track phones in the Russian president Vladimir Putin’s entourage. The phones moved everywhere that Putin did. They concluded the devices in question did not actually belong to Putin himself; Russian state security and counterintelligence were better than that. Instead, they believed the devices belonged to the drivers, the security personnel, the political aides, and other support staff around the Russian president; those people’s phones were trackable in the advertising data. As a result, PlanetRisk knew where Putin was going and who was in his entourage.

There were other oddities. In one data set, they found one phone kept transiting between the United States and North Korea. The device would attend a Korean church in the United States on Sundays. Its owner appeared to work at a GE factory, a prominent American corporation with significant intellectual property and technology that a regime like Pyongyang would be interested in. Why was it traveling back and forth between the United States and North Korea, not exactly known as a tourist destination? PlanetRisk considered raising the issue with either the US intelligence agencies or the company but ultimately decided there wasn’t much they could do. And they didn’t necessarily want their phone-tracking tool to be widely known. They never got to the bottom of it.

Most alarmingly, PlanetRisk began seeing evidence of the US military’s own missions in the Locomotive data. Phones would appear at American military installations such as Fort Bragg in North Carolina and MacDill Air Force Base in Tampa, Florida—home of some of the most skilled US special operators with the Joint Special Operations Command and other US Special Operations Command units. They would then transit through third-party countries like Turkey and Canada before eventually arriving in northern Syria, where they were clustering at the abandoned Lafarge cement factory outside the town of Kobane.

It dawned on the PlanetRisk team that these were US special operators converging at an unannounced military facility. Months later, their suspicions would be publicly confirmed; eventually the US government would acknowledge the facility was a forward operating base for personnel deployed in the anti-ISIS campaign.

Even worse, through Locomotive, they were getting data in pretty close to real time. UberMedia’s data was usually updated every 24 hours or so. But sometimes, they saw movement that had occurred as recently as 15 or 30 minutes earlier. Here were some of the best-trained special operations units in the world, operating at an unannounced base. Yet their precise, shifting coordinates were showing up in UberMedia’s advertising data. While Locomotive was a closely held project meant for government use, UberMedia’s data was available for purchase by anyone who could come up with a plausible excuse. It wouldn’t be difficult for the Chinese or Russian government to get this kind of data by setting up a shell company with a cover story, just as Mike Yeagley had done.

Initially, PlanetRisk was sampling data country by country, but it didn’t take long for the team to wonder what it would cost to buy the entire world. The sales rep at UberMedia provided the answer: For a few hundred thousand dollars a month, the company would provide a global feed of every phone on earth that the company could collect on. The economics were impressive. For the military and intelligence community, a few hundred thousand a month was essentially a rounding error—in 2020, the intelligence budget was $62.7 billion. Here was a powerful intelligence tool for peanuts.

Locomotive, the first version of which was coded in 2016, blew away Pentagon brass. One government official demanded midway through the demo that the rest of it be conducted inside a SCIF, a secure government facility where classified information could be discussed. The official didn’t understand how or what PlanetRisk was doing but assumed it must be a secret. A PlanetRisk employee at the briefing was mystified. “We were like, well, this is just stuff we’ve seen commercially,” they recall. “We just licensed the data.” After all, how could marketing data be classified?

Government officials were so enthralled by the capability that PlanetRisk was asked to keep Locomotive quiet. It wouldn’t be classified, but the company would be asked to tightly control word of the capability to give the military time to take advantage of public ignorance of this kind of data and turn it into an operational surveillance program.

And the same executive remembered leaving another meeting with a different government official. They were on the elevator together when one official asked, could you figure out who is cheating on their spouse?

Yeah, I guess you could, the PlanetRisk executive answered.

But Mike Yeagley wouldn’t last at PlanetRisk.

As the company looked to turn Locomotive from a demo into a live product, Yeagley started to believe that his employer was taking the wrong approach. It was looking to build a data visualization platform for the government. Yet again, Yeagley thought it would be better to provide the raw data to the government and let them visualize it in any way they choose. Rather than make money off of the number of users inside government that buy a software license, Mike Yeagley wanted to just sell the government the data for a flat fee.

