Kili Technology

This article synthesizes insights from Kili Technology's recent geospatial AI roundtable discussion featuring industry experts from government, consulting, and technology sectors. Our panelists shared real-world implementation experiences, revealing critical gaps between AI promises and production realities while providing actionable guidance for organizations evaluating their geospatial AI strategies.

This article examines the modern pipeline for satellite imagery processing, exploring how the integration of AI-assisted annotation tools like SAM2, foundation models like DINOv2, and intelligent data processing platforms are changing earth observation workflows across industries.

As more industries rely on geospatial data and advance geospatial AI, new and persistent challenges present themselves. These become blockers towards production-ready AI and wastes time, money, and effort. Here's what to consider when building geospatial datasets for AI training.

Check out our latest updates for our geospatial projects interface.

Learn how to build reliable geospatial training data for AI from Enabled Intelligence's expertise. Discover geospatial annotation best practices, quality assurance workflows, and technical solutions for scaling satellite imagery labeling projects while maintaining 95%+ accuracy across defense, climate research, and commercial applications.

We're proud to support the upcoming AI and Geospatial Dialogue series by Geoawesome - a geospatial community initiative deep diving into the advancements of AI in the geospatial space.

Discover how geospatial foundation models are revolutionizing Earth observation. Learn about key models, applications, challenges, and implementation strategies.

Geospatial data annotation requires far more than drawing bounding boxes on satellite imagery. As machine learning engineers develop advanced geospatial intelligence, annotation quality has become the critical differentiator for production model performance. This article explores advanced techniques that transform geospatial data labeling into a sophisticated discipline, covering multi-modal fusion, automation workflows, and validation methods for reliable AI systems.

We are excited to introduce our new Geospatial Projects. Designed specifically with geospatial standards and workflows in mind, this new project type unlocks richer capabilities and a more tailored experience, allowing users to fully harness the potential of geographic data.

Unlock the full potential of satellite imagery with advanced approaches to Earth Observation data labeling.

Discover how collaborative approaches to geospatial data can overcome the 47% AI project failure rate in agriculture. Learn from real-world case studies and expert insights from our Geospatial World Forum 2025 presentation on transforming satellite imagery into actionable farming intelligence.

Despite impressive advances in natural language processing, systematic analyses of LLM reasoning abilities remain limited. Our comprehensive benchmark study implements a multi-dimensional evaluation framework across eight prominent models—OpenAI's O1, OpenAI's O3 Mini, Claude 3.5, Claude 3.7, Mistral, Qwen2.5, DeepseekR1, and QwQ-32B—to provide unprecedented insights into their reasoning strengths and limitations.

The quality of annotated geospatial data directly impacts the success of machine learning models, computer vision applications, and automated feature extraction. For geospatial data scientists and GIS professionals working with satellite imagery, aerial photographs, and other remote sensing data, selecting the right annotation tool is crucial for achieving accurate results and efficient workflows.

Learn about challenges, deployment options, and best practices for data security and privacy. This article delves into the complexities of safeguarding unstructured data during labeling and provides a comprehensive guide for data scientists, ML engineers, and AI project directors.

This article recaps what we learned in our most recent webinar, where we delve into the intricacies of fine-tuning Large Language Models (LLMs) and their importance in artificial intelligence and machine learning.

This article uncovers the hurdles that businesses often face when it comes to annotating data and explores the avenues available to overcome them. Let's dive in!

Machine learning models are only as good as the data used to train them. The only thing your company needs to drive AI success is a large amount of high-quality, well-labeled training data. To help you achieve excellent-quality training datasets, we'll share the knowledge we've gained on the subtle art of choosing an adequate data-labeling platform.

Geospatial data analysis requires accurate, high-quality annotations to extract meaningful insights. Kili Technology’s geospatial annotation tool enhances efficiency with AI-powered labeling, seamless GIS integration, and scalable workflows for high-resolution imagery.

Explore the groundbreaking advancements of DeepSeek R1, a reinforcement learning-driven reasoning model. Learn about its distillation into smaller models, ensuring cost-effective, efficient reasoning capabilities across various domains.

Explore LLM reasoning limitations in logical deduction and ongoing efforts to enhance these capabilities through chain-of-thought reasoning, coding, and other novel techniques.

Many companies around the world are currently trying to make the creation & deployment of AI as easy and efficient as possible. Among them, two contestants stand out: Kili Technology and LatentAI. Let's dive into how to use them to your advantage!