Location Intelligence (LI), the science of transforming location-centric geospatial data into actionable knowledge, has become a cornerstone of modern spatial decision-making. The rapid evolution of Geospatial Representation Learning is fundamentally reshaping LI development through two successive technological revolutions: the deep learning breakthrough and the emerging large language model (LLM) paradigm. While deep neural networks (DNNs) have demonstrated remarkable success in automated feature extraction from structured geospatial data (e.g., satellite imagery, GPS trajectories), the recent integration of LLMs introduces transformative capabilities for cross-modal geospatial reasoning and unstructured geo-textual data processing. This survey presents a comprehensive review of geospatial representation learning across both technological eras, organizing them into a structured taxonomy based on the complete pipeline comprising: (1) data perspective, (2) methodological perspective and (3) application perspective. We also highlight current advancements, discuss existing limitations, and propose potential future research directions in the LLM era. This work offers a thorough exploration of the field and providing a roadmap for further innovation in LI. The summary of the up-to-date paper list can be found in https://github.com/CityMind-Lab/Awesome-Location-Intelligence and will undergo continuous updates.

In the early years of the 21st century, there were significant concerns about robots replacing humans in the workforce. Although the protest remains unsettled, it was more terrifying then than now, mostly because the concept of robotics wasn't very clear. The International Organization of Standardization didn't help when it defined a robot as an "automatically controlled, reprogrammable manipulator." By that definition, an ATM is a robot, and bank tellers were in serious trouble. At least, they thought they were.

Geospark Analytics received the Federal Small Business Award for Collaboration at the 2022 Esri Partner Conference (EPC) held in Palm Springs, California, March 5–7, 2022. Geospark Analytics was one of six Esri partners recognized at the conference for excellence in their work with Esri in the US Federal Government market. Geospark Analytics works in tandem with Esri to help clients customize their ArcGIS Online dashboards with the Hyperion AI, machine-learning platform. They empower security and intelligence teams with a precise and detailed visualization of risk and threat assessments so teams can make efficient and effective decisions for their organization. Esri is the global leader in location intelligence with a network of over 2,700 partners around the world.

Organizations such as Facebook, Netflix, Amazon, and Google are leading the deployment of ML for analyzing and understanding user activities and preferences to recommend and sell products. Availability of multilingual support, API integrations, and associated services are expected to create opportunities for NLP. With increasing amount of text data being generated and need to make sense of it across various industry verticals, NLP finds a growing implementation across end users of AI. Computer vision is playing a significant role in semi-autonomous and autonomous cars in interpreting hand gestures/signals. It is also used for monitoring crop health and nutrition deficiency in agriculture.

We often see artificial intelligence depicted as an omnipotent machine, but it is a technology that learns as it goes, too. Key among the lessons AI must be taught is location intelligence: where are assets, events, and people across space and time, and how do they relate to each other? Just think of all the pandemic-era technology to flourish relying on location--the delivery apps, the COVID testing and vaccine dashboards, the safe to-go store orders. Location intelligence, the output of a geographic information system (GIS), offers critical context that forms the foundation of what machines can be trained to do. Here are four ways to ramp up AI with location intelligence.

AI and Content Marketing series, we'll explore the future of artificial intelligence and content marketing. The future of AI and content seems to shine with the eyeball-grabbing glow of personalization. What do we mean by personalization, exactly? Imagine there's a car accident Monday morning in your neighborhood. That's the level of personalization that people are excited about for the future of artificial intelligence and content.

In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.

The landscape of information technology across the business world is changing rapidly. This is majorly accredited to organizations' interests and moves toward digital technologies, particularly Artificial Intelligence (AI). It has the potential to improve business standards, help enhance decision making and streamline overall business operations. While the adoption of AI technologies has become a new business phenomenon, the relentless development of AI applications is creating a wide range of opportunities. AI along with machine learning platforms can be implemented to assess and process extremely large data sets in a quick and accurate manner.

While executives, already working through digital transformation, grapple with pandemic-related recovery issues, they're leaning on technologies--both proven and innovative--to stay on track. Most leaders see artificial intelligence as crucial and describe a "sense of urgency at the top" to implement it. Yet, they struggle to integrate company-wide AI initiatives. Seventy-five percent of executives surveyed believe that if they fail to do so, their companies will be gone in five years. AI in the business world will likely grow at a steady pace over the next five years, then shoot skyward.

Even as population growth slows, projections show that the world will be home to nearly 10 billion people by 2050, 2 billion more than it hosts today. The job of feeding them will fall to an industry that's emerging on the forefront of innovation. Article Snapshot: Long at the mercy of forces beyond their control, farmers are creating tech-reliant connected farms that track everything within inches and help them centrally manage every season in the fields. Farmers are getting younger and more tech savvy, and they're transforming the agriculture industry through location intelligence and tools such as AI, autonomous vehicles, and IoT-connected cattle. In this installment of the WhereNext Think Tank series, Esri's director of Professional Services, Brian Cross, interviews Esri's agriculture practice lead, Matt Harman.