So Yeagley and PlanetRisk parted ways. He took his business relationship with UberMedia with him. PlanetRisk moved on to other lines of work and was eventually sold off in pieces to other defense contractors. Yeagley would land at a company called Aelius Exploitation Technologies, where he would go about trying to turn Locomotive into an actual government program for the Joint Special Operations Command—the terrorist-hunting elite special operations force that killed Osama bin Laden and Ayman Al Zarqawi and spent the past few years dismantling ISIS.

Locomotive was renamed VISR, which stood for Virtual Intelligence, Surveillance, and Reconnaissance. It would be used as part of an interagency program and would be shared widely inside the US intelligence community as a tool to generate leads.

By the time Yeagley went out to warn various security agencies about Grindr in 2019, VISR had been used domestically, too—at least for a short period of time when the FBI wanted to test its usefulness in domestic criminal cases. (In 2018, the FBI backed out of the program.) The Defense Intelligence Agency, another agency that had access to the VISR data, has also acknowledged that it used the tool on five separate occasions to look inside the United States as part of intelligence-related investigations.

But VISR, by now, is only one product among others that sell adtech data to intelligence agencies. The Department of Homeland Security has been a particularly enthusiastic adopter of this kind of data. Three of its components—US Customs and Border Protection, US Immigration and Customs Enforcement, and the US Secret Service —have bought more than 200 licenses from commercial ad tech vendors since 2019. They would use this data for finding border tunnels, tracking down unauthorized immigrants, and trying to solve domestic crimes. In 2023, a government inspector general chastised DHS over the use of adtech, saying that the department did not have adequate privacy safeguards in place and recommending that the data stop being used until policies were drawn. The DHS told the inspector general that they would continue to use the data. Adtech “is an important mission contributor to the ICE investigative process as, in combination with other information and investigative methods, it can fill knowledge gaps and produce investigative leads that might otherwise remain hidden,” the agency wrote in response.

Other governments’ intelligence agencies have access to this data as well. Several Israeli companies—Insanet, Patternz, and Rayzone—have built similar tools to VISR and sell it to national security and public safety entities around the world, according to reports. Rayzone has even developed the capability to deliver malware through targeted ads, according to Haaretz.

Which is to say, none of this is an abstract concern—even if you’re just a private citizen. I’m here to tell you if you’ve ever been on a dating app that wanted your location or if you ever granted a weather app permission to know where you are 24/7, there is a good chance a detailed log of your precise movement patterns has been vacuumed up and saved in some data bank somewhere that tens of thousands of total strangers have access to. That includes intelligence agencies. It includes foreign governments. It includes private investigators. It even includes nosy journalists. (In 2021, a small conservative Catholic blog named The Pillar reported that Jeffrey Burrill, the secretary general of the US Conference of Catholic Bishops, was a regular user of Grindr. The publication reported that Burrill “visited gay bars and private residences while using a location-based hookup app” and described its source as “commercially available records of app signal data obtained by The Pillar.”)

If you cheated on your spouse in the past few years and you were careless about your location data settings, there is a good chance there is evidence of that in data that is available for purchase. If you checked yourself into an inpatient drug rehab, that data is probably sitting in a data bank somewhere. If you told your boss you took a sick day and interviewed at a rival company, that could be in there. If you threw a brick through a storefront window during the George Floyd protests, well, your cell phone might link you to that bit of vandalism. And if you once had a few pints before causing a car crash and drove off without calling the police, data telling that story likely still exists somewhere.

We all have a vague sense that our cell phone carriers have this data about us. But law enforcement generally needs to go get a court order to get that. And it takes evidence of a crime to get such an order. This is a different kind of privacy nightmare.

I once met a disgruntled former employee of a company that competed against UberMedia and PlaceIQ. He had absconded with several gigabytes of data from his former company. It was only a small sampling of data, but it represented the comprehensive movements of tens of thousands of people for a few weeks. Lots of those people could be traced back to a residential address with a great deal of confidence. He offered me the data so I could see how invasive and powerful it was.

What can I do with this—hypothetically? I asked. In theory, could you help me draw geofences around mental hospitals? Abortion clinics? Could you look at phones that checked into a motel midday and stayed for less than two hours?

Easily, he answered.

I never went down that road.

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monpetitrobot · 18 days ago

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sranalytics50 · 18 days ago

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